TL1A-targeted method, system and kit for treating inflammatory diseases

Abstract

Methods, systems, and kits are provided for selecting a subject treated with an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression based on the presence of one or more genotypes associated with a positive therapeutic response to a TL1A inhibitor. Methods, systems, and kits for detecting one or more genotypes described herein are also provided.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 384,925 filed on November 23, 2022, U.S. Provisional Application No. 63 / 385,622 filed on November 30, 2022, U.S. Provisional Application No. 63 / 478,865 filed on January 6, 2023, and U.S. Provisional Application No. 63 / 487,853 filed on March 1, 2023.

[0003] References to sequence listings submitted electronically

[0004] This application contains a sequence listing, which has been submitted electronically in XML format and is incorporated herein by reference in its entirety. The XML file was created on November 13, 2023, is named 25747-WO-PCT_SL.xml, and is 1,647,264 bytes in size. 1. Background Technology

[0005] Inflammatory, fibrostenotic, and fibrotic diseases represent a significant global health burden, affecting a large number of individuals, with diverse pathogenic mechanisms and clinical manifestations. One such disease is inflammatory bowel disease (IBD), which has two common forms: Crohn's disease (CD) and ulcerative colitis (UC). IBD is a chronic, relapsing, inflammatory condition of the gastrointestinal tract. The incidence of IBD is high, affecting nearly three million people in the United States alone.

[0006] There are few treatment options available for patients suffering from inflammatory diseases, fibrostenotic diseases and fibrotic diseases. Existing anti-inflammatory therapies, such as steroids and tumor necrosis factor (TNF) inhibitors, are commonly used as first-line treatments for the treatment of IBD. Unfortunately, a significant number of patients lack or lose response to existing anti-inflammatory therapies, especially to TNF inhibitors. When patients receive ineffective anti-inflammatory therapy, their condition worsens. For patients who do not respond to first-line therapy, surgery in the form of anatomyplasty (remodeling the intestine) or resection (removal of the intestine) is the only treatment option. Surgical treatment of IBD is invasive and causes postoperative risks, such as anastomotic leakage, infection and bleeding, to an estimated one-third of patients undergoing surgery.

[0007] The pathogenesis of inflammatory, fibrostenotic, and fibrotic diseases, such as IBD, is thought to involve an uncontrolled immune response that may be triggered by certain environmental factors in genetically susceptible individuals. The heterogeneity in disease pathogenesis and clinical course, coupled with the varying responses to treatment and associated side effects, suggests that a personalized medicine approach to treating these diseases is the optimal therapeutic strategy. However, few personalized therapies are available to patients. Therefore, there is a need to identify targeted therapies for treating inflammatory, fibrostenotic, and fibrotic diseases and their subclinical phenotypes, and even more so, to develop reliable methods to identify patients likely to respond to any given treatment based on their genotype. Desirable methods would also identify undiagnosed subjects at risk for developing disease so that preventive interventions can be implemented to alleviate the growing health burden. 2. Summary of the Invention

[0008] The models and genotypes described herein are associated (alone or together) with: (i) an increase in the level of TNFSF15 (TL1A) protein expression in a sample obtained from a subject or patient as compared to a reference level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual), (ii) an increase in IBD-enriched cell types in tissues affected by IBD as compared to a reference level in tissues not affected by IBD, (iii) a decrease in IBD-depleted cell types in tissues affected by IBD as compared to a reference level in tissues not affected by IBD, and / or (iv) an increase in a positive therapeutic response in IBD patients to treatment with a TL1A inhibitor as compared to a reference response level in patients not selected by genotype or model. More specifically, the models and genotypes described herein are associated with the following (alone or together): (i) alone; (ii) alone; (iii) alone; (iv) alone; (i) and (ii) together; (i) and (iii) together; (i) and (iv) together; (ii) and (iii) together; (ii) and (iv) together; (iii) and (iv) together; (i), (ii) and (iii) together; (i), (ii) and (iv) together; (i), (iii) and (iv) together; (ii), (iii) and (iv) together; or (i), (ii), (iii) and (iv) together, where (i), (ii), (iii) and (iv) correspond to the items numbered (i), (ii), (iii) and (iv) in the previous sentence of this paragraph. Accordingly, and as an example, the models and genotypes described herein are associated (alone or together) with an increase in the level of TNFSF15 (TL1A) protein expression in a sample obtained from a subject or patient compared to a reference level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual). Additionally, the models and genotypes described herein are associated (alone or together) with an increase in IBD-enriched cell types in tissues affected by IBD compared to a reference level in tissues not affected by IBD. Alternatively, the models and genotypes described herein are associated (alone or together) with a decrease in IBD-depleted cell types in tissues affected by IBD compared to a reference level in tissues not affected by IBD. Additionally, the models and genotypes described herein are associated (alone or together) with an increase in the positive therapeutic response of IBD patients to treatment with an inhibitor of TL1A activity or expression compared to a reference response level in patients not selected by genotype or model. The models provided herein, such as those for calculating a predictive response index (PRI), use the genotypes described herein and apply mathematical functions to the genotypes to generate the PRI, as described in Sections 5.2 and 7. The genotypes disclosed herein are located at genes or genetic loci that are directly or indirectly involved in TL1A-mediated or T cell-dependent inflammatory pathways.In addition, some genotypes provided herein are also significantly associated with inflammatory bowel disease (IBD), such as Crohn's disease (CD). These genotypes can be used to select patients or subjects for treatment with inhibitors of TL1A activity or expression. Patients may be diagnosed with IBD, CD, or both. Subjects may be suspected of having IBD, CD, or both.

[0009] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 29%.

[0010] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0011] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0012] (i) obtaining or having obtained a sample from the subject;

[0013] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0014] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0015] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0016] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0017] (a) obtaining or having obtained a sample from the subject;

[0018] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0019] (c) Calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

[0020] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0021] (a) obtaining or having obtained a sample from the subject;

[0022] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0023] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0024] (d) If the PRI is above the cutoff value, the subject is selected for treatment with an inhibitor of TL1A activity or expression.

[0025] In some embodiments, the method further comprises preparing DNA from the sample.

[0026] In one aspect, provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, the method comprising:

[0027] (a) receiving genotypic data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, wherein the genotypic data comprises a combination of polymorphisms;

[0028] (b) analyzing the genotype data with a first statistical algorithm configured to generate a model risk score (MRS) for the subject by performing operations comprising:

[0029] (i) assigning a weighted value to each polymorphism in the polymorphism combination to generate a plurality of weighted values; and

[0030] (ii) summing the plurality of weighted values;

[0031] (c) providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); and

[0032] (d) applying a cutoff value to the RPS, wherein the RPS relative to the cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression to treat an inflammatory, fibrotic, or fibrostenotic disease or condition.

[0033] In another aspect, provided herein is a computer-implemented method of determining a response probability score (RPS) for a subject, the method comprising:

[0034] (a) obtaining a plurality of multi-single nucleotide polymorphism (multi-SNP) models, wherein each multi-SNP model is predictive for a positive therapeutic response to a TL1A activity or expression inhibitor for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject;

[0035] (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from a subject;

[0036] (c) calculating a model risk score (MRS) using one or more statistical algorithms configured to perform operations comprising: (i) assigning a weighted numerical value to each of the plurality of polymorphisms to produce a plurality of weighted values; and (ii) summing the plurality of weighted values; and

[0037] (d) Logarithmic scaling and cutoff values ​​were applied to the MRS to generate a response probability score (RPS).

[0038] In some embodiments, the PRI is a response probability score (RPS). In some embodiments, the PRI has a positive correlation coefficient with the RPS. In some embodiments, the correlation coefficient between the PRI and the RPS is a Pearson correlation coefficient or a Spearman correlation coefficient. In some embodiments, the positive correlation coefficient between the PRI and the RPS is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1. In some embodiments, the RPS is in the range of 0 to 1. In some embodiments, the cutoff value is 0.5. In some embodiments, the RPS is calculated as 1 / (1+e (-MRS) ), where MRS is calculated as And where χ i is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, β i is the weight of the i-th SNP in the model.

[0039] In some embodiments, the PRI is a model risk score (MRS). In some embodiments, the PRI has a positive correlation coefficient with the MRS. In some embodiments, the correlation coefficient between the PRI and the MRS is a Pearson correlation coefficient or a Spearman correlation coefficient. In some embodiments, the positive correlation coefficient between the PRI and the MRS is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1. In some embodiments, the MRS is calculated as And where χ i is the mathematical representation of the i-th SNP in the model.

[0040] In some embodiments, the SNPs in the model are represented by χ i Mathematically, it is:

[0041] (i) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele;

[0042] (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele;

[0043] (iii) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele;

[0044] (iv) 0 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele;

[0045] (v) 1 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; and / or

[0046] (vi) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele.

[0047] In some embodiments, the polymorphism combination comprises one or more polymorphisms selected from Table 27, or as described above with an R of at least 0.85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

[0048] In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positivity of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a positivity of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%. In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.In some embodiments, the cutoff value is such that a PRI above the cutoff value is predictive for reduction in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.

[0049] In some embodiments, the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from activated fibroblasts, monocyte-derived dendritic cells (moDCs), CD36+ endothelial cells, intestinal epithelial cells and colon epithelial cells (clonocytes), EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

[0050] In some embodiments, the one or more IBD-depleted cell types comprise 1 or 2 cell types selected from tuft cells and BEST4+ epithelial cells.

[0051] In some embodiments, the combination of polymorphisms includes at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen polymorphisms.

[0052] In some embodiments, the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

[0053] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a comparison of a predicted response index (PRI) according to (1) or (2) to a cutoff value:

[0054] (1) If the PRI is positively correlated with the response probability score (RPS), select subjects when the PRI is above the cutoff value; or

[0055] (2) if PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value;

[0056] and wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and comparison of the Predicted Response Index (PRI) according to (1) or (2) to a cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 29%.

[0057] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0058] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0059] (i) obtaining or having obtained a sample from the subject;

[0060] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0061] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0062] (1) if the PRI is positively correlated with the RPS, then the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0063] (2) if the PRI is negatively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0064] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0065] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0066] (a) obtaining or having obtained a sample from the subject;

[0067] (b) performing an assay on the sample adapted to detect a combination of polymorphisms; and

[0068] (c) calculating a PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2):

[0069] (1) if the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression when the PRI is above the cutoff value; or

[0070] (2) If the PRI is negatively correlated with the RPS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0071] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0072] (a) obtaining or having obtained a sample from the subject;

[0073] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0074] (c) calculating the predicted response index (PRI) from the polymorphism combination; and

[0075] (d) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on comparison of the PRI according to (1) or (2) to a cutoff value:

[0076] (1) If PRI is positively correlated with RPS, select subjects when PRI is above the cutoff value; or

[0077] (2) If PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value.

[0078] In some embodiments, the method further comprises preparing DNA from the sample.

[0079] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression based at least in part on a predicted response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, and determining a comparison of the PRI to a cutoff value to predict a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression.

[0080] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering an inhibitor of TL1A activity or expression to a subject predicted to exhibit a positive treatment response to an inhibitor of TL1A activity or expression, as determined by a predicted response index (PRI) calculated as follows:

[0081] (a) detecting the presence of a polymorphism combination in a sample from a subject;

[0082] (b) applying a statistical algorithm to the combination of polymorphisms detected in step (a) to generate a PRI; and

[0083] (c) Determine the PRI compared with the cutoff value.

[0084] In yet another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0085] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0086] (i) obtaining or having obtained a sample from the subject;

[0087] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0088] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the PRI is further determined in comparison with a cutoff value; and

[0089] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0090] In some embodiments, the method further comprises preparing DNA from the sample.

[0091] In some embodiments, the comparison of the PRI to the cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the RPS, the subject's PRI is determined when the PRI is above the cutoff value; or (2) if the PRI is negatively correlated with the RPS, the subject's PRI is determined when the PRI is below the cutoff value.

[0092] In some embodiments, the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

[0093] In some embodiments, if the PRI is positively correlated with the RPS, the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1, or if the PRI is negatively correlated with the RPS, the negative correlation coefficient is at most about -0.6, at most about -0.65, at most about -0.7, at most about -0.75, at most about -0.8, at most about -0.85, at most about -0.95, at most about -0.99, or -1.

[0094] In some embodiments, the RPS is in the range of 0 to 1.

[0095] In some embodiments, the cutoff value is 0.5 if the PRI is positively correlated with the RPS; or -0.5 if the PRI is negatively correlated with the RPS.

[0096] In some embodiments, RPS is calculated as 1 / (1+e(-MRS)), where MRS is calculated as And where χi is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and βi is the weight of the i-th SNP in the model.

[0097] In some embodiments, the SNPs in the model are represented mathematically by χi, which is:

[0098] (i) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele;

[0099] (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele;

[0100] (iii) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele;

[0101] (iv) 0 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele;

[0102] (v) 1 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; and / or

[0103] (vi) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele.

[0104] In some embodiments, the polymorphism combination comprises one or more polymorphisms selected from Table 27, or as described above with an R of at least 0.85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

[0105] In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%.In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%.In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a decrease in one or more IBD-depleted cell types with a positivity of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, or 75%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. In some embodiments, the (1) and (2) referred to in this paragraph are clauses (1) and (2) described in this section (Section 2), e.g., in the applicable preceding paragraphs.

[0106] In some embodiments, the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from activated fibroblasts, monocyte-derived dendritic cells (moDCs), CD36+ endothelial cells, intestinal epithelial cells and colonic epithelial cells, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

[0107] In some embodiments, the one or more IBD-depleted cell types comprise 1 or 2 cell types selected from tuft cells and BEST4+ epithelial cells.

[0108] In some embodiments, the combination of polymorphisms includes at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen polymorphisms.

[0109] In some embodiments, the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

[0110] In some embodiments, (i) PRI is calculated using a combination of polymorphisms selected from the combinations listed in Column 2 of Table 31 and the corresponding beta coefficients listed in Column 1 of Table 31; (ii) MRS is calculated using a combination of polymorphisms selected from the combinations listed in Column 2 of Table 31 and the corresponding beta coefficients listed in Column 1 of Table 31; and / or (iii) RPS is calculated using a combination of polymorphisms selected from the combinations listed in Column 2 of Table 31 and the corresponding beta coefficients listed in Column 1 of Table 31.

[0111] In some embodiments, the combination of polymorphisms is detected in a sample by performing an assay on the sample configured to detect the presence of at least three nucleotides corresponding to nucleic acid positions 501 within at least three of SEQ ID NOs: 2001-2048 and 2057-2059.

[0112] In one aspect, provided herein is a computer-implemented system comprising at least one processor and instructions executable by the at least one processor to provide an application configured to determine a response probability score (RPS) of a subject by performing operations comprising:

[0113] (a) receiving genotypic data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, wherein the genotypic data comprises a combination of polymorphisms;

[0114] (b) applying a first statistical algorithm to the genotype data, the first statistical algorithm configured to generate a model risk score (MRS) for the subject by performing operations comprising:

[0115] (i) assigning a weighted value to each polymorphism in the polymorphism combination to generate a plurality of weighted values; and

[0116] (ii) summing the plurality of weighted values;

[0117] (c) applying a second statistical algorithm to the MRS, the second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); and

[0118] (d) applying a cutoff value to the RPS, wherein the RPS relative to the cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression to treat an inflammatory, fibrotic, or fibrostenotic disease or condition.

[0119] In another aspect, provided herein is a computer-implemented system comprising at least one processor and instructions executable by the at least one processor to provide an application configured to determine a response probability score (RPS) for a subject by performing operations comprising:

[0120] (a) receiving a plurality of multi-single nucleotide polymorphism (multi-SNP) models, wherein each multi-SNP model is predictive for a positive therapeutic response to a TL1A activity or expression inhibitor for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject;

[0121] (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from a subject;

[0122] (c) calculating a model risk score (MRS) using one or more statistical algorithms configured to perform operations comprising: (i) assigning a weighted numerical value to each of the plurality of polymorphisms to produce a plurality of weighted values; and (ii) summing the plurality of weighted values; and

[0123] (d) Logarithmic scaling and cutoff values ​​were applied to the MRS to generate a response probability score (RPS).

[0124] In some embodiments of the computer-implemented system, the RPS ranges from 0 to 1.

[0125] In some embodiments of the computer-implemented system, the cutoff value is 0.5.

[0126] In some embodiments of the computer-implemented system, the genotype data is a combination of single nucleotide polymorphisms (SNPs).

[0127] In some embodiments of the computer-implemented system, RPS is calculated as 1 / (1+e (-MRS) ), where MRS is calculated as And where χ i is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, β i is the weight of the i-th SNP in the model.

[0128] In some embodiments of the computer-implemented system, MRS is calculated as And where χ i is the mathematical representation of the i-th SNP in the model.

[0129] In some embodiments of the computer-implemented system, the SNPs in the model are represented by χ i Mathematically, it is:

[0130] (i) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele;

[0131] (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele;

[0132] (iii) 0, for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele;

[0133] (iv) 0 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele;

[0134] (v) 1 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; and / or

[0135] (vi) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele.

[0136] In some embodiments of the computer-implemented system, the polymorphism combination comprises one or more polymorphisms selected from Table 27, or as described above with an R of at least 0.85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

[0137] In some embodiments of the computer-implemented system, the combination of polymorphisms includes at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen polymorphisms.

[0138] In some embodiments of the computer-implemented system, the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

[0139] In some embodiments of the computer-implemented system, (i) MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; and / or (ii) RPS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31.

[0140] In some embodiments of the computer-implemented system, the combination of polymorphisms is detected in the sample by performing an assay on the sample configured to detect the presence of at least three nucleotides corresponding to nucleic acid positions 501 within at least three of SEQ ID NOs: 2001-2048 and 2057-2059.

[0141] In some embodiments of the methods or computer-implemented systems provided herein, the subject has received a late-stage IBD therapy prior to treatment with a TL1A activity or expression inhibitor. In some embodiments of the methods or computer-implemented systems provided herein, the subject has not received a late-stage IBD therapy prior to treatment with a TL1A activity or expression inhibitor. In some embodiments of the methods or computer-implemented systems provided herein (including in this paragraph), late-stage IBD therapy comprises one or more selected from a biotherapeutic agent for IBD, an S1P1 modulator, or a JAK inhibitor. In some embodiments of the methods or computer-implemented systems provided herein (including in this paragraph), the biotherapeutic agent for IBD comprises an anti-TNFα antibody, an anti-IL23 antibody, or an anti-integrin antibody. In some embodiments of the methods or computer-implemented systems provided herein (including in this paragraph), the anti-TNFα antibody comprises adalimumab, infliximab, golimumab, certolizumab, or etanercept. In some embodiments of the methods or computer-implemented systems provided herein (including in this paragraph), the anti-IL23 antibody comprises ustekinumab, guselkumab, risankizumab, brazikumab, mirikizumab, tildrakizumab, or briakinumab. In some embodiments of the methods or computer-implemented systems provided herein (including in this paragraph), the anti-integrin antibody comprises etrolizumab, vedolizumab, natalizumab, or ontamalimab. In some embodiments of the methods or computer-implemented systems provided herein, including in this paragraph, the S1P1 modulator comprises fingolimod, siponimod, etrasimod, ozanimod, ponesimod, amiselimod, ceralifimod, or mocravimod.In some embodiments of the methods or computer-implemented systems provided herein, including in this paragraph, the JAK inhibitor comprises tofacitinib, abrocitinib, baricitinib, upadacitinib, or filgotinib.

[0142] In some embodiments, the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A (anti-TL1A antibody or antigen-binding fragment), wherein the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable region comprising: (a) HCDR1 comprising the amino acid sequence of SEQ ID NO: 1; (b) HCDR2 comprising the amino acid sequence of any one of SEQ ID NOs: 2-5; and (c) HCDR3 comprising the amino acid sequence of any one of SEQ ID NOs: 6-9; and a light chain variable region comprising: (d) LCDR1 comprising the amino acid sequence of SEQ ID NO: 10; (e) LCDR2 comprising the amino acid sequence of SEQ ID NO: 11; and (f) LCDR3 comprising the amino acid sequence of any one of SEQ ID NOs: 12-15.

[0143] In some embodiments, the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, wherein the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable domain comprising an amino acid sequence that is at least about 90% identical to any one of SEQ ID NOs: 101-135 or 310-302, and a light chain variable domain comprising an amino acid sequence that is at least about 90% identical to any one of SEQ ID NOs: 201-206 or 303. In some embodiments, the heavy chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% identical to any one of SEQ ID NOs: 101-135 or 310-302. In some embodiments, the light chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 201-206 or 303.

[0144] In some embodiments, the TL1A activity or expression inhibitor is an anti-TL1A antibody or antigen-binding fragment, wherein the anti-TL1A antibody or antigen-binding fragment comprises: (a) a heavy chain variable framework region comprising a human IGHV1-46*02 framework or a modified human IGHV1-46*02 framework; and (b) a light chain variable framework region comprising a human IGKV3-20 framework or a modified human IGKV3-20 framework; wherein the heavy chain variable framework region and the light chain variable framework region together comprise fewer than about 14 amino acid modifications compared to the human IGHV1-46*02 framework and the human IGKV3-20 framework. In some embodiments, the amino acid modifications in the less than 14 amino acid modifications include: (a) an amino acid modification at position 47 in the heavy chain variable region, and the amino acid at position 47 is R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (b) an amino acid modification at position 45 in the heavy chain variable region, and the amino acid at position 45 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V. K, M, F, P, S, T, W, Y or V; (c) an amino acid modification at position 55 in the heavy chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y or V; (d) an amino acid modification at position 78 in the heavy chain variable region, and the amino acid at position 78 is A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W or Y; (e) amino acid modification at position 80 in the heavy chain variable region, and the amino acid at position 80 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y or V; (f) amino acid modification at position 82 in the heavy chain variable region, and the amino acid at position 82 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (g) amino acid modification at position 83 in the heavy chain variable region, and the amino acid at position 83 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (a) wherein the amino acid modification is at position 89 in the heavy chain variable region, and the amino acid at position 89 is A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, or Y; or (h) the amino acid modification is at position 91 in the heavy chain variable region, and the amino acid at position 91 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y, or V; or a combination of two or more modifications selected from (a) to (h). In some embodiments, the amino acid modification in the less than 14 amino acid modifications includes: A47R, R45K, M55I, V78A, M80I, R82T, V89A, M91L in the heavy chain variable region, according to Aho or Kabat numbering.In some embodiments, the amino acid modification in the fewer than 14 amino acid modifications comprises: (a) a modification at amino acid position 54 in the light chain variable region; and / or (b) a modification at amino acid position 55 in the light chain variable region; numbered according to Aho or Kabat. In some embodiments, the amino acid modification in the fewer than 14 amino acid modifications comprises: (a) an amino acid modification at position 54 in the light chain variable region, and the amino acid at position 54 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V; and / or (b) an amino acid modification at position 55 in the light chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y, or V. In some embodiments, the amino acid modification in the fewer than 14 amino acid modifications comprises L54P and / or L55W in the light chain variable region, numbered according to Aho or Kabat.

[0145] In some embodiments, the TL1A activity or expression inhibitor is an antibody or antigen-binding fragment thereof that binds to TL1A and comprises: a heavy chain variable region comprising SEQ ID NO: 301

[0146] X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYCAR[HCDR3]WGQGTTVTVSS; and a light chain variable region comprising SEQ ID NO: 303

[0147] EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FGGGTKLEIK, wherein each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V. In some embodiments, the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A and comprises: a heavy chain variable region comprising SEQ ID NO: 302X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLEWX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYC[HCDR3]WGQGTTVTVSS; and a light chain variable region comprising SEQ ID NO: 303EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FG GGTKLEIK, wherein each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V. In some embodiments, (a) X1 is Q or E; (b) X2 is R or K; (c) X3 is A or R; (d) X4 is M or I; (e) X5 is V or A; (f) X6 is M or I; (g) X7 is R or T; (h) X8 is V or A; (i) X9 is M or L; (j) X10 is L or P; (k) X11 is L or W; or (l) X1-X11 are any combination of (a) to (k).

[0148] In some embodiments, the antibody or antigen-binding fragment comprises: a heavy chain CDR1 as set forth in SEQ ID NO: 1; a heavy chain CDR2 as set forth in any one of SEQ ID NOs: 2-5; a heavy chain CDR3 as set forth in any one of SEQ ID NOs: 6-9; a light chain CDR1 as set forth in SEQ ID NO: 10; a light chain CDR2 as set forth in SEQ ID NO: 11; and a light chain CDR3 as set forth in any one of SEQ ID NOs: 12-15.

[0149] In some embodiments, the antibody or antigen-binding fragment comprises: a heavy chain framework (FR) 1 as shown in SEQ ID NO: 304; a heavy chain FR2 as shown in SEQ ID NO: 305 or SEQ ID NO: 313; a heavy chain FR3 as shown in any one of SEQ ID NOs: 306, 307, 314 or 315; a heavy chain FR4 as shown in SEQ ID NO: 308; a light chain FR1 as shown in SEQ ID NO: 309; a light chain FR2 as shown in SEQ ID NO: 310; a light chain FR3 as shown in SEQ ID NO: 311; or a light chain FR4 as shown in SEQ ID NO: 312; or a combination thereof.

[0150] In some embodiments, the antibody or antigen-binding fragment comprises human IgG1 an Fc region comprising (a) 297A, 297Q, 297G or 297D, (b) 279F, 279K or 279L, (c) 228P, (d) 235A, 235E, 235G, 235Q, 235R or 235S, (e) 237A, 237E, 237K, 237N or 237R, (f) 234A, 234V or 234F, (g) 233P, (h) 328A, (i) 327Q or 327T, (j) 329A, 329G, 329Y or 329R, (k) 331S, (l) 236F or 236R, (m) 238A, 238E, 238G, 238H, 238I, 238 V, 238W, or 238Y, (n) 248A, (o) 254D, 254E, 254G, 254H, 254I, 254N, 254P, 254Q, 254T, or 254V, (p) 255N, (q) 256H, 256K, 256R, or 256V, (r) 264S, (s) 265H, 265K, 265S, 265Y, or 265A, (t) 267G, 267H, 267I, or 267K, (u) 268K, (v) 269N or 269Q, (w) 270A, 270G, 270M, or 270N, (x) 271T, (y) 272N, (z) 292E, 292F, 292G or 292I, (aa) 293S, (bb) 301W, (cc) 304E, (dd) 311E, 311G or 311S, (ee) 316F, (ff) 328V, (gg) 330R, (hh) 339E or 339L, (ii) 343I or 343V, (jj) 373A, 373G or 373S, (kk) 376E, 376W or 376Y, (ll) 380D, (mm) 382D or 382P, (nn) 385P, (oo) 424H, 424M or 424V, (pp) 434I, (qq) 438G, (rr) 439E, 439H or 439Q, (ss) 44 0A, 440D, 440E, 440F, 440M, 440T, or 440V, (tt) E233P, (uu) L235E, (vv) L234A and L235A, (ww) L234A, L235A, and G237A, (xx) L234A, L235A, and P329G, (yy) L234F, L235E, and P331S, (zz) L234A, L235E, and G237A, (aaa) L234A, L235E, G237A, and P331S, (bbb) L234A, L235A, G237A, P238S, H268A, A330S, and P331S (IgG1σ),(ccc) L234A, L235A and P329A, (ddd) G236R and L328R, (eee) G237A, (fff) F241A, (ggg) V264A, (hhh) D265A, (iii) D265A and N297A, (jjj) D265A and N297G, (kkk) D270A, (lll) A330L, (mmm) P331A or P331S, or (nnn) any combination of two or more selected from (a)–(uu), according to Kabat numbering.

[0151] In some embodiments, the antibody or antigen-binding fragment comprises a human IgG4 Fc region. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region comprising a sequence having at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identity to any one of SEQ ID NOs: 320-362. In some embodiments, the antibody or antigen-binding fragment comprises a crystallizable fragment (Fc) region having antibody-dependent cell-mediated cytotoxicity (ADCC) function reduced compared to human IgG1 and / or complement-dependent cytotoxicity (CDC) reduced compared to human IgG1. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises human IgG1, and the human IgG1 comprises SEQ ID NO: 320. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the ADCC function of the Fc region with reduced ADCC is reduced by at least about 50% as compared to human IgG1. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the CDC function of the Fc region with reduced CDC is reduced by at least about 50% as compared to human IgGl. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises (i) a human IgG4 Fc region or (ii) a human IgG4 Fc region comprising (a) S228P, (b) S228P and L235E, or (c) S228P, F234A, and L235A, according to Kabat numbering. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises a human IgG2 Fc region; an IgG2-IgG4 cross-subclass Fc region; an IgG2-IgG3 cross-subclass Fc region; an IgG2 comprising H268Q, V309L, A330S, P331S (IgG2m4); or an IgG2 comprising V234A, G237A, P238S, H268A, V309L, A330S, P331S (IgG2σ).In some embodiments, the antibody or antigen binding fragment comprises an Fc region and wherein the Fc comprises a region selected from the group consisting of 329A, 329G, 329Y, 331S, 236F, 236R, 238A, 238E, 238G, 238H, 238I, 238V, 238W, 238Y, 248A, 254D, 254E, 254G, 254H, 254I, 254N, 254 40F, 440M, 440T, and 440V are substituted human IgG1s numbered according to Kabat.

[0152] In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 320-362. In some embodiments, the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises any one of SEQ ID NOs: 401-413 or a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to any one of SEQ ID NOs: 401-413. In some embodiments, the antibody or antigen-binding fragment comprises a heavy chain comprising any one of SEQ ID NOs: 501-513, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs: 501-513. In some embodiments, the antibody or antigen-binding fragment comprises a light chain comprising any one of SEQ ID NOs: 514, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical to any one of SEQ ID NOs: 514.

[0153] In some embodiments, the combination of polymorphisms is detected in a sample by performing an assay on the sample configured to detect the presence of a combination of nucleotides corresponding to nucleic acid position 501 within a combination of sequences selected from SEQ ID NOs: 2001-2041 and 2057-2059.

[0154] In some embodiments, the inflammatory, fibrotic or fibrostenotic disease or condition comprises inflammatory bowel disease, Crohn's disease, obstructive Crohn's disease, ulcerative colitis, intestinal fibrosis, intestinal fibrostenosis, rheumatoid arthritis or primary sclerosing cholangitis. In some embodiments, Crohn's disease is ileal Crohn's disease, ileocolic Crohn's disease or colonic Crohn's disease. In some embodiments, the subject is unresponsive or has lost response to standard therapy, or is at risk of being unresponsive or losing response to standard therapy, and the standard therapy includes glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy, anti-IL12p40 therapy or a combination thereof.

[0155] In some embodiments, the method further comprises determining whether the subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression based at least in part on the at least three polymorphisms detected in the sample. In some embodiments, the at least three polymorphisms are detected using an assay comprising quantitative polymerase chain reaction (qPCR), nucleic acid sequencing reaction, or genotyping array.

[0156] In some embodiments, the combination of polymorphisms comprises or consists of any combination of the polymorphisms described in Table 31, column 2, row x, where x is any number between 2 and 1374.

[0157] In some embodiments, the polymorphism combination comprises or consists of any combination of the polymorphisms described in Table 31, column 2, row x, wherein x is any number between 2 and 1374, wherein the polymorphisms in the polymorphism combination have beta coefficients described in Table 1, column 1, row x, and wherein the polymorphisms in the polymorphism combination are numerically coded as described in Table 1, column 2, row x.

[0158] In further embodiments, the at least three polymorphisms are at least eight polymorphisms. In some embodiments, the at least eight polymorphisms are provided in the 8-SNP model in Table 25.

[0159] In some embodiments, the method further comprises providing a sample to determine the PRI, MRS, or RPS. In some embodiments, the method further comprises selecting a subject based on the PRI, MRS, or RPS.

[0160] In some embodiments, the method further comprises: contacting the genetic material in the sample with one or more nucleic acid primer pairs having forward and reverse primers capable of hybridizing to one or more target nucleic acid sequences, wherein the one or more target nucleic acid sequences collectively constitute the chromosomal position of the polymorphism in column 2, row x of Table 31, where x is any number between 2 and 1374; amplifying the target nucleic acid sequence by polymerase chain reaction using the nucleic acid primer pairs of the contacting step; inputting the results from the amplification step into a computer system; and analyzing the results via the computer system to determine the PRI, RPS or MRS, wherein the computer system includes a storage unit configured to store the parameters in column 1, row y of Table 31, where y is the same as x in the contacting step.

[0161] In some embodiments, the β0 used to calculate MRS is about 0.0077127943934849 or about 0.008. In some embodiments, the cutoff value for MRS is about 0.0322446725024791 or about 0.03.

[0162] Further aspects and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description, in which only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature and not restrictive.

[0163] 3. Incorporation by Reference

[0164] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. If any publication, patent, or patent application incorporated by reference contradicts the disclosure contained in this specification, the present specification is intended to supersede and / or take precedence over any such contradictory material. 4. Description of the Figures

[0165] The novel features of the present invention are particularly set forth in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by referring to the following detailed description of illustrative embodiments that illustrate the principles of the present invention and the accompanying drawings (also referred to herein as "Figures" and "FIG.").

[0166] Figure 1 A workflow for processing a biological sample obtained from a subject to provide information for selecting a therapeutic agent to treat a disease or condition in the subject is shown, according to embodiments of the present disclosure.

[0167] Figure 2 A computer-implemented workflow for generating an electronic report to a user (such as a physician) including a subject's TNFSF15 profile based on analysis of genotype data from the subject is shown according to an embodiment of the present disclosure.

[0168] Figure 3 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein.

[0169] Figure 4 A computer-implemented workflow for generating a TNFSF15 profile according to an embodiment of the present disclosure is shown.

[0170] Figures 5A-5C Shown is a cluster analysis within a TL1A companion diagnostic (CDx) dataset according to some embodiments herein. Figure 5A Cluster 1 is shown, Figure 5B Cluster 2 is shown, and Figure 5C Cluster 3 is shown, which is from the TL1A CDx dataset.

[0171] Figure 6 Shown from Figures 5A-5C The three clusters were reduced to two clusters (the left side shows the high TL1A expression cluster, and the right side shows the low TL1A expression cluster).

[0172] Figures 7A-7C Shown is a chromatogram of analytical size exclusion chromatography of anti-TL1A antibody. Figure 7A Shown are chromatograms from analytical size exclusion chromatography of antibodies A193, A194 and A195. Figure 7B Shown are chromatograms from analytical size exclusion chromatography of antibodies A196, A197, and A198. Figure 7C Shown are chromatograms from analytical size exclusion chromatography of antibodies A199, A200 and A201.

[0173] Figures 8A-8B Depicted is the inhibition of interferon gamma in human blood by anti-TL1A antibodies. Figure 8A Depicted is the inhibition of interferon gamma in human blood by anti-TL1A antibodies A219 and A213. Figure 8B Depicted is the inhibition of interferon gamma in human blood by the anti-TL1A antibody A212.

[0174] Figures 9A-9C A PLS model showing the effects of pH and protein concentration on viscosity is plotted. Figure 9A shows the PLS plot, Figure 9B A model predicting the relationship between viscosity and anti-TL1A antibody concentration (mg / mL) is shown, and Figure 9CThe relationship model between estimated viscosity and actual viscosity is shown. The unit of viscosity is mPa-s.

[0175] Figure 10 Illustrated are TL1ACDx performance statistics for the TL1A ex vivo production assay, according to some embodiments.

[0176] Figures 11A-11C It was shown that based on gene expression profiles established from single-cell RNA sequencing, classes or subclasses of cells in gastrointestinal tissues can be distinguished. Figure 11A It is shown that the cell types and subcell types listed in the figure legend can be distinguished based on gene expression profiles established from single-cell RNA sequencing. Figure 11B It was shown that based on gene expression profiles established from single-cell RNA sequencing, B cells, epithelial cells, innate lymphoid cells (ILCs), myeloid cells, plasma cells, stromal cells, and T cells could be distinguished. Figure 11C It was shown that various classes and subclasses of cells could also be identified by hierarchical cluster analysis.

[0177] Figures 11D-11K We show that various cell types and subcell types from gastrointestinal tissue can be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known cell type markers. Figure 11D It was shown that B cells, epithelial cells, innate lymphoid cells (ILCs), mast cells, MNPs, pDCs, plasma cells, stromal cells, and T cells could be distinguished based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type. Figure 11D In the figure, cell types are listed at the top, known cell markers are listed in the left axis, and each column in the figure represents a single cell. Figure 11E Shown with Figure 11D Same results and data, except that the average of all single cells for each cell type is presented. Figure 11F Based on Figure 11D and Figure 11E Same data, except unsupervised analysis of differentially expressed genes (DEGs) was performed and the top 10 DEGs for each cell type are shown. Figure 11G It was shown that based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type, subcell types within the epithelium can be distinguished, including BEST+ epithelial cells, colon epithelial cells, EECs, enterocytes, goblet cells, microfold cells, Paneth cells, stem cells, TA cells, and tuft cells. Figure 11G In the figure, cell types are listed at the top, known cell markers are listed in the left axis, and each column in the figure represents a single cell. Figure 11H Shown with Figure 11G Same results and data, except that the average of all single cells for each cell type is presented. Figure 11I It was shown that based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type, subcell types within endothelial cells could be distinguished, including ACKR1+ endothelial cells, activated fibroblasts, CD36+ endothelial cells, fibroblasts, glial cells, lymphocytes, myofibroblasts, pericytes, and smooth muscle cells. Figure 11I In the figure, cell types are listed at the top, known cell markers are listed in the left axis, and each column in the figure represents a single cell. Figure 11J It was shown that based on gene expression profiles established from single-cell RNA sequencing and validated with known cytokine markers for each cell type, subcell types within T cells can be distinguished, including cytotoxic T cells, highly activated T cells, ILC1, ILC3, naive / CM T cells, natural killer (NK) cells, NK T-cells, TFH-like cells, Gamma-delta (γδ) T cells, regulatory T cells, and tissue-resident memory T (Trm) cells. Figure 11J In the figure, cell types are listed at the bottom, known cell markers are listed on the left axis, the size of the dots depicts the percentage of the corresponding cell type expressing the cytokine, and the color of the dots depicts the expression level based on RNA-seq, normalized to have the same total reads for all cells. Figure 11K They showed that based on gene expression profiles established from single-cell RNA sequencing and validated with known gene expression markers for each cell type, subcell types within the myeloid lineage can be distinguished, including activated dendritic cells (DCs), DC1, DC2, monocyte-derived dendritic cells (moDCs), plasmacytoid dendritic cells (pDCs), inflammatory macrophages, and resident macrophages. Figure 11K In the figure, cell types are listed at the top, known cell markers are listed on the left axis, and each column in the figure represents a single cell. Figures 11D-11I In 11K and 11K, gene expression levels are depicted by color-coded expression scores, with 0 being the lowest and 6 being the highest, normalized to have the same total read counts across all cells.

[0178] Figure 12A Shown is the procedure for validating the MuSiC method for deconvolution of bulk RNA sequencing data. Figure 12B The right panel shows single cells used to construct pseudo-bulk RNA sequencing data (X-axis at bottom); Figure 12B The left panel shows the cell types (X-axis at the bottom) and their respective proportions (heat map colors) deconvolved from pseudo-bulk RNA sequencing data, and Figure 12BThe validation in was performed on multiple patients, and the subject number is indicated on the Y-axis. Figure 12C Shown, Figure 12B The absolute difference between the left and right panels is small, indicating that the cell types and their respective proportions deconvolved from the pseudo-bulk RNA-seq data match well with the single-cell RNA-seq data used to construct the pseudo-bulk data. Figure 12D The right panel shows single cells used to construct pseudo-bulk RNA sequencing data (X-axis at bottom); Figure 12D The left panel shows the cell types (X-axis at the bottom) and their respective proportions (heat map colors) deconvolved from pseudo-bulk RNA sequencing data, and Figure 12D The validation in was performed on multiple patients, and the subject number is indicated on the Y-axis. Figure 12E Shown, Figure 12D The absolute difference between the left and right panels is small, indicating that the cell types and their respective proportions deconvolved from the pseudo-bulk RNA-seq data match well with the single-cell RNA-seq data used to construct the pseudo-bulk data. Figure 12B and Figure 12D In the , the colors of the heatmap indicate the proportion of cells according to the color legend, and in Figure 12C and Figure 12E , the colors of the difference plots indicate the magnitude of the differences according to the color legend.

[0179] Figure 13A The results show that 31 cell types or subcell types were identified in the Mount Sinai GSE83687 RNAseq dataset, which were enriched or depleted in UC or CD patient tissues compared with control samples. Figure 13B It is shown that 34 cell types or subcell types were identified in the Lloyd Price GSE111889 RNAseq dataset that were enriched or depleted in UC or CD patient tissues compared to control samples. Figure 13C It was shown that 20 cell types or subcell types were identified in UC119, which were enriched or depleted in UC patient tissues. Figure 13D It was shown that 20 cell types or sub-cell types were identified in CD100 that were enriched or depleted in CD patient tissues. Figure 13E It is shown that the deconvoluted cell types and cell type ratios indicate that Paneth cells are enriched in the small intestine and ileum compared to other tissues in the gastrointestinal tract, which is consistent with the known distribution of Paneth cells. Figure 13F It is shown that the deconvoluted cell types and cell type ratios indicate that Paneth cells are enriched in small intestinal tissue compared to rectum or colon, which is consistent with the known distribution of Paneth cells.

[0180] Figures 13G-13JShown, based on the Mount Sinai GSE83687 RNAseq dataset ( Figure 13G ), LloydPrice GSE111889 RNAseq dataset ( Figure 13H )、UC119( Figure 13I ) and CD100( Figure 13J ), activated fibroblasts were enriched in both CD and UC diseased tissues compared to non-diseased control tissues. Figures 13K-13N Shown, based on the Mount Sinai GSE83687 RNAseq dataset ( Figure 13K ), Lloyd Price GSE111889 RNAseq dataset ( Figure 13L )、UC119( Figure 13M ) and CD100( Figure 13N ) deconvolution, as shown by the enrichment of monocyte-derived dendritic cells (moDCs) in both CD and UC diseased tissues compared with non-diseased control tissues. Figures 13O-13R Shown, based on the Mount SinaiGSE83687 RNAseq dataset ( Figure 13O ), Lloyd Price GSE111889 RNAseq dataset ( Figure 13P )、UC119( Figure 13Q ) and CD100( Figure 13R ) deconvolution, as shown by the enrichment of CD36+ endothelial cells in both CD and UC diseased tissues compared to non-diseased control tissues. Figures 13S-13V Shown, based on the Mount Sinai GSE83687 RNAseq dataset ( Figure 13S ), Lloyd Price GSE111889 RNAseq dataset ( Figure 13T )、UC119( Figure 13U ) and CD100( Figure 13V ), as shown by deconvolution of BEST4+ enterocytes / epithelial cells in CD and / or UC diseased tissues compared to non-diseased control tissues. Figures 13W-13X Shown, based on the Mount Sinai GSE83687 RNAseq dataset ( Figure 13W ) and Lloyd Price GSE111889 RNAseq dataset ( Figure 13X ) deconvolution, as shown by the reduction of tuft cells in CD and / or UC diseased tissues compared to non-diseased control tissues. Figures 13A-13X In the figure, the y-axis indicates the proportion of identified cells to all cells.

[0181] Figure 14The software design architecture of the measurement interpretation software according to some embodiments of the present invention is shown.

[0182] Figures 15A-15K Shown are screenshots illustrating a user experience of an assay interpretation software application according to some embodiments herein. The assay interpretation software application includes a user interface including a login view ( Figure 15A ), File View( Figure 15B ), Sample View( Figure 15C ), data analysis view ( Figure 15D ), new data analysis view ( Figure 15E ), System Settings View ( Figure 15F ), Clinical Setting View( Figure 15G ), Clinical Study Setup View ( Figure 15H ), user management view ( Figure 15I ), Activity View( Figure 15J ) or My Profile view ( Figure 15K ) or any combination thereof.

[0183] Figures 16A-16B Exemplary clinical trial studies of the anti-TL1A antibodies disclosed herein are shown. Figure 16A Depicted is a study plan for the induction phase of a Phase 2 clinical trial of A219 in UC, according to some embodiments herein. Figure 16B Depicted is the study plan for the open-label extension phase of the Phase 2 clinical trial of A219 in UC, according to some embodiments herein.

[0184] Figures 17A-17I Comparison of clinical remission and endoscopic improvement between CDx-classified patients and all patients is shown for various CDx models optimized to predict ileal entero-clonocyte ( Figure 17A ), colonic moDC( Figure 17B and Figure 17C ), colonic goblet cells ( Figure 17D and Figure 17E ), colon-resident macrophages ( Figure 17F and Figure 17G ) and colonic transit-amplifying (TA) progenitor cells ( Figure 17H and Figure 17I ) in tissues affected by IBD, as further described in Section 7.23 (Example 23).

[0185] Figure 17J It was shown that CD patients who had received advanced IBD therapy had a robust response after A219 treatment, as measured by clinical remission.

[0186] Figure 17Kshowed that UC patients who had not received advanced IBD therapy ( Figure 17K patients who had received treatment for advanced IBD ( Figure 17K “treated” in the % of patients) and the overall patient population (both treatment-naive and treatment-experienced, Figure 17K ) had a higher response rate, as indicated by clinical remission ( Figure 17K Left panel in the figure) and improved endoscopic examination ( Figure 17K Measured in the right panel). 5. Specific implementation methods

[0187] Provided herein are methods, systems, and kits for treating a subject who may be suitable for treatment with an inhibitor of tumor necrosis factor (ligand) superfamily, member 15 (TL1A) activity or expression, provided that the subject is a carrier of a genotype. The subject may be a patient who may be diagnosed with an inflammatory, fibrostenotic, or fibrotic disease, such as inflammatory bowel disease (IBD) or Crohn's disease (CD). The subject may not be a patient, but may be suspected of having an inflammatory, fibrostenotic, or fibrotic disease. In some cases, the genotype may be useful for treating an inflammatory, fibrostenotic, or fibrotic disease or condition, such as one mediated by TL1A. In some embodiments, if the genotype is detected, the subject is treated by administering an inhibitor of TL1A activity or expression (e.g., an anti-TL1A antibody) to the subject. In some cases, it is necessary to identify a subject as suitable for treatment with an inhibitor of activity or expression in order to administer the inhibitor to the subject.

[0188] See also Figure 1 The methods, systems, and kits disclosed herein, in some embodiments, involve the following steps: providing a buccal swab sample from a subject 101; optionally purifying DNA from the sample by treating the sample 102; assaying the optionally treated sample to detect the genotype of at least three genetic loci in the sample 103; processing the genotype to generate a TNFSF15 profile 104; and treating the subject with an anti-TL1A antibody or antibody fragment as disclosed herein based on the TNFSF15 profile to treat the subject's disease or condition 105.

[0189] Genotype described herein is detected using suitable genotyping equipment (e.g., array, sequencing). In some cases, samples are obtained indirectly or directly from a subject or patient. In some cases, the sample can be obtained by the subject. In other cases, the sample can be obtained by a healthcare professional such as a nurse or doctor. The sample can be derived from almost any biological fluid or tissue containing genetic information, such as blood.

[0190] The subject disclosed herein can be a mammal, such as, for example, a mouse, rat, guinea pig, rabbit, non-human primate, or farm animal. In some cases, the subject is a human. In some cases, the subject suffers from symptoms associated with a disease or condition disclosed herein (e.g., abdominal pain, cramps, diarrhea, rectal bleeding, fever, weight loss, fatigue, loss of appetite, dehydration and malnutrition, anemia, or ulcers).

[0191] In some embodiments, the subject is susceptible to or is suffering from thiopurine toxicity or a disease caused by thiopurine toxicity (such as pancreatitis or leukopenia). The subject may or is suspected of being unresponsive or losing response to standard treatment (e.g., anti-TNFα therapy, anti-a4-b7 therapy (vedolizumab), anti-IL12p40 therapy (ustekinumab), thalidomide, or cytoxin).

[0192] The diseases or conditions disclosed herein can be inflammatory diseases, fibrostenotic diseases, or fibrotic diseases. In some cases, the disease or condition is a TL1A-mediated disease or condition. The term "TL1A-mediated disease or condition" refers to a disease or condition pathology or pathogenesis that is driven, at least in part, by TL1A signaling. In some cases, the disease or condition is an immune-mediated disease or condition, such as those mediated by TL1A.

[0193] In some embodiments, the disease or condition is an inflammatory disease or disorder mediated at least in part by TL1A signaling. Non-limiting examples of inflammatory diseases include: allergies, ankylosing spondylitis, asthma, atopic dermatitis, autoimmune diseases or disorders, cancer, celiac disease, chronic obstructive pulmonary disease (COPD), chronic peptic ulcers, cystic fibrosis, diabetes (e.g., type 1 diabetes and type 2 diabetes), glomerulonephritis, gout, hepatitis (e.g., active hepatitis), immune-mediated diseases or disorders, inflammatory bowel disease (IBD) such as Crohn's disease and ulcerative colitis, myositis, osteoarthritis, pelvic inflammatory disease (PID), multiple sclerosis, neurodegenerative diseases of the elderly, periodontal disease (e.g., periodontitis), pre-perfusion injury transplant rejection, psoriasis, pulmonary fibrosis, rheumatic diseases, scleroderma, sinusitis, tuberculosis.

[0194] In some embodiments, the disease or condition is an autoimmune disease mediated at least in part by TL1A signaling. Non-limiting examples of autoimmune diseases or conditions include achalasia, Addison's disease, adult-onset Still's disease, agammaglobulinemia, alopecia areata, amyloidosis, ankylosing spondylitis, anti-GBM / anti-TBM nephritis, antiphospholipid syndrome, autoimmune angioedema, autoimmune familial dysautonomia, autoimmune encephalomyelitis, autoimmune hepatitis, autoimmune inner ear disease (AIED), autoimmune myocarditis, autoimmune oophoritis, autoimmune orchitis, autoimmune pancreatitis, autoimmune retinopathy, autoimmune urticaria, axonal and neuronal neuropathy (AMAN), Barlow's disease, Behcet's disease, benign mucous membrane pemphigoid , bullous pemphigoid, Castleman disease (CD), celiac disease, Chagas disease, chronic inflammatory demyelinating polyneuropathy (CIDP), chronic relapsing multifocal osteomyelitis (CRMO), Churg-Strauss syndrome (CSS) or eosinophilic granulomatosis with pancreatic leukemia (EGPA), cicatricial pemphigoid, Cogan syndrome, cold agglutinin disease, congenital heart block, coxsackie myocarditis, CREST syndrome, Crohn's disease, dermatitis herpetiformis, dermatomyositis, Devic's disease (neuromyelitis optica), discoid lupus, Dressler syndrome, endometriosis, eosinophilic esophagitis (EoE), eosinophilic fasciitis, erythema nodosum, idiopathic Mixed cryoglobulinemia, Evans syndrome, fibromyalgia, fibrosing alveolitis, giant cell arteritis (temporal arteritis), giant cell myocarditis, glomerulonephritis, Goodpasture syndrome, granulomatosis with polyangiitis, Graves' disease, Guillain-Barré syndrome, Hashimoto's thyroiditis, hemolytic anemia, Henoch-Schonlein purpura (HSP), herpes gestationis or pemphigoid gestationis (PG), hidradenitis suppurativa (HS) (acne inversa), hypogammaglobulinemia, IgA nephropathy, IgG4-related sclerosing disease, immune thrombocytopenic purpura (ITP), inclusion body myositis (IBM), interstitial cystitis (IC), juvenile arthritis, juvenile diabetes (1 Type 2 diabetes mellitus), juvenile myositis (JM), Kawasaki disease, Lambert-Eaton syndrome, leukocytoclastic vasculitis, lichen planus, lichen sclerosus, woody conjunctivitis, linear IgA disease (LAD), lupus, chronic Lyme disease, Meniere's disease, microscopic polyangiitis (MPA), mixed connective tissue disease (MCTD), Mooren's ulcer, Mucha-Habermann disease, multifocal motor neuropathy (MMN) or MMNCB, multiple sclerosis, myasthenia gravis, myositis, narcolepsy, neonatal lupus, neuromyelitis optica, neutropenia, ocular cicatricial pemphigoid, optic neuritis, palliative rheumatism (PR), PANDAS,Paraneoplastic cerebellar degeneration (PCD), paroxysmal nocturnal hemoglobinuria (PNH), Parry-Romberg syndrome, pars planitis (peripheral uveitis), Parsonage-Turner syndrome, pemphigus, peripheral neuropathy, perivenous encephalomyelitis, pernicious anemia (PA), POEMS syndrome, polyarteritis nodosa, polyglandular syndromes types I, II, and III, polymyalgia rheumatica, polymyositis, post-myocardial infarction syndrome, postpericardiotomy syndrome, primary biliary cirrhosis, primary sclerosing cholangitis, progesterone-induced dermatitis, psoriasis, psoriatic arthritis, pure red cell aplasia (PRCA), pyoderma gangrenosum, Raynaud's phenomenon, reactive arthritis, reflex sympathetic dystrophy, relapsing polychondritis, restless legs syndrome (RLS), retroperitoneal fibrosis, rheumatic fever, rheumatoid arthritis, sarcoidosis, Schmidt syndrome, scleritis, scleroderma, Sjögren's syndrome, sperm and testicular autoimmunity, stiff-man syndrome (SPS), subacute bacterial endocarditis (SBE), Susac's syndrome, sympathetic ophthalmia (SO), Takayasu's arteritis, temporal arteritis / giant cell arteritis, thrombocytopenic purpura (TTP), Tolosa-Hunt syndrome (THS), transverse myelitis, type 1 diabetes mellitus, ulcerative colitis (UC), undifferentiated connective tissue disease (UCTD), uveitis, vasculitis, vitiligo, and Vogt-Koyanagi-Harada disease.

[0195] In some embodiments, the disease or condition is a cancer mediated at least in part by TL1A signaling. Non-limiting examples of cancer include adenoid cystic carcinoma, adrenal cancer, amyloidosis, anal cancer, ataxia telangiectasia, atypical nevus syndrome, basal cell carcinoma, bile duct cancer, Birt Hogg Dubé syndrome, bladder cancer, bone cancer, brain tumor, breast cancer, male breast cancer, carcinoid tumor, cervical cancer, colorectal cancer, ductal carcinoma, endometrial cancer, esophageal cancer, gastric cancer, gastrointestinal stromal tumor (GIST), HER2-positive breast cancer, islet cell tumor, juvenile polyposis syndrome, kidney cancer, laryngeal cancer, leukemia - acute lymphoblastic leukemia, leukemia - acute lymphoblastic (ALL), leukemia - acute myeloid AML, leukemia - adult, leukemia - childhood, leukemia - chronic lymphocytic (CLL), leukemia - chronic myeloid (CML), liver cancer, lobular carcinoma, lung cancer, small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), Hodgkin lymphoma, non-Hodgkin lymphoma, malignant glioma, melanoma, meningioma, multiple myeloma, myelodysplastic syndrome (MDS), nasopharyngeal carcinoma, neuroendocrine tumors, oral cancer, osteosarcoma, ovarian cancer, pancreatic cancer, pancreatic neuroendocrine tumors, parathyroid cancer, penile cancer, peritoneal cancer, Peutz-Jeghers syndrome, pituitary adenoma, polycythemia vera, prostate cancer, renal cell carcinoma, retinoblastoma, salivary gland cancer, sarcoma, Kaposi sarcoma, skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymoma, thyroid cancer, uterine (endometrial) cancer, vaginal cancer, and Wilms' tumor.

[0196] In some embodiments, the disease or condition is inflammatory bowel disease, such as Crohn's disease (CD) or ulcerative colitis (UC). The subject may suffer from fibrosis, fibrostenosis or fibrotic diseases, which exist alone or simultaneously with inflammatory diseases. In some cases, CD is severe CD. Severe CD may be caused by inflammation, which has resulted in scar tissue (fibrostenosis) and / or swelling in the intestinal wall. In some cases, severe CD is characterized by the presence of fibrosis and / or inflammatory stenosis. Stenosis can be determined by computed tomography enterography (CTE) and magnetic resonance imaging enterography (MRE). The disease or condition may be described as refractory, which in some cases means that the disease is resistant to standard treatment (e.g., anti-TNFα therapy). Non-limiting examples of standard treatment include glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy (vedolizumab), anti-IL12p40 therapy (ustekinumab), thalidomide and cytotoxins.

[0197] 5.1 Genotype

[0198] Disclosed herein is a genotype in a sample that can be detected by analyzing the genetic material in the sample obtained from the subject. In some cases, the subject can be a human. In some embodiments, the genetic material is obtained from a subject suffering from a disease or condition disclosed herein. In some cases, the genetic material is obtained from blood, serum, plasma, sweat, hair, tears, urine, and other technologies known to those skilled in the art. In some cases, the genetic material is obtained from a biopsy (e.g., intestinal biopsy from a subject).

[0199] The genotype of the present disclosure comprises genetic material that is deoxyribonucleic acid (DNA). In some cases, the genotype comprises a denatured DNA molecule or a fragment thereof. In some cases, the genotype comprises a DNA selected from the group consisting of genomic DNA, viral DNA, mitochondrial DNA, plasmid DNA, amplified DNA, circular DNA, circulating DNA, cell-free DNA, or exosomal DNA. In some cases, the DNA is single-stranded DNA (ssDNA), double-stranded DNA, denatured double-stranded DNA, synthetic DNA, and combinations thereof. Circular DNA may be cut or fragmented.

[0200] The genotype disclosed herein comprises at least one polymorphism at a gene or genetic locus described herein. In some cases, the gene or genetic locus is selected from tumor necrosis factor (ligand) superfamily member 15 (TNFSF15), THADA armadillo repeat-containing sequence (THADA), Pleckstrin homology, MyTH4 and FERM domain-containing H2 (PLEKHH2), XK-related 6 (XKR6), myotubularin-related protein 9 (MTMR9), ETS proto-oncogene 1, transcription factor (ETS1), C-type lectin domain-containing 16A (CLEC16A), suppressor of cytokine signaling 1 (SOCS1), protein tyrosine phosphatase non-receptor type 2 (PTPN2), inducible T cell costimulatory ligand (ICOSLG), Janus kinase 2 (JAK2), catenin delta 2 (CTNND2), regulator of G protein signaling 7 (RGS7), RNA binding Fox 1 homolog 1 (RBFOX1), RNA binding motif protein 17 (RBM17), 6-phosphofructo-2-kinase / fructose-2,6-bisphosphatase 3 (PFKFB3), Ecto-NOX disulfide-thiol exchanger 1 (ENOX1), coiled-coil domain-containing 122 (CCDC122), regulator of telomere elongation helicase 1 (RTEL1), TNF receptor superfamily member 6b (TNFRSF6B), GLIS family zinc finger 3 (GLIS3), solute carrier family 1 member 1 (SLC1A1), IKAROS family zinc finger 2 (IKZF2), fatty acyl-CoA reductase 1 (FAR1), spondin 1 (SPON1), plexin A2 (PLXNA2), MIR205 host gene (MIR205HG), C-type lectin domain-containing 16A (CLEC16A), PR / SET domain 14 (PRDM), autophagy-related 5 (ATG5) and prostaglandin E receptor 4 (PTGER4). In some cases, the gene or genetic locus comprises the gene or genetic locus provided in Table 1. In some cases, the genotype disclosed herein is a haplotype. In some cases, the genotype comprises a specific polymorphism, a polymorphism in linkage disequilibrium (LD) therewith, or a combination thereof. In some cases, the LD consists of an r of at least or about 0.70, 0.75, 0.80, 0.85, 0.90 or 1.0. 2 The genotypes disclosed herein can comprise at least or about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or more polymorphisms. In a preferred embodiment, the genotypes disclosed herein comprise a combination of three polymorphisms, such as those provided in Table 1.

[0201] Polymorphism described herein can be single nucleotide polymorphism or insertion / deletion (insertion / deletion).In some cases, this polymorphism is the insertion or deletion (for example, insertion / deletion) of at least one core base.In some cases, genotype can comprise copy number variation (CNV), it is the variation of the nucleotide sequence number between individual in given colony.In some cases, CNV comprises at least or about two, three, four, five, six, seven, eight, nine, ten, twenty, thirty, forty or fifty nucleic acid molecules.In some cases, genotype is heterozygous.In some cases, genotype is homozygous.

[0202] The genotypes disclosed herein are disclosed herein in the following embodiments:

[0203] 1. A genotype comprising at least one polymorphism at a gene or genetic locus.

[0204] 2. The genotype of embodiment 1, comprising the polymorphisms provided in Table 1.

[0205] 3. The genotype of embodiment 1-2, which is heterozygous.

[0206] 4. The genotype of embodiment 1-2, which is homozygous.

[0207] 5. The genotype of embodiments 1-4, wherein the genotype comprises at least two polymorphisms.

[0208] 6. The genotype of embodiments 1-4, wherein the genotype comprises at least three polymorphisms.

[0209] 7. The genotype of embodiments 1-4, wherein the genotype comprises at least four polymorphisms.

[0210] 8. The genotype of embodiments 1-4, wherein the genotype comprises at least five polymorphisms.

[0211] 9. The genotype of embodiments 1-4, wherein the genotype comprises at least six polymorphisms.

[0212] 10. The genotype of embodiments 1-4, wherein the genotype comprises at least seven polymorphisms.

[0213] 11. The genotype of embodiments 1-4, wherein the genotype comprises at least eight polymorphisms.

[0214] 12. The genotype of embodiment 1, comprising a polymorphism in linkage disequilibrium with the polymorphisms provided in Table 1.

[0215] 13. The genotype of embodiment 12, wherein LD is determined by (i) a D' value of at least about 0.70 or (ii) a D' value of 0 and an r value of at least about 0.70. 2 Defined by value.

[0216] 14. The genotype of embodiment 12, wherein LD is determined by (i) a D' value of at least about 0.80 or (ii) a D' value of 0 and an r value of at least about 0.80. 2 Defined by value.

[0217] 15. The genotype of embodiment 12, wherein LD is determined by (i) a D' value of at least about 0.90 or (ii) a D' value of 0 and an r value of at least about 0.90. 2 Defined by value.

[0218] 16. The genotype of embodiment 12, wherein LD is determined by (i) a D' value of at least about 0.95 or (ii) a D' value of 0 and an r value of at least about 0.95. 2 Defined by value.

[0219] 17. The genotype of embodiments 1-16, wherein the gene or genetic locus is selected from tumor necrosis factor (ligand) superfamily member 15 (TNFSF15), THADA armadillo repeat-containing sequence (THADA), Pleckstrin homology, MyTH4 and FERM domain-containing H2 (PLEKHH2), XK-related 6 (XKR6), myotubularin-related protein 9 (MTMR9), ETS proto-oncogene 1, transcription factor (ETS1), C-type lectin domain-containing 16A (CLEC16A), suppressor of cytokine signaling 1 (SOCS1), protein tyrosine phosphatase non-receptor type 2 (PTPN2), inducible T-cell costimulatory ligand (ICOSLG), Janus kinase 2 (JAK2), catenin delta 2 (CTNND2), regulator of G protein signaling 7 (RGS7), RNA-binding Fox 1 homolog 1 (RBFOX1), RNA binding motif protein 17 (RBM17), 6-phosphofructo-2-kinase / fructose-2,6-bisphosphatase 3 (PFKFB3), Ecto-NOX disulfide-thiol exchanger 1 (ENOX1), coiled-coil domain-containing 122 (CCDC122), regulator of telomere elongation helicase 1 (RTEL1), TNF receptor superfamily member 6b (TNFRSF6B), GLIS family zinc finger 3 (GLIS3), solute carrier family 1 member 1 (SLC1A1), IKAROS family zinc finger 2 (IKZF2), fatty acyl-CoA reductase 1 (FAR1), spondin 1 (SPON1), plexin A2 (PLXNA2), MIR205 host gene (MIR205HG), C-type lectin domain-containing 16A (CLEC16A), PR / SET domain-containing 14 (PRDM), autophagy-related 5 (ATG5), and prostaglandin E receptor 4 (PTGER4).

[0220] 18. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of: (1) rs16901748, rs7759385, rs4246905; (2) rs16901748, rs7759385, rs7935393; (3) rs16901748, rs7759385, rs1892231; (4) rs16901748, rs7759385, rs12934476; (5) rs16901748, rs7759385, rs9806914; (6) rs16901748, rs7759385, rs2297437; (7) rs16901748, rs7759385, rs2070557; (8) rs16901748, rs7759385, rs7278257; (9) rs16901748, rs7759385, rs11221332; (10) rs16901748, rs77593 85. rs41309367; (11) rs16901748, rs7759385, rs6478109; (12) rs16901748, rs4246905, rs7935393; (13) rs16901748, rs4246905, rs1892231; (14) rs16901748, rs4246905, rs12934476; (15) rs16901748, rs4246905, rs9806914; (16) rs16901748, rs4246905, rs2297437; (17) rs16901748, rs424 6905, rs2070557; (18) rs16901748, rs4246905, rs7278257; (19) rs16901748, rs4246905, rs11221332; (20) rs16901748, rs4246905, rs41309367; ( 21)rs16901748, rs4246905, rs6478109; (22) rs16901748, rs7935393, rs1892231; (23) rs16901748, rs7935393, rs12934476; (24) rs16901748, rs 7935393, rs9806914; (25) rs16901748, rs7935393, rs2297437; (26) rs16901748, rs7935393, rs2070557; (27) rs16901748, rs7935393, rs7278257;(28)rs16901748、rs7935393、rs11221332;(29)rs16901748、rs7935393、rs41309367;(30)rs16901748、rs7935393、rs6478109;(31)rs16901748、rs1892231、rs12934476;(32)rs16901748、rs1892231、rs9806914;(33)rs16901748、rs1892231、rs2297437;(34)rs16901748、rs1892231、rs2070557;(35)rs16901748、rs1892231、rs7278257;(36)rs16901748、rs1892231、rs11221332;(37)rs16901748、rs1892231、rs41309367;(38)rs16901748、rs1892231、rs6478109;(39)rs16901748、rs12934476、rs9806914;(40)rs16901748、rs12934476、rs2297437;(41)rs16901748、rs12934476、rs2070557;(42)rs16901748、rs12934476、rs7278257;(43)rs16901748、rs12934476、rs11221332;(44)rs16901748、rs12934476、rs41309367;(45)rs16901748、rs12934476、rs6478109;(46)rs16901748、rs9806914、rs2297437;(47)rs16901748、rs9806914、rs2070557;(48)rs16901748、rs9806914、rs7278257;(49)rs16901748、rs9806914、rs11221332;(50)rs16901748、rs9806914、rs41309367;(51)rs16901748、rs9806914、rs6478109;(52)rs16901748、rs2297437、rs2070557;(53)rs16901748、rs2297437、rs7278257;(54)rs16901748、rs2297437、rs11221332;(55)rs16901748、rs2297437、rs41309367;<h2 style=";text-align:left;direction:ltr">(56)rs16901748、rs2297437、rs6478109;(57)rs16901748、rs2070557、r s7278257;(58)rs16901748、rs2070557、rs11221332;(59)rs16901748、r s2070557、rs41309367;(60)rs16901748、rs2070557、rs6478109;(61)rs 16901748、rs7278257、rs11221332;(62)rs16901748、rs7278257、rs4130 9367;(63)rs16901748、rs7278257、rs6478109;(64)rs16901748、rs1122 1332、rs41309367;(65)rs16901748、rs11221332、rs6478109;(66)rs169 01748、rs41309367、rs6478109;(67)rs7759385、rs4246905、rs7935393;(68)rs7759385、rs4246905、rs1892231;(69)rs7759385、rs4246905、rs1 2934476;(70)rs7759385、rs4246905、rs9806914;(71)rs7759385、rs4246905、rs2297437;(72)rs7759385、rs4246905、rs2070557;(73)rs775938 5、rs4246905、rs7278257;(74)rs7759385、rs4246905、rs11221332;(75)rs7759385、rs4246905、rs41309367;(76)rs7759385、rs4246905、rs6478 109;(77)rs7759385、rs7935393、rs1892231;(78)rs7759385、rs7935393、rs12934476;(79)rs7759385、rs7935393、rs9806914;(80)rs7759385、r s7935393、rs2297437;(81)rs7759385、rs7935393、rs2070557;(82)rs7759385、rs7935393、rs7278257;(83)rs7759385、rs7935393、rs11221332;<h2 style=";text-align:left;direction:ltr">(84)rs7759385、rs7935393、rs41309367;(85)rs7759385、rs7935393、rs6478109;(86)rs7759385、rs1892231、rs12934476;(87)rs7759385、rs18 92231、rs9806914;(88)rs7759385、rs1892231、rs2297437;(89)rs7759385、rs1892231、rs2070557;(90)rs7759385、rs1892231、rs7278257;(91)r s7759385、rs1892231、rs11221332;(92)rs7759385、rs1892231、rs41309367;(93)rs7759385、rs1892231、rs6478109;(94)rs7759385、rs1293447 6、rs9806914;(95)rs7759385、rs12934476、rs2297437;(96)rs7759385、 rs12934476、rs2070557;(97)rs7759385、rs12934476、rs7278257;(98)rs 7759385, rs12934476, rs11221332;(99)rs7759385, rs12934476, rs41309367;(100)rs7759385, rs12934476, rs6478109;(101)rs7759385, rs980 6914、rs2297437;(102)rs7759385、rs9806914、rs2070557;(103)rs7759385、rs9806914、rs7278257;(104)rs7759385、rs9806914、rs11221332;(1 05)rs7759385、rs9806914、rs41309367;(106)rs7759385、rs9806914、rs6478109;(107)rs7759385、rs2297437、rs2070557;(108)rs7759385、rs22 97437、rs7278257;(109)rs7759385、rs2297437、rs11221332;(110)rs7759385、rs2297437、rs41309367;(111)rs7759385、rs2297437、rs6478109;<h2 style=";text-align:left;direction:ltr">(112)rs7759385、rs2070557、rs7278257;(113)rs7759385、rs2070557、rs 11221332;(114)rs7759385、rs2070557、rs41309367;(115)rs7759385、rs 2070557、rs6478109;(116)rs7759385、rs7278257、rs11221332;(117)rs7759385、rs7278257、rs41309367;(118)rs7759385、rs7278257、rs6478109 (119)rs7759385, rs11221332, rs41309367 (120)rs7759385, rs11221332, rs6478109 (121)rs7759385, rs41309367, rs6478109 (122)rs4246905 、rs7935393、rs1892231;(123)rs4246905、rs7935393、rs12934476;(124)rs4246905、rs7935393、rs9806914;(125)rs4246905、rs7935393、rs229743 7;(126)rs4246905、rs7935393、rs2070557;(127)rs4246905、rs7935393、rs7278257;(128)rs4246905、rs7935393、rs11221332;(129)rs4246905、r s7935393、rs41309367;(130)rs4246905、rs7935393、rs6478109;(131)rs4246905、rs1892231、rs12934476;(132)rs4246905、rs1892231、rs9806914 ;(133)rs4246905、rs1892231、rs2297437;(134)rs4246905、rs1892231、r s2070557;(135)rs4246905、rs1892231、rs7278257;(136)rs4246905、rs1 892231、rs11221332;(137)rs4246905、rs1892231、rs41309367;(138)rs4 246905、rs1892231、rs6478109;(139)rs4246905、rs12934476、rs9806914;(140)rs4246905、rs12934476、rs2297437;(141)rs4246905、rs12934476、rs2070557;(142)rs4246905、rs12934476、rs7278257;(143)rs4246905、rs12934476、rs11221332;(144)rs4246905、rs12934476、rs41309367;(145)rs4246905、rs12934476、rs6478109;(146)rs4246905、rs9806914、rs2297437;(147)rs4246905、rs9806914、rs2070557;(148)rs4246905、rs9806914、rs7278257;(149)rs4246905、rs9806914、rs11221332;(150)rs4246905、rs9806914、rs41309367;(151)rs4246905、rs9806914、rs6478109;(152)rs4246905、rs2297437、rs2070557;(153)rs4246905、rs2297437、rs7278257;(154)rs4246905、rs2297437、rs11221332;(155)rs4246905、rs2297437、rs41309367;(156)rs4246905、rs2297437、rs6478109;(157)rs4246905、rs2070557、rs7278257;(158)rs4246905、rs2070557、rs11221332;(159)rs4246905、rs2070557、rs41309367;(160)rs4246905、rs2070557、rs6478109;(161)rs4246905、rs7278257、rs11221332;(162)rs4246905、rs7278257、rs41309367;(163)rs4246905、rs7278257、rs6478109;(164)rs4246905、rs11221332、rs41309367;(165)rs4246905、rs11221332、rs6478109;(166)rs4246905、rs41309367、rs6478109;(167)rs7935393、rs1892231、rs12934476;<h2 style=";text-align:left;direction:ltr">(168)rs7935393、rs1892231、rs9806914;(169)rs7935393、rs1892231、rs2297437;(170)rs7935393、rs1892231、rs2070557;(171)rs7935393、rs18 92231、rs7278257;(172)rs7935393、rs1892231、rs11221332;(173)rs7935393、rs1892231、rs41309367;(174)rs7935393、rs1892231、rs6478109;(1 75)rs7935393、rs12934476、rs9806914;(176)rs7935393、rs12934476、rs2297437;(177)rs7935393、rs12934476、rs2070557;(178)rs7935393、rs1 2934476、rs7278257;(179)rs7935393、rs12934476、rs11221332;(180)rs7935393、rs12934476、rs41309367;(181)rs7935393、rs12934476、rs64781 09;(182)rs7935393、rs9806914、rs2297437;(183)rs7935393、rs9806914、rs2070557;(184)rs7935393、rs9806914、rs7278257;(185)rs7935393、r s9806914、rs11221332;(186)rs7935393、rs9806914、rs41309367;(187)rs7935393、rs9806914、rs6478109;(188)rs7935393、rs2297437、rs2070557 ;(189)rs7935393、rs2297437、rs7278257;(190)rs7935393、rs2297437、r s11221332;(191)rs7935393、rs2297437、rs41309367;(192)rs7935393、rs 2297437、rs6478109;(193)rs7935393、rs2070557、rs7278257;(194)rs7935393、rs2070557、rs11221332;(195)rs7935393、rs2070557、rs41309367;<h2 style=";text-align:left;direction:ltr">(196)rs7935393、rs2070557、rs6478109;(197)rs7935393、rs7278257、rs11221332;(198)rs7935393、rs7278257、rs41309367;(199)rs7935393、rs7 278257, rs6478109, (200) rs7935393, rs11221332, rs4130936, (201) rs7935393, rs11221332, rs6478109, (202) rs7935393, rs41309367, rs6478109 ;(203)rs1892231、rs12934476、rs9806914;(204)rs1892231、rs12934476 、rs2297437;(205)rs1892231、rs12934476、rs2070557;(206)rs1892231、r s12934476、rs7278257;(207)rs1892231、rs12934476、rs11221332;(208) rs1892231、rs12934476、rs41309367;(209)rs1892231、rs12934476、rs647 8109;(210)rs1892231、rs9806914、rs2297437;(211)rs1892231、rs98069 14、rs2070557;(212)rs1892231、rs9806914、rs7278257;(213)rs1892231、 rs9806914、rs11221332;(214) rs1892231、rs9806914、rs41309367;(215) rs1892231、rs9806914、rs6478109;(216)rs1892231、rs2297437、rs207055 7;(217)rs1892231、rs2297437、rs7278257;(218)rs1892231、rs2297437、 rs11221332;(219)rs1892231、rs2297437、rs41309367;(220)rs1892231、r s2297437、rs6478109;(221)rs1892231、rs2070557、rs7278257;(222)rs18 92231、rs2070557、rs11221332;(223)rs1892231、rs2070557、rs41309367;(224)rs1892231、rs2070557、rs6478109;(225)rs1892231、rs7278257、rs11221332;(226)rs1892231、rs7278257、rs41309367;(227)rs1892231、rs7278257、rs6478109;(228)rs1892231、rs11221332、rs41309367;(229)rs1892231、rs11221332、rs6478109;(230)rs1892231、rs41309367、rs6478109;(231)rs12934476、rs9806914、rs2297437;(232)rs12934476、rs9806914、rs2070557;(233)rs12934476、rs9806914、rs7278257;(234)rs12934476、rs9806914、rs11221332;(235)rs12934476、rs9806914、rs41309367;(236)rs12934476、rs9806914、rs6478109;(237)rs12934476、rs2297437、rs2070557;(238)rs12934476、rs2297437、rs7278257;(239)rs12934476、rs2297437、rs11221332;(240)rs12934476、rs2297437、rs41309367;(241)rs12934476、rs2297437、rs6478109;(242)rs12934476、rs2070557、rs7278257;(243)rs12934476、rs2070557、rs11221332;(244)rs12934476、rs2070557、rs41309367;(245)rs12934476、rs2070557、rs6478109;(246)rs12934476、rs7278257、rs11221332;(247)rs12934476、rs7278257、rs41309367;(248)rs12934476、rs7278257、rs6478109;(249)rs12934476、rs11221332、rs41309367;(250)rs12934476、rs11221332、rs6478109;(251)rs12934476、rs41309367、rs6478109;(252)rs9806914、rs2297437、rs2070557;(253)rs9806914、rs2297437、rs7278257;(254)rs9806914、rs2297437、rs11221332;(255)rs9806914、rs2297437、rs41309367;(256)rs9806914、rs2297437、rs6478109;(257)rs9806914、rs2070557、rs7278257;(258)rs9806914、rs2070557、rs11221332;(259)rs9806914、rs2070557、rs41309367;(260)rs9806914、rs2070557、rs6478109;(261)rs9806914、rs7278257、rs11221332;(262)rs9806914、rs7278257、rs41309367;(263)rs9806914、rs7278257、rs6478109;(264)rs9806914、rs11221332、rs41309367;(265)rs9806914、rs11221332、rs6478109;(266)rs9806914、rs41309367、rs6478109;(267)rs2297437、rs2070557、rs7278257;(268)rs2297437、rs2070557、rs11221332;(269)rs2297437、rs2070557、rs41309367;(270)rs2297437、rs2070557、rs6478109;(271)rs2297437、rs7278257、rs11221332;(272)rs2297437、rs7278257、rs41309367;(273)rs2297437、rs7278257、rs6478109;(274)rs2297437、rs11221332、rs41309367;(275)rs2297437、rs11221332、rs6478109;(276)rs2297437、rs41309367、rs6478109;(277)rs2070557、rs7278257、rs11221332;(278)rs2070557, rs7278257, rs41309367; (279)rs2070557, rs7278257, rs6478109; (280)rs207 0557, rs11221332, rs41309367; (281) rs2070557, rs11221332, rs6478109; (282) rs2070557, rs41 309367, rs6478109; (283) rs7278257, rs11221332, rs41309367; (284) rs7278257, rs11221332, rs6478109; (285) rs7278257, rs41309367, rs6478109; or (286) rs11221332, rs41309367, rs6478109. ;

[0221] 19. The genotype of embodiment 18, wherein the rs7278257 is replaced by rs56124762.

[0222] 20. The genotype of embodiment 18, wherein the rs7278257 is replaced by rs2070558.

[0223] 21. The genotype of embodiment 18, wherein the rs7278257 is replaced by rs2070561.

[0224] 22. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, imm_11_127948309, and rs1892231.

[0225] 23. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, imm_11_127948309, and rs9806914.

[0226] 24. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, imm_11_127948309, and imm_21_44478192.

[0227] 25. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, imm_11_127948309, and imm_21_44479552.

[0228] 26. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, rs1892231, and rs9806914.

[0229] 27. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, rs1892231, and imm_21_44478192.

[0230] 28. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, rs1892231, and imm_21_44479552.

[0231] 29. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, rs9806914, and imm_21_44478192.

[0232] 30. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, rs9806914, and imm_21_44479552.

[0233] 31. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_9_116608587, imm_21_44478192, and imm_21_44479552.

[0234] 32. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, rs1892231, and rs9806914.

[0235] 33. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, rs1892231, and imm_21_44478192.

[0236] 34. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, rs1892231, and imm_21_44479552.

[0237] 35. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, rs9806914, and imm_21_44478192.

[0238] 36. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, rs9806914, and imm_21_44479552.

[0239] 37. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from the group consisting of imm_11_127948309, imm_21_44478192, and imm_21_44479552.

[0240] 38. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, rs9806914, and imm_21_44478192.

[0241] 39. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, rs9806914, and imm_21_44479552.

[0242] 40. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs1892231, imm_21_44478192, and imm_21_44479552.

[0243] 41. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs9806914, imm_21_44478192, and imm_21_44479552.

[0244] 42. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs56124762, and rs1892231.

[0245] 43. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs56124762, and rs16901748.

[0246] 44. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231, and rs16901748.

[0247] 45. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs56124762, rs1892231, and rs16901748.

[0248] 46. ​​The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070558, and rs1892231.

[0249] 47. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070558, and rs16901748.

[0250] 48. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231, and rs16901748.

[0251] 49. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs2070558, rs1892231, and rs16901748.

[0252] 50. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070561, and rs1892231.

[0253] 51. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs2070561, and rs16901748.

[0254] 52. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs6478109, rs1892231, and rs16901748.

[0255] 53. The genotype of embodiments 5-6, wherein the genotype comprises at least two polymorphisms selected from rs2070561, rs1892231, and rs16901748.

[0256] 54. The genotype of embodiment 11, wherein the genotype comprises eight polymorphisms selected from any 8-SNP combination of Model_1 to Model_495 described in Table 25.

[0257] 55. The genotype of embodiments 1-53, wherein the genotype comprises the minor allele provided in Table 1 for at least one polymorphism.

[0258] 56. The genotype of embodiments 1-53, wherein the genotype comprises the major allele provided in Table 1 for at least one polymorphism.

[0259] 57. The genotype of embodiments 1-56, wherein the presence of the genotype can predict a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0260] 58. The genotype of embodiments 1-57, wherein the presence of the genotype can predict a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0261] 59. The genotype of embodiments 1-58, wherein the presence of the genotype can predict a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0262] 60. The genotype of embodiments 1-59, wherein the presence of the genotype can predict a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0263] 61. The genotype of embodiments 1-60, wherein the presence of the genotype can predict a positive therapeutic response of an IBD patient to treatment with an inhibitor of TL1A expression activity with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100%.

[0264] 62. The genotype of embodiments 1-61, wherein the presence of the genotype can predict a positive therapeutic response in an IBD patient to treatment with an inhibitor of TL1A expression activity with a positivity rate of at least about 10%, 15%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 55%, 60%, 65% or 70%.

[0265] Aspects disclosed herein provide genotypes that are associated with, and therefore indicate, a subject suffering from or susceptible to a particular disease or condition, or a subclinical phenotype thereof. Additionally, the genotypes disclosed herein are associated with increased expression or activity of TNFSF15 (TL1A). Thus, the genotype indicates that a subject will have a positive therapeutic response to an inhibitor of TL1A activity or expression. Table 1 provides exemplary polymorphisms that are associated with, and therefore predictive of a positive therapeutic response to, an inhibitor of TNFSF15 (TL1A) expression or activity. The term "positive therapeutic response" refers to a reduction or elimination of at least one symptom of a disease or condition (e.g., Crohn's disease) following induction of a treatment (e.g., an anti-TL1A antibody).

[0266] Table 1: Exemplary polymorphisms

[0267]

[0268]

[0269]

[0270] The present disclosure provides a model comprising three polymorphisms (e.g., a "3-SNP model") that, when detected in a sample obtained from a subject, indicates a positive therapeutic response of the subject to treatment, such as treatment with an inhibitor of TL1A activity or expression. Non-limiting examples of the models described herein include Model A (rs6478109, rs7278257, and rs1892231); Model B (rs6478109, rs2070557, and rs9806914); Model C (rs6478109, rs7935393, and rs1892231); Model D (rs6478109, rs7935393, and rs9806914); Model E (rs6478109, rs9806914, and rs16901748); Model F (rs64781 09, rs16901748, and rs2297437); model G (rs6478109, rs1892231, and rs16901748); model H (rs6478109, rs2070557, and rs7935393); model I (rs6478109, rs7278257, and rs7935393); model J (rs6478109, rs9806914, and rs1892231); and model K (rs6478109, rs7278257, and rs16901748).

[0271] The present disclosure provides models comprising 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more polymorphisms (e.g., a "1-SNP model," a "2-SNP model," or a "3-SNP model," etc.), which, when detected in a sample obtained from a subject, indicate a positive therapeutic response of the subject to treatment with an inhibitor of TL1A activity or expression. Non-limiting examples of the models described herein include the 10-SNP, 9-SNP, 8-SNP, 7-SNP, 6-SNP, 5-SNP, 4-SNP, 3-SNP, 2-SNP, or 1-SNP models described in Table 31.

[0272] The genotypes and / or polymorphisms provided herein in Table 1 have been validated as being associated with increased TL1A expression in inflammatory cells, increased inflammation, an IBD phenotype, an increase in IBD-enriched cell types, a decrease in IBD-depleted cell types, and / or an increased positive therapeutic response in IBD patients to treatment with inhibitors of TL1A activity or expression, as further described in Section 7 (Examples). Thus, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for identifying subjects or patients using the various methods provided herein, including Sections 2, 5.2, and 7 (Examples). Similarly, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for identifying subjects or patients using the various kits and compositions provided herein, including Sections 2, 5.5, 5.7, and 7 (Examples). Additionally, the present disclosure provides that the genotypes and / or polymorphisms, combinations of genotypes, and / or combinations of polymorphisms provided herein can be used as criteria for the various methods of selecting patients provided herein, including Section 2, Section 5.2, and Section 7.

[0273] The present disclosure also provides simple methods to verify the suitability of genotypes and / or polymorphisms, combinations of genotypes and / or combinations of polymorphisms for the various treatment methods, methods of selecting patients and / or kits and compositions provided herein (including Sections 2, 5.2, 5.5, 5.7 and 7).

[0274] The polymorphisms identified in the assays provided in the above examples can be used to predict a positive therapeutic response of a subject or patient to an inhibitor of TL1A activity or expression (e.g., an anti-TL1A antibody), either individually or in combination (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10 or more). The polymorphisms described herein can be used in diagnostic or prognostic tests to identify subjects suitable for treatment with an inhibitor of TL1A activity or expression to treat a disease or condition described herein in the subject. In some cases, the diagnostic is a companion diagnostic test, such as, for example, a TL1A companion diagnostic test ("TL1A CDx").

[0275] To validate the rules and polymorphisms, an external cohort of IBD (e.g., UC or CD) patients can be identified and genotyped. Positive predictive value, negative predictive value, specificity, sensitivity, and positivity rate can be calculated for the patient population based on the patient's genotype and the clinical response or clinical remission of the IBD patient after TL1A inhibitor therapy, as described, for example, in Section 7 (Examples). The present disclosure provides that because the polymorphisms provided herein have been selected by the machine learning algorithms provided herein (e.g., in the preceding paragraphs and in Section 7 (Examples)) as being associated with IBD readouts (e.g., TL1A expression, TL1A activity, IBD phenotype, increase in IBD-enriched cell types, decrease in IBD-depleted cell types, patient clinical response, and patient clinical remission, as described in Section 7 (Examples)), rules and polymorphism combinations for a given positive predictive value, negative predictive value, specificity, sensitivity, and / or positivity rate can be identified by the machine learning algorithms and validated as provided herein without undue experimentation.

[0276] In some embodiments of the methods provided herein, the genotype comprises a polymorphism. In certain embodiments of the methods provided herein, the polymorphism comprises a SNP. In some embodiments of the methods provided herein, the combination of genotypes comprises a combination of polymorphisms. In certain embodiments of the methods provided herein, the combination of polymorphisms comprises a combination of SNPs.

[0277] Tables 1 and 27 provide exemplary SNPs that predict clinical response or remission in patients treated with TL1A inhibitor therapy. The present disclosure provides that various combinations of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict increased TL1A expression, combinations of polymorphisms that predict increased TL1A transcriptome expression, or combinations of both polymorphisms that predict increased TL1A expression and polymorphisms that predict increased TL1A transcriptome expression. The present disclosure also provides that various combinations of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict clinical response or remission in patients treated with TL1A inhibitor therapy, as listed in Tables 1 and 27. The present disclosure further provides that various combinations of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include combinations of polymorphisms that predict clinical response or remission in patients treated with TL1A inhibitor therapy, as listed in Tables 1 and 27.

[0278] Therefore, in one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of two polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of three polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of four polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of five polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of six polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of seven polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 8 polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 9 polymorphisms that predict increased TL1A expression. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 10 polymorphisms that predict increased TL1A expression. In one embodiment, a polymorphism that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes 1 polymorphism that predicts increased TL1A expression. In some embodiments, the polymorphisms or combinations of polymorphisms that predict elevated TL1A expression for use in the various methods provided herein (including in this paragraph) are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the polymorphisms or combinations of polymorphisms that predict elevated TL1A expression for use in the various methods provided herein (including in this paragraph) are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0279] Alternatively, in one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of two polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of three polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of four polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of five polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of six polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of seven polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 8 polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 9 polymorphisms that predict increased TL1A activity. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 10 polymorphisms that predict increased TL1A activity. In one embodiment, the polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include 1 polymorphism that predicts increased TL1A activity. In some embodiments, the polymorphisms or combinations of polymorphisms provided herein (including in this paragraph) that predict increased TL1A activity for use in the various methods are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the polymorphisms or combinations of polymorphisms provided herein (including in this paragraph) that predict increased TL1A activity for use in the various methods are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0280] Additionally, in one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of two polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of three polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of four polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of five polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of six polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of seven polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 8 polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 9 polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 10 polymorphisms that predict an increase in IBD-enriched cell types. In one embodiment, the polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include 1 polymorphism that predicts an increase in IBD-enriched cell types. In some embodiments, the increased polymorphisms or combinations of polymorphisms for use in the various methods provided herein (including in this paragraph) that predict IBD-enriched cell types are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the increased polymorphisms or combinations of polymorphisms for use in the various methods provided herein (including in this paragraph) that predict IBD-enriched cell types are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0281] Furthermore, in one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of two polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of three polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of four polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of five polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of six polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, a combination of polymorphisms that predicts a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of seven polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 8 polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 9 polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the combination of polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression includes a combination of 10 polymorphisms that predict a reduction in IBD-depleted cell types. In one embodiment, the polymorphisms that predict a positive treatment response to an inhibitor of TL1A activity or expression include 1 polymorphism that predicts a reduction in IBD-depleted cell types. In some embodiments, the polymorphisms or combinations of polymorphisms provided herein (including in this paragraph) that predict a decrease in IBD-depleted cell types for use in the various methods are selected from Table 27 (SEQ ID NOs: 2004-2006, 2009, 2011, 2012, 2014-2016, 2019, 2024, 2026, 2028, 2032, 2039, and 2057). In some embodiments, the polymorphisms or combinations of polymorphisms provided herein (including in this paragraph) that predict a decrease in IBD-depleted cell types for use in the various methods are selected from Table 1 (SEQ ID NOs: 2001-2041 and 2057-2059).

[0282] In some embodiments, the increased TL1A expression, increased TL1A activity, increase in IBD-enriched cell types, and decrease in IBD-depleted cell types in this section (including in the preceding paragraphs) are relative to such indicators in a tissue or subject not affected by IBD (e.g., UC or CD).

[0283] 5.2 Treatment

[0284] Disclosed herein are methods for treating a disease or condition, or a symptom of a disease or condition, in a subject, comprising administering to the subject a therapeutically effective amount of one or more therapeutic agents. In some embodiments, the one or more therapeutic agents are administered alone to the subject (e.g., as a stand-alone therapy). In some embodiments, the one or more therapeutic agents are administered in combination with another agent. In some embodiments, the therapeutic agent is a first-line therapy for the disease or condition. In some embodiments, the therapeutic agent is a second-line, third-line, or fourth-line therapy for the disease or condition. In some embodiments, the therapeutic agent comprises an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression.

[0285] Various embodiments provide methods of treating inflammatory bowel disease (IBD) comprising administering an anti-TL1A antibody described herein to a subject in need thereof. In some embodiments, the subject comprises one or more risk genotypes. In some embodiments, the IBD is a severe form of IBD.

[0286] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0287] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0288] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0289] (i) obtaining or having obtained a sample from the subject;

[0290] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0291] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0292] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0293] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0294] (a) obtaining or having obtained a sample from the subject;

[0295] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0296] (c) Calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

[0297] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0298] (a) obtaining or having obtained a sample from the subject;

[0299] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0300] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0301] (d) If the PRI is above the cutoff value, the subject is selected for treatment with an inhibitor of TL1A activity or expression.

[0302] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a predictive response index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and the PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a positive correlation coefficient with a response probability score (RPS).

[0303] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0304] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0305] (i) obtaining or having obtained a sample from the subject;

[0306] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0307] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0308] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0309] The PRI and RPS have a positive correlation coefficient.

[0310] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0311] (a) obtaining or having obtained a sample from the subject;

[0312] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0313] (c) calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0314] The PRI and RPS have a positive correlation coefficient.

[0315] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0316] (a) obtaining or having obtained a sample from the subject;

[0317] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0318] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0319] (d) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0320] The PRI and RPS have a positive correlation coefficient.

[0321] In one aspect, provided herein is a method for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a predictive response index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and the PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%, wherein the PRI has a positive correlation coefficient with a model risk score (MRS).

[0322] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0323] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0324] (i) obtaining or having obtained a sample from the subject;

[0325] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0326] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0327] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0328] The PRI and MRS have a positive correlation coefficient.

[0329] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0330] (a) obtaining or having obtained a sample from the subject;

[0331] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0332] (c) calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0333] The PRI and MRS have a positive correlation coefficient.

[0334] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0335] (a) obtaining or having obtained a sample from the subject;

[0336] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0337] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0338] (d) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0339] The PRI and MRS have a positive correlation coefficient.

[0340] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0341] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0342] (i) obtaining or having obtained a sample from the subject;

[0343] (ii) preparing DNA from the sample;

[0344] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0345] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0346] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0347] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0348] (a) obtaining or having obtained a sample from the subject;

[0349] (b) preparing DNA from the sample;

[0350] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0351] (d) Calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

[0352] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0353] (a) obtaining or having obtained a sample from the subject;

[0354] (b) preparing DNA from the sample;

[0355] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0356] (d) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0357] (e) If the PRI is above the cutoff value, the subject is selected for treatment with an inhibitor of TL1A activity or expression.

[0358] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0359] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0360] (i) obtaining or having obtained a sample from the subject;

[0361] (ii) preparing DNA from the sample;

[0362] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0363] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0364] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0365] The PRI and RPS have a positive correlation coefficient.

[0366] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0367] (a) obtaining or having obtained a sample from the subject;

[0368] (b) preparing DNA from the sample;

[0369] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0370] (d) calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0371] The PRI and RPS have a positive correlation coefficient.

[0372] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0373] (a) obtaining or having obtained a sample from the subject;

[0374] (b) preparing DNA from the sample;

[0375] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0376] (d) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0377] (e) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0378] The PRI and RPS have a positive correlation coefficient.

[0379] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0380] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0381] (i) obtaining or having obtained a sample from the subject;

[0382] (ii) preparing DNA from the sample;

[0383] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0384] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0385] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0386] The PRI and MRS have a positive correlation coefficient.

[0387] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0388] (a) obtaining or having obtained a sample from the subject;

[0389] (b) preparing DNA from the sample;

[0390] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0391] (d) calculating a PRI from the combination of polymorphisms, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0392] The PRI and MRS have a positive correlation coefficient.

[0393] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0394] (a) obtaining or having obtained a sample from the subject;

[0395] (b) preparing DNA from the sample;

[0396] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0397] (d) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0398] (e) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0399] The PRI and MRS have a positive correlation coefficient.

[0400] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a predictive response index (PRI) below a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and the PRI below the cutoff value predicts a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a negative correlation coefficient with a response probability score (RPS).

[0401] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0402] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0403] (i) obtaining or having obtained a sample from the subject;

[0404] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0405] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0406] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0407] The PRI and RPS have a negative correlation coefficient.

[0408] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0409] (a) obtaining or having obtained a sample from the subject;

[0410] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0411] (c) calculating a PRI from the combination of polymorphisms, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0412] The PRI and RPS have a negative correlation coefficient.

[0413] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0414] (a) obtaining or having obtained a sample from the subject;

[0415] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0416] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0417] (d) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0418] The PRI and RPS have a negative correlation coefficient.

[0419] In one aspect, provided herein is a method for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a predictive response index (PRI) below a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and the PRI below the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%, wherein the PRI has a negative correlation coefficient with a model risk score (MRS).

[0420] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0421] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0422] (i) obtaining or having obtained a sample from the subject;

[0423] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0424] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0425] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0426] The PRI and MRS have a negative correlation coefficient.

[0427] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0428] (a) obtaining or having obtained a sample from the subject;

[0429] (b) performing an assay on the sample adapted to detect combinations of polymorphisms; and

[0430] (c) calculating a PRI from the combination of polymorphisms, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0431] The PRI and MRS have a negative correlation coefficient.

[0432] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0433] (a) obtaining or having obtained a sample from the subject;

[0434] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0435] (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0436] (d) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0437] The PRI and MRS have a negative correlation coefficient.

[0438] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0439] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0440] (i) obtaining or having obtained a sample from the subject;

[0441] (ii) preparing DNA from the sample;

[0442] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0443] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0444] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0445] The PRI and RPS have a negative correlation coefficient.

[0446] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0447] (a) obtaining or having obtained a sample from the subject;

[0448] (b) preparing DNA from the sample;

[0449] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0450] (d) calculating a PRI from the combination of polymorphisms, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0451] The PRI and RPS have a negative correlation coefficient.

[0452] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0453] (a) obtaining or having obtained a sample from the subject;

[0454] (b) preparing DNA from the sample;

[0455] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0456] (d) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0457] (e) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0458] The PRI and RPS have a negative correlation coefficient.

[0459] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0460] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0461] (i) obtaining or having obtained a sample from the subject;

[0462] (ii) preparing DNA from the sample;

[0463] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0464] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0465] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0466] The PRI and MRS have a negative correlation coefficient.

[0467] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0468] (a) obtaining or having obtained a sample from the subject;

[0469] (b) preparing DNA from the sample;

[0470] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0471] (d) calculating a PRI from the combination of polymorphisms, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0472] The PRI and MRS have a negative correlation coefficient.

[0473] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0474] (a) obtaining or having obtained a sample from the subject;

[0475] (b) preparing DNA from the sample;

[0476] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0477] (d) calculating a predicted response index (PRI) from the combination of polymorphisms; and

[0478] (e) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0479] The PRI and MRS have a negative correlation coefficient.

[0480] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0481] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0482] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0483] (i) obtaining or having obtained a sample from the subject;

[0484] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0485] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0486] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0487] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0488] (a) obtaining or having obtained a sample from the subject;

[0489] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0490] (c) Calculating a PRI from the genotype combination, where a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

[0491] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0492] (a) obtaining or having obtained a sample from the subject;

[0493] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0494] (c) calculating a predicted response index (PRI) from the genotype combination; and

[0495] (d) If the PRI is above the cutoff value, the subject is selected for treatment with an inhibitor of TL1A activity or expression.

[0496] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a Predicted Response Index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a positive correlation coefficient with a Response Probability Score (RPS).

[0497] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0498] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0499] (i) obtaining or having obtained a sample from the subject;

[0500] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0501] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0502] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0503] The PRI and RPS have a positive correlation coefficient.

[0504] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0505] (a) obtaining or having obtained a sample from the subject;

[0506] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0507] (c) calculating a PRI from the combination of genotypes, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0508] The PRI and RPS have a positive correlation coefficient.

[0509] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0510] (a) obtaining or having obtained a sample from the subject;

[0511] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0512] (c) calculating a predicted response index (PRI) from the genotype combination; and

[0513] (d) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0514] The PRI and RPS have a positive correlation coefficient.

[0515] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a predictive response index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and the PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%, wherein the PRI has a positive correlation coefficient with a model risk score (MRS).

[0516] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0517] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0518] (i) obtaining or having obtained a sample from the subject;

[0519] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0520] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0521] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0522] The PRI and MRS have a positive correlation coefficient.

[0523] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0524] (a) obtaining or having obtained a sample from the subject;

[0525] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0526] (c) calculating a PRI from the combination of genotypes, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0527] The PRI and MRS have a positive correlation coefficient.

[0528] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0529] (a) obtaining or having obtained a sample from the subject;

[0530] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0531] (c) calculating a predicted response index (PRI) from the genotype combination; and

[0532] (d) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0533] The PRI and MRS have a positive correlation coefficient.

[0534] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0535] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0536] (i) obtaining or having obtained a sample from the subject;

[0537] (ii) preparing DNA from the sample;

[0538] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0539] (iv) calculating a predicted response index (PRI) from the combination of genotypes, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0540] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0541] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0542] (a) obtaining or having obtained a sample from the subject;

[0543] (b) preparing DNA from the sample;

[0544] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0545] (d) Calculating a PRI from the genotype combination, where a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

[0546] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0547] (a) obtaining or having obtained a sample from the subject;

[0548] (b) preparing DNA from the sample;

[0549] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0550] (d) calculating a predicted response index (PRI) from the genotype combination; and

[0551] (e) If the PRI is above the cutoff value, the subject is selected for treatment with an inhibitor of TL1A activity or expression.

[0552] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0553] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0554] (i) obtaining or having obtained a sample from the subject;

[0555] (ii) preparing DNA from the sample;

[0556] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0557] (iv) calculating a predicted response index (PRI) from the combination of genotypes, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0558] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0559] The PRI and RPS have a positive correlation coefficient.

[0560] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0561] (a) obtaining or having obtained a sample from the subject;

[0562] (b) preparing DNA from the sample;

[0563] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0564] (d) calculating a PRI from the combination of genotypes, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0565] The PRI and RPS have a positive correlation coefficient.

[0566] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0567] (a) obtaining or having obtained a sample from the subject;

[0568] (b) preparing DNA from the sample;

[0569] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0570] (d) calculating a predicted response index (PRI) from the genotype combination; and

[0571] (e) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0572] The PRI and RPS have a positive correlation coefficient.

[0573] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0574] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0575] (i) obtaining or having obtained a sample from the subject;

[0576] (ii) preparing DNA from the sample;

[0577] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0578] (iv) calculating a predicted response index (PRI) from the combination of genotypes, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0579] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0580] The PRI and MRS have a positive correlation coefficient.

[0581] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0582] (a) obtaining or having obtained a sample from the subject;

[0583] (b) preparing DNA from the sample;

[0584] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0585] (d) calculating a PRI from the combination of genotypes, wherein a PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0586] The PRI and MRS have a positive correlation coefficient.

[0587] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0588] (a) obtaining or having obtained a sample from the subject;

[0589] (b) preparing DNA from the sample;

[0590] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0591] (d) calculating a predicted response index (PRI) from the genotype combination; and

[0592] (e) if the PRI is above the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0593] The PRI and MRS have a positive correlation coefficient.

[0594] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a Predicted Response Index (PRI) below a cutoff value, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and the PRI below the cutoff value predicts a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a negative correlation coefficient with a probability of response score (RPS).

[0595] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0596] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0597] (i) obtaining or having obtained a sample from the subject;

[0598] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0599] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0600] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0601] The PRI and RPS have a negative correlation coefficient.

[0602] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0603] (a) obtaining or having obtained a sample from the subject;

[0604] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0605] (c) calculating a PRI from the combination of genotypes, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0606] The PRI and RPS have a negative correlation coefficient.

[0607] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0608] (a) obtaining or having obtained a sample from the subject;

[0609] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0610] (c) calculating a predicted response index (PRI) from the genotype combination; and

[0611] (d) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0612] The PRI and RPS have a negative correlation coefficient.

[0613] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a predictive response index (PRI) below a cutoff value, wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and the PRI below the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%, wherein the PRI has a negative correlation coefficient with a model risk score (MRS).

[0614] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0615] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0616] (i) obtaining or having obtained a sample from the subject;

[0617] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0618] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0619] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0620] The PRI and MRS have a negative correlation coefficient.

[0621] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0622] (a) obtaining or having obtained a sample from the subject;

[0623] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0624] (c) calculating a PRI from the combination of genotypes, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0625] The PRI and MRS have a negative correlation coefficient.

[0626] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0627] (a) obtaining or having obtained a sample from the subject;

[0628] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0629] (c) calculating a predicted response index (PRI) from the genotype combination; and

[0630] (d) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0631] The PRI and MRS have a negative correlation coefficient.

[0632] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0633] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0634] (i) obtaining or having obtained a sample from the subject;

[0635] (ii) preparing DNA from the sample;

[0636] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0637] (iv) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0638] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0639] The PRI and RPS have a negative correlation coefficient.

[0640] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0641] (a) obtaining or having obtained a sample from the subject;

[0642] (b) preparing DNA from the sample;

[0643] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0644] (d) calculating a PRI from the combination of genotypes, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0645] The PRI and RPS have a negative correlation coefficient.

[0646] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0647] (a) obtaining or having obtained a sample from the subject;

[0648] (b) preparing DNA from the sample;

[0649] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0650] (d) calculating a predicted response index (PRI) from the genotype combination; and

[0651] (e) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0652] The PRI and RPS have a negative correlation coefficient.

[0653] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0654] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0655] (i) obtaining or having obtained a sample from the subject;

[0656] (ii) preparing DNA from the sample;

[0657] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0658] (iv) calculating a predicted response index (PRI) from the genotype combination, wherein if the PRI is below a cutoff value, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression; and

[0659] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression,

[0660] The PRI and MRS have a negative correlation coefficient.

[0661] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0662] (a) obtaining or having obtained a sample from the subject;

[0663] (b) preparing DNA from the sample;

[0664] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0665] (d) calculating a PRI from the combination of genotypes, wherein a PRI below a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression,

[0666] The PRI and MRS have a negative correlation coefficient.

[0667] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0668] (a) obtaining or having obtained a sample from the subject;

[0669] (b) preparing DNA from the sample;

[0670] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0671] (d) calculating a predicted response index (PRI) from the genotype combination; and

[0672] (e) if the PRI is below the cutoff value, selecting the subject for treatment with an inhibitor of TL1A activity or expression,

[0673] The PRI and MRS have a negative correlation coefficient.

[0674] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a comparison of a predicted response index (PRI) according to (1) or (2) to a cutoff value:

[0675] (1) If the PRI is positively correlated with the response probability score (RPS), select subjects when the PRI is above the cutoff value; or

[0676] (2) if PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value;

[0677] wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and comparison of the Predicted Response Index (PRI) according to (1) or (2) to a cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%.

[0678] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0679] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0680] (i) obtaining or having obtained a sample from the subject;

[0681] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0682] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0683] (1) if the PRI is positively correlated with the RPS, then the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0684] (2) if the PRI is negatively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0685] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0686] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0687] (a) obtaining or having obtained a sample from the subject;

[0688] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0689] (c) calculating a PRI from the genotype combination, wherein the comparison is determined according to (1) or (2):

[0690] (1) if the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression when the PRI is above the cutoff value; or

[0691] (2) If the PRI is negatively correlated with the RPS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0692] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0693] (a) obtaining or having obtained a sample from the subject;

[0694] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0695] (c) calculating the predicted response index (PRI) from the genotype combination; and

[0696] (d) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on comparison of the PRI according to (1) or (2) to a cutoff value:

[0697] (1) If PRI is positively correlated with RPS, select subjects when PRI is above the cutoff value; or

[0698] (2) If PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value.

[0699] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0700] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0701] (i) obtaining or having obtained a sample from the subject;

[0702] (ii) preparing DNA from the sample;

[0703] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0704] (iv) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0705] (1) if the PRI is positively correlated with the RPS, then the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0706] (2) if the PRI is negatively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0707] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0708] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0709] (a) obtaining or having obtained a sample from the subject;

[0710] (b) preparing DNA from the sample;

[0711] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0712] (d) calculating a PRI from the genotype combination, wherein the comparison is determined according to (1) or (2):

[0713] (1) if the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression when the PRI is above the cutoff value; or

[0714] (2) If the PRI is negatively correlated with the RPS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0715] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0716] (a) obtaining or having obtained a sample from the subject;

[0717] (b) preparing DNA from the sample;

[0718] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0719] (d) calculating the predicted response index (PRI) from the genotype combination; and

[0720] (e) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0721] (1) If PRI is positively correlated with RPS, select subjects when PRI is above the cutoff value; or

[0722] (2) If PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value.

[0723] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a comparison of a predicted response index (PRI) to a cutoff value according to (1) or (2) as follows:

[0724] (1) If the PRI is positively correlated with the model risk score (MRS), subjects are selected when the PRI is above the cutoff value; or

[0725] (2) if PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value;

[0726] wherein the PRI is calculated from a combination of genotypes determined from a sample from the subject, and comparison of the Predicted Response Index (PRI) according to (1) or (2) to a cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%.

[0727] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0728] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0729] (i) obtaining or having obtained a sample from the subject;

[0730] (ii) performing an assay on the sample adapted to detect combinations of genotypes;

[0731] (iii) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0732] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0733] (2) if the PRI is negatively correlated with the MRS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0734] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0735] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0736] (a) obtaining or having obtained a sample from the subject;

[0737] (b) performing an assay on the sample that is adapted to detect combinations of genotypes; and

[0738] (c) calculating a PRI from the genotype combination, wherein the comparison is determined according to (1) or (2):

[0739] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is above the cutoff value; or

[0740] (2) If the PRI is negatively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0741] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0742] (a) obtaining or having obtained a sample from the subject;

[0743] (b) performing an assay on the sample adapted to detect combinations of genotypes;

[0744] (c) calculating the predicted response index (PRI) from the genotype combination; and

[0745] (d) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) with a cutoff value of:

[0746] (1) If PRI is positively correlated with MRS, subjects are selected when PRI is above the cutoff value; or

[0747] (2) If PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value.

[0748] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0749] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0750] (i) obtaining or having obtained a sample from the subject;

[0751] (ii) preparing DNA from the sample;

[0752] (iii) subjecting the DNA to an assay adapted to detect combinations of genotypes;

[0753] (iv) calculating a predicted response index (PRI) from the genotype combination, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0754] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0755] (2) if the PRI is negatively correlated with the MRS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0756] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0757] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0758] (a) obtaining or having obtained a sample from the subject;

[0759] (b) preparing DNA from the sample;

[0760] (c) subjecting the DNA to an assay adapted to detect combinations of genotypes; and

[0761] (d) calculating a PRI from the genotype combination, wherein the comparison is determined according to (1) or (2):

[0762] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is above the cutoff value; or

[0763] (2) If the PRI is negatively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0764] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0765] (a) obtaining or having obtained a sample from the subject;

[0766] (b) preparing DNA from the sample;

[0767] (c) subjecting the DNA to an assay suitable for detecting combinations of genotypes;

[0768] (d) calculating the predicted response index (PRI) from the genotype combination; and

[0769] (e) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0770] (1) If PRI is positively correlated with MRS, subjects are selected when PRI is above the cutoff value; or

[0771] (2) If PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value.

[0772] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a comparison of a predicted response index (PRI) to a cutoff value according to (1) or (2) as follows:

[0773] (1) If the PRI is positively correlated with the response probability score (RPS), select subjects when the PRI is above the cutoff value; or

[0774] (2) if PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value;

[0775] wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and comparison of the Predicted Response Index (PRI) according to (1) or (2) to a cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%.

[0776] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0777] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0778] (i) obtaining or having obtained a sample from the subject;

[0779] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0780] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0781] (1) if the PRI is positively correlated with the RPS, then the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0782] (2) if the PRI is negatively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0783] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0784] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0785] (a) obtaining or having obtained a sample from the subject;

[0786] (b) performing an assay on the sample adapted to detect a combination of polymorphisms; and

[0787] (c) calculating a PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2):

[0788] (1) if the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression when the PRI is above the cutoff value; or

[0789] (2) If the PRI is negatively correlated with the RPS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0790] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0791] (a) obtaining or having obtained a sample from the subject;

[0792] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0793] (c) calculating the predicted response index (PRI) from the polymorphism combination; and

[0794] (d) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on comparison of the PRI according to (1) or (2) to a cutoff value:

[0795] (1) If PRI is positively correlated with RPS, select subjects when PRI is above the cutoff value; or

[0796] (2) If PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value.

[0797] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0798] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0799] (i) obtaining or having obtained a sample from the subject;

[0800] (ii) preparing DNA from the sample;

[0801] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0802] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0803] (1) if the PRI is positively correlated with the RPS, then the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0804] (2) if the PRI is negatively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0805] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0806] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0807] (a) obtaining or having obtained a sample from the subject;

[0808] (b) preparing DNA from the sample;

[0809] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0810] (d) calculating a PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2):

[0811] (1) if the PRI is positively correlated with the RPS, the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression when the PRI is above the cutoff value; or

[0812] (2) If the PRI is negatively correlated with the RPS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0813] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0814] (a) obtaining or having obtained a sample from the subject;

[0815] (b) preparing DNA from the sample;

[0816] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0817] (d) calculating a predicted response index (PRI) from the polymorphism combination; and

[0818] (e) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0819] (1) If PRI is positively correlated with RPS, select subjects when PRI is above the cutoff value; or

[0820] (2) If PRI was negatively correlated with RPS, subjects were selected when PRI was below the cutoff value.

[0821] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of tumor necrosis factor-like cytokine 1A (TL1A) activity or expression, wherein the subject is selected based on a comparison of a predicted response index (PRI) to a cutoff value according to (1) or (2) as follows:

[0822] (1) If the PRI is positively correlated with the model risk score (MRS), subjects are selected when the PRI is above the cutoff value; or

[0823] (2) if PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value;

[0824] wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and comparison of the Predicted Response Index (PRI) according to (1) or (2) to a cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50% or 51%.

[0825] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0826] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0827] (i) obtaining or having obtained a sample from the subject;

[0828] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0829] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0830] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0831] (2) if the PRI is negatively correlated with the MRS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0832] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0833] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0834] (a) obtaining or having obtained a sample from the subject;

[0835] (b) performing an assay on the sample adapted to detect a combination of polymorphisms; and

[0836] (c) calculating a PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2):

[0837] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is above the cutoff value; or

[0838] (2) If the PRI is negatively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0839] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0840] (a) obtaining or having obtained a sample from the subject;

[0841] (b) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0842] (c) calculating the predicted response index (PRI) from the polymorphism combination; and

[0843] (d) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on comparison of the PRI according to (1) or (2) to a cutoff value:

[0844] (1) If PRI is positively correlated with MRS, subjects are selected when PRI is above the cutoff value; or

[0845] (2) If PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value.

[0846] In another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0847] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0848] (i) obtaining or having obtained a sample from the subject;

[0849] (ii) preparing DNA from the sample;

[0850] (iii) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0851] (iv) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0852] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for the treatment if the PRI is above the cutoff value; or

[0853] (2) if the PRI is negatively correlated with the MRS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; and

[0854] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0855] In yet another aspect, provided herein is a method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising:

[0856] (a) obtaining or having obtained a sample from the subject;

[0857] (b) preparing DNA from the sample;

[0858] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms; and

[0859] (d) calculating a PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2):

[0860] (1) if the PRI is positively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is above the cutoff value; or

[0861] (2) If the PRI is negatively correlated with the MRS, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

[0862] In yet another aspect, provided herein is a method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising:

[0863] (a) obtaining or having obtained a sample from the subject;

[0864] (b) preparing DNA from the sample;

[0865] (c) subjecting the DNA to an assay adapted to detect combinations of polymorphisms;

[0866] (d) calculating a predicted response index (PRI) from the polymorphism combination; and

[0867] (e) selecting subjects for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) to a cutoff value:

[0868] (1) If PRI is positively correlated with MRS, subjects are selected when PRI is above the cutoff value; or

[0869] (2) If PRI was negatively correlated with MRS, subjects were selected when PRI was below the cutoff value.

[0870] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a genotype combination from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a Predicted Response Index (PRI) above a cutoff value, wherein the PRI is calculated from the genotype combination, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0871] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a combination of polymorphisms from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject is responsive to treatment with an inhibitor of TL1A activity or expression based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from the combination of polymorphisms and a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%.

[0872] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a genotype combination from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from the genotype combination, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a positive correlation coefficient with RPS, MRS, or both RPS and MRS.

[0873] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a combination of polymorphisms from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject is responsive to treatment with an inhibitor of TL1A activity or expression based on a predictive response index (PRI) above a cutoff value, wherein the PRI is calculated from the combination of polymorphisms and a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a positive correlation coefficient with RPS, MRS, or both RPS and MRS.

[0874] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a genotype combination from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the genotype combination to determine that the subject is responsive to treatment based on a Predicted Response Index (PRI) below a cutoff value, wherein the PRI is calculated from the genotype combination, and a PRI below the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a negative correlation coefficient with RPS, MRS, or both RPS and MRS.

[0875] In one aspect, provided herein is a computer-implemented method comprising: (a) receiving a combination of polymorphisms from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition; and (b) analyzing the combination of polymorphisms to determine that the subject is responsive to treatment with an inhibitor of TL1A activity or expression based on a predictive response index (PRI) below a cutoff value, wherein the PRI is calculated from the combination of polymorphisms and the PRI below the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 40%, 45%, 50%, or 51%, wherein the PRI has a negative correlation coefficient with RPS, MRS, or both RPS and MRS.

[0876] In one aspect, provided herein is a computer-implemented method for determining a response probability score (RPS) for a subject, the method comprising:

[0877] (a) receiving genotypic data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, wherein the genotypic data comprises a combination of polymorphisms;

[0878] (b) analyzing the genotype data with a first statistical algorithm configured to generate a model risk score (MRS) for the subject by performing operations comprising:

[0879] (i) assigning a weighted value to each polymorphism in the polymorphism combination to generate a plurality of weighted values; and

[0880] (ii) summing the plurality of weighted values;

[0881] (c) providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); and

[0882] (d) applying a cutoff value to the RPS, wherein the RPS relative to the cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression to treat an inflammatory, fibrotic, or fibrostenotic disease or condition.

[0883] In another aspect, provided herein is a computer-implemented method of determining a response probability score (RPS) for a subject, the method comprising:

[0884] (a) obtaining a plurality of multi-single nucleotide polymorphism (multi-SNP) models, wherein each multi-SNP model is predictive for a positive therapeutic response to a TL1A activity or expression inhibitor for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject;

[0885] (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from a subject;

[0886] (c) calculating a model risk score (MRS) using one or more statistical algorithms configured to perform operations comprising: (i) assigning a weighted numerical value to each of the plurality of polymorphisms to produce a plurality of weighted values; and (ii) summing the plurality of weighted values; and

[0887] (d) Logarithmic scaling and cutoff values ​​were applied to the MRS to generate a response probability score (RPS).

[0888] In yet another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression based at least in part on a predictive response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, and determining a comparison of the PRI to a cutoff value to predict a positive therapeutic response in the subject to treatment with the inhibitor of TL1A activity or expression.

[0889] In yet another aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering an inhibitor of TL1A activity or expression to a subject predicted to exhibit a positive treatment response to the inhibitor of TL1A activity or expression, as determined by a predicted response index (PRI) calculated as follows:

[0890] (a) detecting the presence of a polymorphism combination in a sample from a subject;

[0891] (b) applying a statistical algorithm to the combination of polymorphisms detected in step (a) to generate a PRI; and

[0892] (c) Determine the PRI compared with the cutoff value.

[0893] In one aspect, provided herein is a method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising:

[0894] (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps:

[0895] (i) obtaining or having obtained a sample from the subject;

[0896] (ii) performing an assay on the sample adapted to detect combinations of polymorphisms;

[0897] (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the PRI is further determined in comparison with a cutoff value; and

[0898] (b) treating the subject by administering to the subject a therapeutically effective amount of an inhibitor of TL1A activity or expression.

[0899] In various embodiments of the methods provided herein, including in Section 2, Section 5.2 (eg, preceding paragraph), and Section 7, the method further comprises preparing DNA from the sample.

[0900] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the comparison of the PRI to a cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the RPS, the subject's PRI is determined when the PRI is above the cutoff value; or (2) if the PRI is negatively correlated with the RPS, the subject's PRI is determined when the PRI is below the cutoff value.

[0901] The term "model risk score" or "MRS" is calculated as the weighted sum of the SNPs in the SNP combination model, or Wherein χi is the mathematical representation of the i-th SNP in the model, which can be any one of the mathematical representations described in Table 28. In some examples of methods provided herein, MRS can be used as the basis for patient binary classification as responder (CDx positive) or non-responder (CDx negative). The MRS cutoff value for determining this binary classification of the patient can be determined by training a model with treatment response data from a patient cohort so that the MRS cutoff value provides the highest accuracy for classifying responders, non-responders or responders and non-responders, as described elsewhere in the present disclosure, for example, in sections 7.20.4. Such MRS cutoff value can also be determined by other machine learning or computerized clustering methods known in the art and practiced and described elsewhere in the present disclosure (for example, in sections 7.20.4). Generally speaking, if MRS≥cutoff value, then prediction is "yes, responder" and CDx is positive, and if MRS<cutoff value, then prediction is "no, non-responder" and CDx is negative.

[0902] The term "probability of response score" or "RPS" refers to a score calculated via a mathematical function using a patient's genotype or combination of genotypes as variable inputs, wherein the genotype input for any SNP in the model can be a mathematical representation selected from the mathematical representations described in Table 28. Such an RPS can be used to indicate the probability that a patient will respond to treatment with a TL1A inhibitor, with a higher RPS indicating a higher probability that the patient will respond to treatment and a lower RPS indicating a lower probability that the patient will respond to treatment. In some examples of the methods provided herein, the RPS can be used as the basis for binary classification of patients as responders (CDx positive) or non-responders (CDx negative). The RPS cutoff value for determining this binary classification of patients can be determined by training the model with treatment response data from a patient cohort such that the RPS cutoff value provides the highest accuracy for classifying responders, non-responders, or both responders and non-responders, as described elsewhere in this disclosure, for example, in Section 7.20.4. Such RPS cutoff values ​​may also be determined by other machine learning or computerized clustering methods known and practiced in the art and as described elsewhere in this disclosure (e.g., in Section 7.20.4). In other examples, the mathematical function (e.g., the coefficients in the function) used to calculate the RPS from a patient's genotype or combination of genotypes can be trained and normalized so that the RPS ranges from 0 to 1 and is cutoff at 0.5 to optimally classify patients as responders (CDx positive) or non-responders (CDx negative) to TL1A inhibitor treatment. In one specific example, the RPS is calculated as described in this section and in Section 7.20.4. In another specific example, the RPS is calculated as RPS = 1 / (1 + e (-MRS) ), where (1) if RPS ≥ 0.5, the prediction is “yes, responder” and CDx is positive, and (2) if RPS < 0.5, the prediction is “no, non-responder” and CDx is negative. In general, if RPS ≥ cutoff value, the prediction is “yes, responder” and CDx is positive, and if RPS < cutoff value, the prediction is “no, non-responder” and CDx is negative.

[0903] The term "predictive response index" or "PRI" refers to a value calculated via a mathematical function using a patient's genotype or combination of genotypes as variable inputs that predicts (i) an increase in the level of TNFSF15 (TL1A) protein expression in a sample obtained from a subject or patient, as compared to a reference level of TNFSF15 (TL1A) protein expression (e.g., from a normal individual), (ii) an increase in IBD-enriched cell types, as compared to a reference level in tissue not affected by IBD, (iii) a decrease in IBD-depleted cell types, as compared to a reference level in tissue not affected by IBD, and / or (iv) an increase in a positive therapeutic response in an IBD patient to treatment with a TL1A inhibitor, as compared to a reference response level in patients not selected by genotype or model, wherein the genotype input for any SNP in the model can be a mathematical representation selected from the mathematical representations described in Table 28. Such a PRI can be an MRS as described in this section and in Section 7. Such a PRI can also be an RPS as described in this section and in Section 7. The PRI can also be a mathematical function that generates a score that is positively or negatively correlated with an MRS or RPS calculated from a subject's genotype. In some instances, the PRI is associated with the RPS with a correlation coefficient of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or at least about 0.99. In other instances, the PRI is associated with the RPS with a correlation coefficient of at most about -0.7, at most about -0.75, at most about -0.8, at most about -0.85, at most about -0.9, at most about -0.95, or at most about -0.99.

[0904] In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), a combination of beta coefficients and corresponding SNPs is used to calculate MRS, wherein the combination of beta coefficients and corresponding SNPs is selected from the combinations described in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), a combination of beta coefficients and corresponding SNPs is used to calculate MRS. To calculate MRS, wherein the combination of beta coefficient and corresponding SNP is selected from the combinations described in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), the combination of beta coefficient and corresponding SNP is selected from the combinations described in columns 1 and 2 of Table 31. To calculate the MRS, wherein the combination of the beta coefficient and the corresponding SNP is selected from the combinations described in columns 1 and 2 of Table 31, and wherein χi is the mathematical representation of the i-th SNP in the model, which can be any of the mathematical representations described in Table 28. As is apparent from the description of Table 31, each row in columns 1 and 2 of Table 31 describes a specific combination of a beta coefficient and a corresponding SNP (e.g., the beta coefficient in row 3 of column 1 corresponds to the SNP code in row 3 of column 2). Therefore, in some embodiments of the various methods provided herein (including in this paragraph, including in Section 2, Section 5.2, and Section 7), the MRS is calculated using a combination of a beta coefficient and a corresponding SNP, wherein the combination of the beta coefficient and the corresponding SNP can be described in any row of columns 1 and 2 of Table 31. More specifically, in some embodiments of the various methods provided herein (including in this paragraph), MRS is calculated using a combination of beta coefficients and corresponding SNPs, where the combination of beta coefficients and corresponding SNPs is described in row x of columns 1 and 2 of Table 31, and where x is any number between 2 and 1374.

[0905] In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), a combination of a beta coefficient and a corresponding SNP is used to calculate RPS, wherein the combination of a beta coefficient and a corresponding SNP is selected from the combinations described in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), a combination of a beta coefficient and a corresponding SNP is used to calculate RPS as RPS = 1 / (1+e (-MRS) ) to calculate RPS, where And wherein the combination of beta coefficient and corresponding SNP is selected from the combination described in columns 1 and 2 of Table 31. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2, and Section 7), the combination of beta coefficient and corresponding SNP is selected from the combination described in columns 1 and 2 of Table 31. (-MRS) ) to calculate RPS, where Wherein the combination of beta coefficients and corresponding SNPs is selected from the combinations described in columns 1 and 2 of Table 31, and wherein χi is the mathematical representation of the i-th SNP in the model, which can be any of the mathematical representations described in Table 28. As is apparent from the description of Table 31, each row in columns 1 and 2 of Table 31 describes a specific combination of beta coefficients and corresponding SNPs (e.g., the beta coefficients in row 3 of column 1 correspond to the combination of SNP codes in row 3 of column 2). Thus, in some embodiments of the various methods provided herein (including in this paragraph), the RPS is calculated using a combination of beta coefficients and corresponding SNPs, wherein the combination of beta coefficients and corresponding SNPs can be described in any row of columns 1 and 2 of Table 31. More specifically, in some embodiments of the various methods provided herein (including in this paragraph), the RPS is calculated using a combination of beta coefficients and corresponding SNPs, wherein the combination of beta coefficients and corresponding SNPs is described in row x of columns 1 and 2 of Table 31, and wherein x is any number between 2 and 1374.

[0906] In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.6. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.65. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.7. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.75. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.8. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.85. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.9. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of at least about 0.99.

[0907] In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.6. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.65. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.7. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.75. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.8. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.85. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of at most about -0.99.

[0908] In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.6. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.65. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.7. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.75. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.8. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.85. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.9. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with the RPS with a correlation coefficient of about 0.99.

[0909] In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.6. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.65. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.7. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.75. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.8. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.85. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with the RPS with a correlation coefficient of about -0.99.

[0910] In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.6. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.65. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.7. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.75. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.8. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.85. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.9. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of at least about 0.99.

[0911] In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.6. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.65. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.7. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.75. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.8. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.85. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of at most about -0.99.

[0912] In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.6. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.65. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.7. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.75. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.8. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.85. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.9. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.95. In some embodiments of the various methods provided herein, the PRI is positively correlated with MRS with a correlation coefficient of about 0.99.

[0913] In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.6. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.65. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.7. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.75. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.8. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.85. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.9. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.95. In some embodiments of the various methods provided herein, the PRI is negatively correlated with MRS with a correlation coefficient of about -0.99.

[0914] In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is MRS. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is RPS. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is equal to MRS. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is equal to RPS. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is equal to -1×MRS (with a correlation coefficient of -1 with MRS). In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the PRI is equal to -1×RPS (with a correlation coefficient of -1 with RPS).

[0915] In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the RPS ranges from 0 to 1. In some embodiments, the cutoff value for the RPS is 0.5.

[0916] In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the β0 used in the calculation of MRS, RPS, and / or PRI is about 0.0077127943934849. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the β0 used in the calculation of MRS, RPS, and / or PRI is 0.0077127943934849. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the β0 used in the calculation of MRS, RPS, and / or PRI is about 0.008. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the cutoff value for MRS is about 0.0322446725024791. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the cutoff value of MRS is 0.0322446725024791. In some embodiments of the various methods provided herein (including in Sections 2, 5.2, and 7), the cutoff value of MRS is about 0.03.

[0917] In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the polymorphisms in the model are represented mathematically using χi, which is as follows to calculate the PRI, RPS, or MRS: (i) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 2 for the homozygous alternative allele (additive digital coding); (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele (reference subtractive digital coding); (iii) 0 for the homozygous reference allele; 1, for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele (alternative subtractive digital coding); (iv) 0 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele (alternative recessive digital coding); (v) 1 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele (reference recessive digital coding); and / or (vi) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele (haploinsufficient digital coding). In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the genotypes in the model are represented mathematically by χi, which are as follows to calculate the PRI, RPS, or MRS: (i) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 2 for the homozygous alternative allele; (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; (iii) 0 for for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele; (iv) 0 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 1 for the homozygous alternative allele; (v) 1 for the homozygous reference allele; 0 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; and / or (vi) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele.

[0918] In certain embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each polymorphism is represented by χi, which is as follows to calculate the PRI, RPS, or MRS: (i) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 2 for the homozygous alternative allele; (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; (iii) 0 for the homozygous 28. In some embodiments, the polymorphisms are represented by any of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each polymorphism can be represented by any three of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each polymorphism can be represented by any four of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each polymorphism can be represented by any five of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each polymorphism can be represented by all six of the six numerical codes described in Table 28. Additionally, for the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any of the six numerical codes described in Table 28.In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any two of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any three of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any four of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any five of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by all six of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all of the polymorphisms can be represented by any one, two, three, four, five, or six of the six numerical codes described in Table 28. Thus, combinations of polymorphisms can be represented by any combination of the six numerical codes described in Table 28. For example, a 3-SNP CDx model can have each of the three SNPs represented by one code selected from Table 28; each of the three SNPs represented by two codes selected from Table 28; each of the three SNPs represented by three codes selected from Table 28; each of the three SNPs represented by four codes selected from Table 28; each of the three SNPs represented by five codes selected from Table 28; each of the three SNPs represented by six codes selected from Table 28; one of the three SNPs represented by one code selected from Table 28 and two of the three SNPs represented by two codes selected from Table 28; etc. Table 31 describes some specific embodiments of CDx models based on SNP combinations and the representation of the SNPs in the models.

[0919] In certain embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each genotype is represented by χi, which is as follows to calculate the PRI, RPS, or MRS: (i) 0 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 2 for the homozygous alternative allele; (ii) 1 for the homozygous reference allele; 1 for the heterozygous reference and alternative alleles; and 0 for the homozygous alternative allele; (iii) 0 for the homozygous 28. In some embodiments of the present invention, each genotype can be represented by any of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each genotype can be represented by any three of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each genotype can be represented by any four of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each genotype can be represented by any five of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), each genotype can be represented by all six of the six numerical codes described in Table 28. Additionally, for the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all genotypes can be represented by any of the six numerical codes described in Table 28.In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all genotypes can be represented by any two of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all genotypes can be represented by any three of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all genotypes can be represented by any four of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more or all genotypes can be represented by any five of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more, or all, genotypes can be represented by all six of the six numerical codes described in Table 28. In some embodiments of the various methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), any one or more, or all, genotypes can be represented by any one, two, three, four, five, or six of the six numerical codes described in Table 28. Thus, combinations of genotypes can be represented by any combination of the six numerical codes described in Table 28. For example, a 3-SNP CDx model can have each of the three SNPs represented by one code selected from Table 28; each of the three SNPs represented by two codes selected from Table 28; each of the three SNPs represented by three codes selected from Table 28; each of the three SNPs represented by four codes selected from Table 28; each of the three SNPs represented by five codes selected from Table 28; each of the three SNPs represented by six codes selected from Table 28; one of the three SNPs represented by one code selected from Table 28 and two of the three SNPs represented by two codes selected from Table 28; etc. Table 31 describes some specific embodiments of CDx models based on SNP combinations and the representation of the SNPs in the models.

[0920] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., preceding paragraphs) and Section 7), the methods include explaining genotypes, combinations of genotypes, polymorphisms, or combinations of polymorphisms by calculating PRI, MRS, or RPS. In some embodiments, the interpretation step is performed by interpretation software. In certain embodiments, the interpretation software is as described in Sections 5.8.1, 5.8.2, 5.8.3, 5.8.4, 1.1.1, and 5.8.5. In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., preceding paragraphs) and Section 7), the methods include explaining genotypes, combinations of genotypes, polymorphisms, or combinations of polymorphisms with interpretation software. In certain embodiments, the step of interpreting a genotype, a combination of genotypes, a polymorphism, or a combination of polymorphisms is any embodiment of such an interpretation step as described in Section 5.8.1, Section 5.8.2, Section 5.8.3, Section 5.8.4, Section 1.1.1, and Section 5.8.5. In some embodiments, the step of interpreting a genotype, a combination of genotypes, a polymorphism, or a combination of polymorphisms is performed using software as described in Section 5.8.1, Section 5.8.2, Section 5.8.3, Section 5.8.4, Section 1.1.1, and Section 5.8.5. In some embodiments, the step of interpreting a genotype, a combination of genotypes, a polymorphism, or a combination of polymorphisms is performed in a system as described in Section 5.8.1, Section 5.8.2, Section 5.8.3, Section 5.8.4, Section 1.1.1, and Section 5.8.5. In some embodiments, the step of interpreting a genotype, a combination of genotypes, a polymorphism, or a combination of polymorphisms is performed using software as described in Sections 5.8.1, 5.8.2, 5.8.3, 5.8.4, 1.1.1, and 5.8.5 and in a system as described in Sections 5.8.1, 5.8.2, 5.8.3, 5.8.4, 1.1.1, and 5.8.5.

[0921] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., preceding paragraphs), and Section 7), the polymorphism combination comprises a genotype combination. In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., preceding paragraphs), and Section 7), the polymorphism combination consists of a genotype combination.

[0922] In various embodiments of the method provided herein (including in Section 2, Section 5.2 (e.g., preceding paragraph) and Section 7), the polymorphism combination comprises 2 kinds of polymorphisms or is composed of 2 kinds of polymorphisms. In some embodiments, the polymorphism combination comprises 3 kinds of polymorphisms or is composed of 3 kinds of polymorphisms. In certain embodiments, the polymorphism combination comprises 4 kinds of polymorphisms or is composed of 4 kinds of polymorphisms. In some other embodiments, the polymorphism combination comprises 5 kinds of polymorphisms or is composed of 5 kinds of polymorphisms. In some other embodiments, the polymorphism combination comprises 6 kinds of polymorphisms or is composed of 6 kinds of polymorphisms. In some embodiments, the polymorphism combination comprises 7 kinds of polymorphisms or is composed of 7 kinds of polymorphisms. In certain embodiments, the polymorphism combination comprises 8 kinds of polymorphisms or is composed of 8 kinds of polymorphisms. In some other embodiments, the polymorphism combination comprises 9 kinds of polymorphisms or is composed of 9 kinds of polymorphisms. In some other embodiments, the polymorphism combination comprises 10 kinds of polymorphisms or is composed of 10 kinds of polymorphisms. In the various embodiments of the method that this paper (being included in the 2nd, 5.2nd (for example, preceding paragraph) and the 7th) provides, the polymorphism combination comprises at least 2 kinds of polymorphisms or is made up of at least 2 kinds of polymorphisms.In some embodiments, the polymorphism combination comprises at least 3 kinds of polymorphisms or is made up of at least 3 kinds of polymorphisms.In certain embodiments, the polymorphism combination comprises at least 4 kinds of polymorphisms or is made up of at least 4 kinds of polymorphisms.In some other embodiments, the polymorphism combination comprises at least 5 kinds of polymorphisms or is made up of at least 5 kinds of polymorphisms.In some other embodiments, the polymorphism combination comprises at least 6 kinds of polymorphisms or is made up of at least 6 kinds of polymorphisms.In some embodiments, the polymorphism combination comprises at least 7 kinds of polymorphisms or is made up of at least 7 kinds of polymorphisms.In certain embodiments, the polymorphism combination comprises at least 8 kinds of polymorphisms or is made up of at least 8 kinds of polymorphisms.In some other embodiments, the polymorphism combination comprises at least 9 kinds of polymorphisms or is made up of at least 9 kinds of polymorphisms. In some additional embodiments, the combination of polymorphisms comprises or consists of at least 10 polymorphisms.

[0923] In various embodiments of the methods provided herein, including in Section 2, Section 5.2 (eg, preceding paragraph), and Section 7, the method further comprises preparing DNA from a sample obtained from the subject.

[0924] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the methods further comprise contacting the sample with one or more probes adapted to detect the presence of a combination of genotypes or a combination of polymorphisms. In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the methods further comprise contacting the sample with an assay adapted to detect the presence of a combination of genotypes or a combination of polymorphisms.

[0925] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the methods further comprise processing the sample to enrich for genetic material. In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), the methods further comprise processing the sample to enrich for genetic material targeted by the assay or the one or more probes.

[0926] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), a PRI above the cutoff value predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least 25%. Other embodiments described in this paragraph are provided for various methods described herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7). In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least 26%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least 27%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least 28%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response in a subject to treatment with an inhibitor of TL1A activity or expression with a positive predictive value of at least 29%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 30%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 31%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 32%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 33%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 34%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 35%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 40%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 45%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 50%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 51%.In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 55%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 60%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 65%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 70%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 75%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 80%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 85%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 90%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of at least 95%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of approximately 25%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of approximately 26%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of approximately 27%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of approximately 28%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of approximately 29%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 30%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 31%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 32%.In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 33%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 34%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 35%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 40%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 45%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 50%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 51%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 55%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 60%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 65%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 70%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 75%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 80%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 85%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 90%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a positive predictive value of about 95%.In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response of a subject to treatment with an inhibitor of TL1A activity or expression and the positive predictive value is 25% to 95%, 30% to 95%, 35% to 95%, 40% to 95%, 45% to 95%, 50% to 95%, 55% to 95%, 60% to 95%, 65% to 95%, 70% to 95%, 75% to 95%, 80% to 95%, 85% to 95%, 90% to 95%, 25% to 90%, 30% to 90%, 35% to 90%, 40% to 90%, 45% to 90%, 50% to 90%, 55% to 90%, 60% to 95%, 65% to 95%, 70% to 95%, 75% to 95%, 80% to 95%, 85% to 95%, 90% to 95%, 25% to 90%, 30% to 90%, 35% to 90%, 40% to 90%, 45% to 90%, 50% to 90%, 55% to 90%, 60% to 95%, 65% to 95%, % to 90%, 65% to 90%, 70% to 90%, 75% to 90%, 80% to 90%, 85% to 90%, 25% to 85%, 30% to 85%, 35% to 85%, 40% to 85%, 45% to 85%, 50% to 85%, 55% to 85%, 60% to 85%, 65% to 85%, 70% to 85%, 75% to 85%, 80% to 85%, 25% to 80%, 30% to 80%, 35% to 80%, 40% to 80%, 45% to 80%, 50% to 80%, 55% to 80%, 60% to 80%, 65% to 80%, 70% to 80%, 75 % to 80%, 25% to 75%, 30% to 75%, 35% to 75%, 40% to 75%, 45% to 75%, 50% to 75%, 55% to 75%, 60% to 75%, 65% to 75%, 70% to 75%, 25% to 70%, 30% to 70%, 35% to 70%, 40% to 70%, 45% to 70%, 50% to 70%, 55% to 70%, 60% to 70%, 65% to 70%, 25% to 65%, 30% to 65%, 35% to 65%, 40% to 65%, 45% to 65%, 50% to 65%, 55% to 65%, 60% to 65%, 2 % to 40%, 30% to 40%, 35% to 40%, 40% to 45%, 25% to 30%, 30% to 40%, 35% to 40%, 25% to 35%, 30% to 35%, or 25% to 30%. In some embodiments of the methods provided herein, including in this paragraph, the PRI is an MRS. In certain embodiments of the methods provided herein, including in this paragraph, the PRI is RPS.In addition, for each embodiment described in this paragraph, there is also provided a corresponding embodiment in which the statement "a PRI above the cutoff value is predictive of" is replaced by the statement "the cutoff value is such that the comparison of the PRI to the cutoff value is predictive of..." In some further embodiments of the preceding clause, the comparison of the PRI to the cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the RPS, the comparison of the PRI to the cutoff value is based on a PRI above the cutoff value; or (2) if the PRI is negatively correlated with the RPS, the comparison of the PRI to the cutoff value is based on a PRI below the cutoff value. Similarly, in some further embodiments, the comparison of the PRI to the cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the MRS, the comparison of the PRI to the cutoff value is based on a PRI above the cutoff value; or (2) if the PRI is negatively correlated with the MRS, the comparison of the PRI to the cutoff value is based on a PRI below the cutoff value.

[0927] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 40%. Other embodiments described in this paragraph are provided for various methods described herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7). In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 45%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 50%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 55%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 60%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 65%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 70%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 75%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 80%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 85%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 90%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of at least 95%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 40%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 45%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 50%.In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 55%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 60%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 65%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 70%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 75%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 80%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 85%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 90%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of about 95%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a negative predictive value of 50% to 95%, 55% to 95%, 60% to 95%, 65% to 95%, 70% to 95%, 75% to 95%, 80% to 95%, 85% to 95%, 90% to 95%, 50% to 90%, 55% to 90%, 60% to 90%, 65% to 90%, 70% to 90%, 75% to 90%, 80% to 90%, 85% to 90%, 50% to 85%, 55% to 85 ...5%, 65% to 90%, 70% to 90%, 75% to 90%, 80% to 90%, 85% to 90%, 50% to 85%, In some embodiments of the methods provided herein, including in this paragraph, the PRI is an MRS.In certain embodiments of the methods provided herein (including in this paragraph), the PRI is RPS. In addition, for each embodiment described in this paragraph, there is also provided a corresponding embodiment in which the statement "a PRI above the cutoff value is predictive of" is replaced by the statement "the cutoff value is such that the comparison of the PRI to the cutoff value is predictive of..." In some further embodiments of the preceding clause, the comparison of the PRI to the cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the RPS, the comparison of the PRI to the cutoff value is based on a PRI above the cutoff value; or (2) if the PRI is negatively correlated with the RPS, the comparison of the PRI to the cutoff value is based on a PRI below the cutoff value. Similarly, in some further embodiments, the comparison of the PRI to the cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with the MRS, the comparison of the PRI to the cutoff value is based on a PRI above the cutoff value; or (2) if the PRI is negatively correlated with the MRS, the comparison of the PRI to the cutoff value is based on a PRI below the cutoff value.

[0928] In various embodiments of the methods provided herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7), a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least 20%. Other embodiments described in this paragraph are provided for various methods described herein (including in Section 2, Section 5.2 (e.g., the preceding paragraph), and Section 7). In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least 25%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least 30%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least 35%. In some embodiments, a PRI above the cutoff value predicts a positive therapeutic response to treatment with an inhibitor of TL1A activity or expression in a subject with a sensitivity of at least 40%. In some embodiments, a PRI above the cutoff value predicts a positive therape...

Claims

1. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: A therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor is administered to the subject, wherein the subject is selected based on a Predictive Response Index (PRI) above a cutoff value, wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and a PRI above the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 29%.

2. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps: (i) obtaining or having obtained a sample from the subject; (ii) performing an assay on the sample adapted to detect a combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein if the PRI is above a cutoff value, the subject is determined to be suitable for treatment with a TL1A activity or expression inhibitor; as well as (b) treating the subject by administering to the subject a therapeutically effective amount of the inhibitor of TL1A activity or expression.

3. A method of determining a Predicted Response Index (PRI) in a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising: (a) obtaining or having obtained a sample from the subject; (b) performing an assay on the sample adapted to detect a combination of polymorphisms; and (c) calculating the PRI from the combination of polymorphisms, wherein the PRI above a cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression.

4. A method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising: (a) obtaining or having obtained a sample from the subject; (b) performing an assay on the sample adapted to detect a combination of polymorphisms; (c) calculating a predicted response index (PRI) from the combination of polymorphisms; and (d) if the PRI is above a cutoff value, selecting the subject for treatment with the TL1A activity or expression inhibitor.

5. The method according to any one of claims 2 to 4, wherein the method further comprises preparing DNA from the sample.

6. The method according to any one of claims 1 to 5, wherein the PRI is a response probability score (RPS).

7. The method according to any one of claims 1 to 6, wherein the PRI has a positive correlation coefficient with the RPS. The method according to claim 7 , wherein the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

9. The method of claim 7 or 8, wherein the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1.

10. A computer-implemented method for determining a response probability score (RPS) for a subject, the method comprising: (a) receiving genotypic data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, wherein the genotypic data comprises a combination of polymorphisms; (b) analyzing the genotype data with a first statistical algorithm configured to generate a model risk score (MRS) for the subject by performing operations comprising: (i) assigning a weighted value to each polymorphism in the polymorphism combination to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; (c) providing the MRS to a second statistical algorithm configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); and (d) applying a cutoff value to the RPS, wherein an RPS relative to the cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression to treat the inflammatory, fibrotic or fibrostenotic disease or condition.

11. A computer-implemented method for determining a response probability score (RPS) for a subject, the method comprising: (a) obtaining a plurality of multi-single nucleotide polymorphism (multi-SNP) models, wherein each multi-SNP model is predictive for a positive therapeutic response to a TL1A activity or expression inhibitor for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in the subject; (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from the subject; (c) calculating a model risk score (MRS) using one or more statistical algorithms configured to perform operations comprising: (i) assigning a weighted numerical value to each of the plurality of polymorphisms to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; as well as (d) Logarithmic scaling and cutoff values ​​were applied to the MRS to generate a response probability score (RPS).

12. The method according to any one of claims 6 to 11, wherein the RPS is in the range of 0 to 1.

13. The method according to any one of claims 6 to 12, wherein the cut-off value is 0.

5.

14. The method according to any one of claims 6 to 13, wherein the RPS is calculated as 1 / (1+e (-MRS) ), wherein the MRS is calculated as And where χ i is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and β i is the weight of the i-th SNP in the model.

15. The method according to any one of claims 1 to 5, wherein the PRI is a Model Risk Score (MRS).

16. The method according to any one of claims 1 to 5 and 15, wherein the PRI has a positive correlation coefficient with MRS.

17. The method according to claims 15 and 16, wherein the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

18. The method of claims 16 and 17, wherein the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1.

19. The method according to any one of claims 10 to 16, wherein the MRS is calculated as And where χ i is the mathematical representation of the i-th SNP in the model.

20. The method according to claim 10 or 19, wherein the SNPs in the model are represented by χ i Mathematically, it is: (i) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele; (ii) 1, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; (iii) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (iv) 0, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (v) 1, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; and / or (vi) 0, for homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele.

21. according to the method described in any one of claims 1 to 20, wherein said polymorphism combination comprises one or more polymorphisms selected from Table 27, or as with an R of at least 0.

85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

22. The method of any one of claims 1 to 21, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

23. The method of any one of claims 1 to 22, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

24. The method of any one of claims 1 to 23, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

25. The method of any one of claims 1 to 24, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

26. The method of any one of claims 1 to 25, wherein the cutoff value is such that a PRI above the cutoff value is predictive of a positive therapeutic response to treatment with the inhibitor of TL1A activity or expression in the subject at a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

27. The method of any one of claims 1 to 26, wherein the cutoff value is such that a PRI above the cutoff value is predictive of a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

28. The method of any one of claims 1 to 27, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

29. The method of any one of claims 1 to 28, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

30. The method of any one of claims 1 to 29, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

31. The method of any one of claims 1 to 30, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

32. The method of any one of claims 1 to 31, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

33. The method of any one of claims 1 to 31, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

34. The method of any one of claims 1 to 33, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

35. The method of any one of claims 1 to 34, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

36. The method of any one of claims 1 to 35, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

37. The method of any one of claims 1 to 36, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

38. The method of any one of claims 1 to 37, wherein the cutoff value is such that a PRI above the cutoff value is predictive for an increase in one or more IBD-enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

39. The method of any one of claims 1 to 38, wherein the cutoff value is such that a PRI above the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

40. The method of any one of claims 28 to 39, wherein the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from activated fibroblasts, monocyte-derived dendritic cells (moDCs), and CD36+ endothelial cells, intestinal epithelial cells and colon epithelial cells, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

41. The method of any one of claims 29 to 40, wherein the one or more IBD-depleted cell types comprise 1 or 2 cell types selected from tuft cells and BEST4+ epithelial cells.

42. The method of any one of claims 1 to 41, wherein the polymorphism combination comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen polymorphisms.

43. The method of any one of claims 1 to 42, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

44. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: administering to the subject a therapeutically effective amount of a tumor necrosis factor-like cytokine 1A (TL1A) activity or expression inhibitor, wherein the subject is selected based on a comparison of a predicted response index (PRI) according to (1) or (2) to a cutoff value: (1) if the PRI is positively correlated with a response probability score (RPS), selecting the subject when the PRI is above the cutoff value; or (2) if the PRI is negatively correlated with RPS, the subject is selected when the PRI is below the cutoff value; and wherein the PRI is calculated from a combination of polymorphisms determined from a sample from the subject, and comparison of the predicted response index (PRI) according to (1) or (2) with the cutoff value predicts a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 29%.

45. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: (a) determining whether a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by the following steps: (i) obtaining or having obtained a sample from the subject; (ii) performing an assay on the sample adapted to detect a combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the subject is determined to be suitable for treatment with an inhibitor of TL1A activity or expression based on a comparison of the PRI according to (1) or (2) with a cutoff value: (1) if the PRI is positively correlated with the RPS, determining that the subject is suitable for the treatment when the PRI is above the cutoff value; or (2) if the PRI is negatively correlated with RPS, determining that the subject is suitable for the treatment when the PRI is below the cutoff value; as well as (b) treating the subject by administering to the subject a therapeutically effective amount of the inhibitor of TL1A activity or expression.

46. ​​A method of determining a Predicted Response Index (PRI) compared to a cutoff value for a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, the method comprising: (a) obtaining or having obtained a sample from the subject; (b) performing an assay on the sample adapted to detect a combination of polymorphisms; as well as (c) calculating the PRI from the combination of polymorphisms, wherein the comparison is determined according to (1) or (2): (1) if the PRI is positively correlated with RPS, determining that the subject is suitable for treatment with a TL1A activity or expression inhibitor when the PRI is above the cutoff value; or (2) If the PRI is negatively correlated with RPS, determining that the subject is suitable for treatment with a TL1A activity or expression inhibitor when the PRI is below the cutoff value.

47. A method of selecting a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition for treatment with an inhibitor of TL1A activity or expression, the method comprising: (a) obtaining or having obtained a sample from the subject; (b) performing an assay on the sample adapted to detect a combination of polymorphisms; (c) calculating a predicted response index (PRI) from the combination of polymorphisms; as well as (d) selecting a subject for treatment with the TL1A activity or expression inhibitor based on a comparison of the PRI according to (1) or (2) with a cutoff value: (1) if the PRI is positively correlated with RPS, selecting the subject when the PRI is above the cutoff value; or (2) If the PRI is negatively correlated with RPS, the subject is selected when the PRI is below the cutoff value.

48. The method of any one of claims 45 to 47, wherein the method further comprises preparing DNA from the sample.

49. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: A positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject is predicted based at least in part on a predicted response index (PRI) calculated by applying one or more statistical algorithms to a combination of polymorphisms detected from a sample obtained from the subject, administering to the subject a therapeutically effective amount of a TL1A activity or expression inhibitor, and determining a comparison of the PRI to a cutoff value.

50. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: The TL1A activity or expression inhibitor is administered to a subject predicted to show a positive treatment response to the TL1A activity or expression inhibitor as determined by a predicted response index (PRI) calculated as follows: (a) detecting the presence of a polymorphism combination in a sample from the subject; (b) applying a statistical algorithm to the polymorphism combinations detected in step (a) to generate the PRI; and (c) Determining the PRI compared to a cutoff value.

51. A method of treating an inflammatory, fibrotic, or fibrostenotic disease or condition in a subject, the method comprising: (a) determining whether the subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression by: (i) obtaining or having obtained a sample from the subject; (ii) performing an assay on the sample adapted to detect a combination of polymorphisms; (iii) calculating a predicted response index (PRI) from the combination of polymorphisms, wherein the PRI is further determined in comparison to a cutoff value; and (b) treating the subject by administering to the subject a therapeutically effective amount of the inhibitor of TL1A activity or expression.

52. The method of any one of claims 50 to 51 , wherein the method further comprises preparing DNA from the sample.

53. The method of any one of claims 49 to 52, wherein the comparison of the PRI to a cutoff value is determined according to (1) or (2): (1) if the PRI is positively correlated with RPS, determining the PRI for the subject when the PRI is above the cutoff value; or (2) If the PRI is negatively correlated with RPS, determining the PRI of the subject when the PRI is below the cutoff value.

54. The method according to any one of claims 44 to 48 and 53, wherein the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

55. The method of any one of claims 44 to 48 and 53 to 54, wherein if the PRI is positively correlated with RPS, the positive correlation coefficient is at least about 0.6, at least about 0.65, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.95, at least about 0.99, or 1, or wherein if the PRI is negatively correlated with RPS, the negative correlation coefficient is at most about -0.6, at most about -0.65, at most about -0.7, at most about -0.75, at most about -0.8, at most about -0.85, at most about -0.95, at most about -0.99, or -1.

56. The method of any one of claims 44 to 48 and 53 to 55, wherein the RPS is in the range of 0 to 1.

57. The method of any one of claims 44 to 48 and 53 to 56, wherein if the PRI is positively correlated with RPS, the cutoff value is 0.5, or wherein if the PRI is negatively correlated with RPS, the cutoff value is -0.

5.

58. The method of any one of claims 44 to 48 and 53 to 57, wherein the RPS is calculated as 1 / (1+e (-MRS) ), wherein the MRS is calculated as And where χ i is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and β i is the weight of the i-th SNP in the model.

59. The method of claim 58, wherein the SNPs in the model are represented by χ i Mathematically, it is: (i) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele; (ii) 1, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; (iii) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (iv) 0, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (v) 1, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; and / or (vi) 0, for homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele.

60. according to the method described in any one of claims 44 to 59, wherein said polymorphism combination comprises one or more polymorphisms selected from Table 27, or as with an R of at least 0.

85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

61. The method of any one of claims 44 to 48 and 53 to 60, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

62. The method of any one of claims 44 to 48 and 53 to 61, wherein the cutoff value is such that comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

63. The method of any one of claims 44 to 48 and 53 to 62, wherein the cutoff value is such that comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

64. The method of any one of claims 44 to 48 and 53 to 63, wherein the cutoff value is such that comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

65. The method of any one of claims 44 to 48 and 53 to 64, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject at a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

66. The method of any one of claims 44 to 48 and 53 to 65, wherein the cutoff value is such that comparison of the PRI according to (1) or (2) to the cutoff value is predictive of a positive therapeutic response to treatment with the TL1A activity or expression inhibitor in the subject with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

67. The method of any one of claims 44 to 48 and 53 to 66, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

68. The method of any one of claims 44 to 48 and 53 to 67, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a positive predictive value of at least about 29%, 30%, 35%, 40%, 45%, 50%, 51%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

69. The method of any one of claims 44 to 48 and 53 to 68, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

70. The method of any one of claims 44 to 48 and 53 to 69, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a specificity of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

71. The method of any one of claims 44 to 48 and 53 to 70, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

72. The method of any one of claims 44 to 48 and 53 to 71, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a negative predictive value of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

73. The method of any one of claims 44 to 48 and 53 to 72, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

74. The method of any one of claims 44 to 48 and 53 to 73, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a sensitivity of at least about 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

75. The method of any one of claims 44 to 48 and 53 to 74, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

76. The method of any one of claims 44 to 48 and 53 to 75, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with a positivity rate of at least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70% or 75%.

77. The method of any one of claims 44 to 48 and 53 to 76, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for an increase in one or more IBD-enriched cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

78. The method of any one of claims 44 to 48 and 53 to 77, wherein the cutoff value is such that a comparison of the PRI according to (1) or (2) to the cutoff value is predictive for a reduction in one or more IBD-depleted cell types with an accuracy of at least about 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90% or 95%.

79. The method of any one of claims 67 to 78, wherein the one or more IBD-enriched cell types comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, or 14 cell types selected from activated fibroblasts, monocyte-derived dendritic cells (moDC), and CD36+ endothelial cells, intestinal epithelial cells and colon epithelial cells, EECs, goblet cells, IgG plasma cells, Paneth cells, resident macrophages, TA cells, highly activated T cells, lymphoepithelial cells, microfold cells, and myofibroblasts.

80. The method of any one of claims 68 to 79, wherein the one or more IBD-depleted cell types comprise 1 or 2 cell types selected from tuft cells and BEST4+ epithelial cells.

81. according to the method described in any one of claims 44 to 80, wherein the polymorphism combination comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen or at least sixteen polymorphisms.

82. The method of any one of claims 44 to 81, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

83. The method according to any one of claims 1 to 82, wherein (i) the PRI is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; (ii) the MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; and / or (iii) The RPS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31.

84. The method of any one of claims 1 to 83, wherein the polymorphism combination in the sample is detected by performing an assay on the sample configured to detect the presence of at least three nucleotides corresponding to nucleic acid position 501 within at least three of SEQ ID NOs: 2001-2048 and 2057-2059.

85. A computer-implemented system comprising at least one processor and instructions executable by the at least one processor to provide an application configured to determine a response probability score (RPS) for a subject by performing operations comprising: (a) receiving genotypic data obtained from a sample from a subject having an inflammatory, fibrotic, or fibrostenotic disease or condition, wherein the genotypic data comprises a combination of polymorphisms; (b) applying a first statistical algorithm to the genotype data, the first statistical algorithm configured to generate a model risk score (MRS) for the subject by performing operations comprising: (i) assigning a weighted value to each polymorphism in the combination of polymorphisms to generate a plurality of weighted values; and (ii) summing the plurality of weighted values; (c) applying a second statistical algorithm to the MRS, the second statistical algorithm being configured to perform a logarithmic function on the MRS to generate a response probability score (RPS); and (d) applying a cutoff value to the RPS, wherein the RPS relative to the cutoff value indicates that the subject is suitable for treatment with an inhibitor of TL1A activity or expression to treat the inflammatory, fibrotic or fibrostenotic disease or condition.

86. A computer-implemented system comprising at least one processor and instructions executable by the at least one processor to provide an application configured to determine a response probability score (RPS) for a subject by performing operations comprising: (a) receiving a plurality of multi-single nucleotide polymorphism (multi-SNP) models, wherein each multi-SNP model is predictive for a positive therapeutic response to a TL1A activity or expression inhibitor for treating an inflammatory, fibrotic, or fibrostenotic disease or condition in the subject; (b) receiving genotype data for a plurality of polymorphisms obtained from a sample from the subject; (c) calculating a model risk score (MRS) using one or more statistical algorithms configured to perform operations comprising: (i) assigning a weighted numerical value to each of the plurality of polymorphisms to produce a plurality of weighted values; and (ii) summing the plurality of weighted values; and (d) A logarithmic scale and cutoff values ​​were applied to the MRS to generate a response probability score (RPS).

87. The computer-implemented system of any one of claims 85 to 86, wherein the RPS ranges from 0 to 1.

88. The computer-implemented system of any one of claims 85 to 87, wherein the cutoff value is 0.

5.

89. The computer-implemented system of claim 85, wherein the genotype data is a combination of single nucleotide polymorphisms (SNPs).

90. The computer-implemented system of any one of claims 85 to 89, wherein the RPS is calculated as 1 / (1+e (-MRS) ), wherein the MRS is calculated as And where χ i is the mathematical representation of the i-th single nucleotide polymorphism (SNP) in the model, and β i is the weight of the i-th SNP in the model.

91. The computer-implemented system of any one of claims 85 to 90, wherein the MRS is calculated as And where χ i is the mathematical representation of the i-th SNP in the model.

92. The computer-implemented system of any one of claims 86 to 91, wherein the SNPs in the model are represented by χ i Mathematically, it is: (i) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 2, for the homozygous alternative allele; (ii) 1, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; (iii) 0, for the homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (iv) 0, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 1, for the homozygous alternative allele; (v) 1, for the homozygous reference allele; 0, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele; and / or (vi) 0, for homozygous reference allele; 1, for heterozygous reference and alternative alleles; and 0, for the homozygous alternative allele.

93. The computer-implemented system of any one of claims 86 to 92, wherein the polymorphism combination comprises one or more polymorphisms selected from Table 27, or as described herein with an R of at least 0.

85. 2 The identified proxy polymorphisms are in linkage disequilibrium therewith, or a combination thereof.

94. The computer-implemented system of any one of claims 86 to 93, wherein the polymorphism combination comprises at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen, at least fifteen, or at least sixteen polymorphisms.

95. The computer-implemented system of any one of claims 86 to 94, wherein the PRI is calculated from a 1-SNP model selected from the 1-SNP model of Table 5, a 2-SNP combination selected from the 2-SNP model of Table 5, a 3-SNP combination selected from the 3-SNP model of Table 5, a 4-SNP combination selected from the 4-SNP model of Table 5, a 5-SNP combination selected from the 5-SNP model of Table 5, a 6-SNP combination selected from the 6-SNP model of Table 5, a 7-SNP combination selected from the 7-SNP model of Table 5, or an 8-SNP combination selected from the 8-SNP model of Table 5.

96. A computer-implemented system according to any one of claims 86 to 95, wherein (i) the MRS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31; and / or (ii) The RPS is calculated using a combination of polymorphisms selected from the combinations listed in column 2 of Table 31 and the corresponding beta coefficients listed in column 1 of Table 31.

97. A computer-implemented system according to any one of claims 86 to 96, wherein the polymorphism combination in the sample is detected by performing an assay on the sample configured to detect the presence of at least three nucleotides corresponding to nucleic acid position 501 within at least three of SEQ ID NOs: 2001-2048 and 2057-2059.

98. The method of any one of claims 1 to 84 or the computer-implemented system of any one of claims 85 to 97, wherein the subject has been treated with advanced IBD therapy prior to treatment with the inhibitor of TL1A activity or expression.

99. The method of any one of claims 1 to 84 or the computer-implemented system of any one of claims 85 to 97, wherein the subject has not been treated with advanced IBD therapy prior to treatment with the inhibitor of TL1A activity or expression.

100. The method or system of claim 98 or 99, wherein the late-stage IBD therapy comprises one or more selected from a biologic therapeutic for IBD, an S1P1 modulator, or a JAK inhibitor.

101. The method or system of claim 100, wherein the biotherapeutic for IBD comprises an anti-TNFα antibody, an anti-IL23 antibody, or an anti-integrin α4β7 antibody.

102. The method of any one of claims 1 to 84 and 98 to 101 or the computer-implemented system of any one of claims 85 to 101, wherein the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A (anti-TL1A antibody or antigen-binding fragment), wherein the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable region comprising: (a) a HCDR1 comprising the amino acid sequence of SEQ ID NO: 1; (b) a HCDR2 comprising the amino acid sequence of any one of SEQ ID NOs: 2-5; and (c) a HCDR3 comprising the amino acid sequence of any one of SEQ ID NOs: 6-9; and a light chain variable region comprising: (d) a LCDR1 comprising the amino acid sequence of SEQ ID NO: 10; (e) a LCDR2 comprising the amino acid sequence of SEQ ID NO: 11; and (f) a LCDR3 comprising the amino acid sequence of any one of SEQ ID NOs: 12-15.

103. The method of any one of claims 1 to 84 and 98 to 102 or the computer-implemented system of any one of claims 85 to 102, wherein the inhibitor of TL1A activity or expression is an anti-TL1A antibody or antigen-binding fragment, wherein the anti-TL1A antibody or antigen-binding fragment comprises a heavy chain variable domain comprising an amino acid sequence at least about 90% identical to any one of SEQ ID NOs: 101-135 or 310-302 and a light chain variable domain comprising an amino acid sequence at least about 90% identical to any one of SEQ ID NOs: 201-206 or 303.

104. The method or system of claim 102 or 103, wherein the heavy chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 101-135 or 310-302.

105. The method or system of any one of claims 102 to 104, wherein the light chain variable domain comprises an amino acid sequence that is at least about 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 201-206 or 303.

106. The method of any one of claims 1 to 84 and 98 to 105 or the computer-implemented system of any one of claims 85 to 105, wherein the TL1A activity or expression inhibitor is an anti-TL1A antibody or antigen-binding fragment, wherein the anti-TL1A antibody or antigen-binding fragment comprises: (a) a heavy chain variable framework region comprising a human IGHV1-46*02 framework or a modified human IGHV1-46*02 framework; and (b) a light chain variable framework region comprising a human IGKV3-20 framework or a modified human IGKV3-20 framework; wherein the heavy chain variable framework region and the light chain variable framework region together comprise fewer than about 14 amino acid modifications compared to the human IGHV1-46*02 framework and the human IGKV3-20 framework.

107. The method or system of claim 106, wherein the amino acid modifications in the fewer than 14 amino acid modifications comprise: (a) the amino acid modification is at position 47 in the heavy chain variable region, and the amino acid at position 47 is R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (b) the amino acid modification is at position 45 in the heavy chain variable region, and the amino acid at position 45 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (c) the amino acid modification is at position 47 in the heavy chain variable region, and the amino acid at position 47 is R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V. (d) the amino acid modification is at position 78 in the heavy chain variable region, and the amino acid at position 78 is A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (e) the amino acid modification is at position 55 in the heavy chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y or V; (f) the amino acid modification is at position 82 in the heavy chain variable region, and the amino acid at position 82 is A, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V; (g) the amino acid modification is at position 80 in the heavy chain variable region, and the amino acid at position 80 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y or V. (a) to (h) the amino acid modification is at position 91 in the heavy chain variable region, and the amino acid at position 91 is A, R, N, D, C, Q, E, G, H, I, L, K, F, P, S, T, W, Y or V; or a combination of two or more modifications selected from (a) to (h).

108. The method or system of claim 107, wherein the amino acid modifications in the fewer than 14 amino acid modifications comprise: A47R, R45K, M55I, V78A, M80I, R82T, V89A, and M91L in the heavy chain variable region are numbered according to Aho or Kabat.

109. The method or system of claim 107, wherein the amino acid modifications in the fewer than 14 amino acid modifications comprise: (a) modification at amino acid position 54 in the light chain variable region; and / or (b) modification at amino acid position 55 in the light chain variable region; according to Aho or Kabat numbering.

110. The method or system of claim 106, wherein the amino acid modifications in the fewer than 14 amino acid modifications comprise: (a) the amino acid modification is at position 54 in the light chain variable region, and the amino acid at position 54 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y or V; and / or (b) the amino acid modification is at position 55 in the light chain variable region, and the amino acid at position 55 is A, R, N, D, C, Q, E, G, H, I, K, M, F, P, S, T, W, Y or V.

111. The method or system of claim 110, wherein the amino acid modifications in the fewer than 14 amino acid modifications include L54P and / or L55W in the light chain variable region, according to Aho or Kabat numbering.

112. The method of any one of claims 1 to 84 and 98 to 101 or the computer-implemented system of any one of claims 85 to 101, wherein the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A and comprises: a heavy chain variable region comprising SEQ ID NO: 301 X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLE WX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYCAR[HCDR3]WGQGTTVTVSS, and A light chain variable region comprising SEQ ID NO: 303EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FGGGT KLEIK, wherein each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V.

113. The method of any one of claims 1 to 84 and 98 to 101 or the computer-implemented system of any one of claims 85 to 101, wherein the inhibitor of TL1A activity or expression is an antibody or antigen-binding fragment thereof that binds to TL1A and comprises: a heavy chain variable region comprising SEQ ID NO: 302X1VQLVQSGAEVKKPGASVKVSCKAS[HCDR1]WVX2QX3PGQGLE WX4G[HCDR2]RX5TX6TX7DTSTSTX8YX9ELSSLRSEDTAVYYC[HCDR3]WGQGTTVTVSS, and A light chain variable region comprising SEQ ID NO: 303EIVLTQSPGTLSLSPGERATLSC[LCDR1]WYQQKPGQAPRX10X11IY[LCDR2]GIPDRFSGSGSGTDFTLTISRLEPEDFAVYYC[LCDR3]FGGGT KLEIK, wherein each of X1-X11 is independently selected from A, R, N, D, C, Q, E, G, H, I, L, K, M, F, P, S, T, W, Y or V.

114. The method or system of any one of claims 112 to 113, wherein: (a) X1 is Q or E; (b) X2 is R or K; (c) X3 is A or R; (d) X4 is M or I; (e) X5 is V or A; (f) X6 is M or I; (g) X7 is R or T; (h) X8 is V or A; (i) X9 is M or L; (j) X10 is L or P; (k) X11 is L or W; or (l) X1-X11 are any combination of (A) to (K).

115. The method or system of any one of claims 112 to 114, wherein the antibody or antigen-binding fragment comprises: a heavy chain CDR1 as shown in SEQ ID NO: 1; a heavy chain CDR2 as shown in any one of SEQ ID NOs: 2-5; a heavy chain CDR3 as shown in any one of SEQ ID NOs: 6-9; a light chain CDR1 as shown in SEQ ID NO: 10; a light chain CDR2 as shown in SEQ ID NO: 11; and a light chain CDR3 as shown in any one of SEQ ID NOs: 12-15.

116. The method or system of any one of claims 112 to 114, wherein the antibody or antigen-binding fragment comprises: a heavy chain framework (FR) 1 as shown in SEQ ID NO: 304; a heavy chain FR2 as shown in SEQ ID NO: 305 or SEQ ID NO: 313; a heavy chain FR3 as shown in any one of SEQ ID NOs: 306, 307, 314 or 315; a heavy chain FR4 as shown in SEQ ID NO: 308; a light chain FR1 as shown in SEQ ID NO: 309; a light chain FR2 as shown in SEQ ID NO: 310; a light chain FR3 as shown in SEQ ID NO: 311; or a light chain FR4 as shown in SEQ ID NO: 312; or a combination thereof.

117. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises human IgG1 The Fc region of human IgG1 comprises (a) 297A, 297Q, 297G or 297D, (b) 279F, 279K or 279L, (c) 228P, (d) 235A, 235E, 235G, 235Q, 235R or 235S, (e) 237A, 237E, 237K, 237N or 237R, (f) 234A, 234V or 234F, (g) 233P, (h) 328A, (i) 327Q or 327T, (j) 329A, 329G, 329Y or 329R, (k) 331S, (l) 236F or 236R, (m) 238A, 238 E, 238G, 238H, 238I, 238V, 238W or 238Y, (n) 248A, (o) 254D, 254E, 254G, 254H, 254I, 254N, 254P, 254Q, 254T or 254V, (p) 255N, (q) 256H, 256K, 256R or 256V, (r) 264S, (s) 265H, 265K, 265S, 265Y or 265A, (t) 267G, 267H, 267I or 267K, (u) 268K, (v) 269N or 269Q, (w) 270A, 270G, 270M or 270N, ( 376E, 376W or 376Y, (ll) 380D, (mm) 382D or 382P, (nn) 385P, (oo) 424H, 424M or 424V, (pp)434I, (qq)438G, (rr)439E, 439H, or 439Q, (ss)440A, 440D, 440E, 440F, 440M, 440T, or 440V, (tt)E233P, (uu)L235E, (vv)L234A and L235A, (ww)L234A, L235A, and G237A, (xx)L234A, L235A, and P329G, (yy)L234F, L235E, and P331S, (zz)L234A, L235E, and G237A, (aaa)L234A, L235E, G237A, and P331S,(bbb) L234A, L235A, G237A, P238S, H268A, A330S and P331S (IgG1σ), (ccc) L234A, L235A and P329A, (ddd) G236R and L328R, (eee) G237A, (fff) F241A, (ggg) V264A, (hhh) D265A, (iii) D265A and N297A, (jjj) D265A and N297G, (kkk) D270A, (lll) A330L, (mmm) P331A or P331S, or (nnn) any combination of two or more selected from (a)–(uu), according to Kabat numbering.

118. The method of any one of claims 1 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises a human IgG4 Fc region.

119. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region comprising a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 320-362.

120. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises a crystallizable fragment (Fc) region having reduced antibody-dependent cell-mediated cytotoxicity (ADCC) function compared to human IgG1 and / or reduced complement-dependent cytotoxicity (CDC) compared to human IgG1.

121. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises human IgG1 comprising SEQ ID NO:

320.

122. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the ADCC function of the Fc region with reduced ADCC is reduced by at least about 50% compared to human IgG1.

123. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the CDC function of the Fc region with reduced CDC is reduced by at least about 50% compared to human IgG1.

124. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises (i) a human IgG4 Fc region or (ii) a human IgG4 Fc region comprising (a) S228P, (b) S228P and L235E, or (c) S228P, F234A, and L235A, according to Kabat numbering.

125. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises a human IgG2 Fc region; an IgG2-IgG4 cross-subclass Fc region; an IgG2-IgG3 cross-subclass Fc region; an IgG2 comprising H268Q, V309L, A330S, P331S (IgG2m4); or an IgG2 comprising V234A, G237A, P238S, H268A, V309L, A330S, P331S (IgG2σ).

126. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises a region selected from the group consisting of 329A, 329G, 329Y, 331S, 236F, 236R, 238A, 238E, 238G, 238H, 238I, 238V, 238W, 238Y, 248A, 254D, 255D, 40F, 440M, 440T, and 440V are substituted human IgG1s numbered according to Kabat.

127. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises a sequence that is at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% identical to any one of SEQ ID NOs: 320-362.

128. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises an Fc region and wherein the Fc comprises any one of SEQ ID NOs: 401-413, or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to any one of SEQ ID NOs: 401-413.

129. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises a heavy chain comprising any one of SEQ ID NOs: 501-513 or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to any one of SEQ ID NOs: 501-513.

130. The method of any one of claims 1 to 84 and 98 to 116 or the computer-implemented system of any one of claims 85 to 116, wherein the antibody or antigen-binding fragment comprises a light chain comprising any one of SEQ ID NO: 514 or a sequence at least about 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99% identical to any one of SEQ ID NO:

514.

131. The method of any one of claims 1 to 84 and 98 to 130 or the computer-implemented system of any one of claims 85 to 130, wherein the polymorphism combination in the sample is detected by performing an assay on the sample configured to detect the presence of a nucleotide combination corresponding to nucleic acid position 501 within a sequence combination selected from SEQ ID NOs: 2001-2041 and 2057-2059.

132. The method of any one of claims 1 to 84 and 98 to 131 or the computer-implemented system of any one of claims 85 to 131, wherein the inflammatory, fibrotic, or fibrostenotic disease or condition comprises inflammatory bowel disease, Crohn's disease, obstructive Crohn's disease, ulcerative colitis, intestinal fibrosis, intestinal fibrostenosis, rheumatoid arthritis, or primary sclerosing cholangitis.

133. The method or system of claim 132, wherein the Crohn's disease is ileal Crohn's disease, ileocolic Crohn's disease, or colonic Crohn's disease.

134. The method of any one of claims 1 to 84 and 98 to 133 or the computer-implemented system of any one of claims 85 to 133, wherein the subject is unresponsive or has lost response to, or is at risk of being unresponsive or losing response to, standard therapy comprising glucocorticoids, anti-TNF therapy, anti-a4-b7 therapy, anti-IL12p40 therapy, or a combination thereof.

135. The method of any one of claims 1 to 84 and 98 to 134 or the computer-implemented system of any one of claims 85 to 134, further comprising determining whether the subject having an inflammatory, fibrotic, or fibrostenotic disease or condition is suitable for treatment with an inhibitor of TL1A activity or expression based at least in part on at least three polymorphisms detected in the sample.

136. The method or system of claim 135, wherein the at least three polymorphisms are detected by utilizing an assay comprising quantitative polymerase chain reaction (qPCR), a nucleic acid sequencing reaction, or a genotyping array.

137. The method of any one of claims 1 to 84 and 98 to 136 or the computer-implemented system of any one of claims 85 to 136, wherein the polymorphism combination comprises or consists of: Any combination of the polymorphisms described in Table 31, column 2, row x, where x is any number between 2 and 1374.

138. The method of any one of claims 1 to 84 and 98 to 136 or the computer-implemented system of any one of claims 85 to 136, wherein the polymorphism combination comprises or consists of: Any combination of the polymorphisms described in Table 31, column 2, row x, wherein x is any number between 2 and 1374, wherein the polymorphisms in the polymorphism combination have beta coefficients described in Table 31, column 1, row x, and wherein the polymorphisms in the polymorphism combination are digitally coded as described in Table 1, column 2, row x.

139. The method of any one of claims 1 to 84 and 98 to 138, further comprising providing the sample to determine the PRI of claims 1 to 9, 12 to 84 and 98 to 138, the MRS of claims 10 to 43, 58 to 84 and 98 to 138, or the RPS of claims 6 to 49, 53 to 84 and 98 to 138.

140. The method of any one of claims 1 to 3, 5 to 46, 48 to 84, and 98 to 139, further comprising selecting the subject according to a PRI in claims 1 to 3, 5 to 9, 12 to 84, and 98 to 138.

141. The method of claim 140, wherein the PRI is RPS or MRS.

142. The method of any one of claims 1 to 84 and 98 to 141, further comprising: contacting the genetic material in the sample with one or more nucleic acid primer pairs having forward and reverse primers capable of hybridizing to one or more target nucleic acid sequences, wherein the one or more target nucleic acid sequences collectively constitute the chromosomal location of the polymorphism in column 2, row x of Table 31, where x is any number between 2 and 1374; amplifying the target nucleic acid sequence by polymerase chain reaction using the nucleic acid primer pair from the contacting step; inputting the results from the amplifying step into a computer system; as well as The results are analyzed via the computer system to determine the PRI in claims 1 to 9, 12 to 84, and 98 to 138, the MRS in claims 10 to 43, 58 to 84, and 98 to 138, or the RPS in claims 6 to 49, 53 to 84, and 98 to 138, wherein the computer system includes a storage unit configured to store the parameter in column 1, row y of Table 31, wherein y is the same as the x in the contacting step.

143. The method of any one of claims 14, 19-43, 58-84, and 98-142, wherein (i) the β0 is about 0.0077127943934849 or (ii) the β0 is about 0.

008.

144. The method of any one of claims 14, 19-43, 58-84, and 98-143, wherein (i) the cutoff value of the MRS is about 0.0322446725024791 or (ii) the cutoff value of the MRS is about 0.03.

Images