Primer composition for IgA nephropathy risk assessment, detection kit, polygene scoring model and risk prediction method

Abstract

The invention provides a primer composition for IgA nephropathy risk assessment, a detection kit, a polygene scoring model and a risk prediction method. According to the invention, 47 IgA nephropathy genetic susceptibility sites are systematically identified for the first time, 17 sites are new sites reported for the first time globally, and the 47 IgA nephropathy genetic susceptibility sites are utilized to construct a polygene scoring model for predicting the attack and progress risk of the IgA nephropathy. The model fully integrates the high-specificity SNP loci found in the invention, can be used for quantitatively evaluating the individual disease risk, predicting the disease progress trend and guiding clinical layered follow-up visit and individualized intervention, and has remarkable originality, scientificity and application value.

Description

Technical Field

[0001] This invention belongs to the field of biomedical and gene detection technology, specifically relating to a primer composition, detection kit, multi-gene scoring model, and risk prediction method for IgA nephropathy risk assessment. Background Technology

[0002] IgA nephropathy (IgAN) is the most common primary glomerular disease worldwide, with a particularly high incidence in Asian populations. It is one of the leading causes of end-stage kidney disease (ESKD). Its clinical phenotype varies significantly, manifesting as isolated microscopic hematuria, mild proteinuria, or rapid progression to renal failure. Epidemiological data shows that 50% of IgA nephropathy patients develop renal failure within 12 years of onset. Therefore, early identification of individuals at high genetic risk for IgA nephropathy in the general population and distinguishing high-progression-risk individuals among diagnosed patients are crucial for achieving precise prevention and individualized intervention.

[0003] The etiology of IgA nephropathy is complex, regulated by both genetic and environmental factors. Previous genome-wide association studies (GWAS) have identified more than 30 loci associated with susceptibility to IgA nephropathy, providing important clues to reveal its genetic basis. However, existing loci can only explain part of the heritability, and their clinical predictive ability is limited.

[0004] IgA nephropathy is a complex polygenic inherited disease, with individual risk determined by multiple genes and their interactions. The predictive power of a single SNP is limited; therefore, weighted integration of multiple risk loci to establish a polygenic risk score (PRS) model can more accurately quantify an individual's genetic susceptibility. Currently, only IgA nephropathy PRS models have been reported for European and American populations, and their predictive efficacy in Asian populations is low. Summary of the Invention

[0005] In view of this, in order to overcome the shortcomings of the prior art, the present invention is proposed.

[0006] The first aspect of this invention provides a set of SNP loci combinations, wherein the SNP loci combinations include one or more of the following SNP loci, the reference genome for the SNP loci is GRCh37, and the location information of the SNP loci includes rs2477134, rs530225, rs12754452, rs118170123, rs11799853, rs1801274, rs4916312, rs6677604, rs1177206, rs3769684, rs7607437, rs1057258, rs73065118, rs7634389, rs6832151, rs6828610, rs60307531, rs6942325, rs12530084, and rs926. 8557, rs9272105, rs9275355, rs9275596, rs3128927, rs114394181, rs3798258, rs3 093018, rs2615771, rs75413466, rs548902, rs1347321, rs77134091, rs13300483, rs 4077515, rs10821944, rs1108618, rs7127924, rs10896045, rs2187461, rs2273393, rs1806882, rs11150612, rs1879210, rs3803800, rs4561508, rs1865097, rs4823074.

[0007] In some implementations, the information for each SNP site is shown in the table below: .

[0008] A second aspect of the present invention provides a primer or probe comprising primers or probes for amplifying or detecting all or part of the SNP sites described in the first aspect of the present invention.

[0009] As used herein, the term "primer" refers to a short nucleic acid sequence that recognizes a target gene sequence, comprising a pair of forward and reverse primers. Specifically, the "primer" comprises a pair of primers that provide analytical results with specificity and sensitivity. Primers are considered to provide high specificity when used to amplify target gene sequences, but they do not induce the amplification of non-target sequences that are inconsistent with or complementary to the target gene sequence.

[0010] As used herein, the term "probe" refers to a substance that specifically binds to a target to be detected in a sample. Through this binding, the probe determines the presence of the target in the sample. Any probe can be used in this disclosure, provided it is commonly used in the art. Specifically, the probe can be a PNA (peptide nucleic acid), an LNA (locked nucleic acid), a peptide, polypeptide, protein, RNA, or DNA, with PNA being the most preferred. Specifically, the probe is a biological material that can be derived from an organism or synthesized in vitro, or a mimic thereof. For example, the probe can be an enzyme, protein, antibody, microorganism, animal or plant cell or organ, neuron, DNA, or RNA. DNA can include cDNA, genomic DNA, and oligonucleotides. Similarly, genomic RNA, mRNA, and oligonucleotides fall within the scope of RNA. Examples of proteins include antibodies, antigens, enzymes, and peptides.

[0011] In some embodiments, the primers include one or more of the primers shown in SEQ ID NO.3-SEQ ID NO.96.

[0012] A third aspect of the present invention provides a reagent or kit comprising the primers or probes described in the second aspect of the present invention.

[0013] In some implementations, the kit includes instructions, buffer solution, and container.

[0014] The fourth aspect of the present invention provides for any of the following applications: (1) The use of reagents for detecting all or part of the SNP sites described in the first aspect of the present invention in the preparation of products for diagnosing IgA nephropathy; In some embodiments, the reagent includes a probe that identifies the SNP site and primers that amplify the SNP site; In some implementations, the product includes chips, arrays, reagent kits, high-throughput sequencing platforms, or nucleic acid membrane strips; (2) The use of reagents for detecting all or part of the SNP sites described in the first aspect of the present invention in the preparation of products for predicting the risk of IgA nephropathy; (3) The use of reagents for detecting all or part of the SNP sites described in the first aspect of the present invention in the preparation of products for predicting the prognosis of IgA nephropathy; (4) The application of all or part of the SNP sites described in the first aspect of the present invention in constructing a multi-gene scoring model for IgA nephropathy.

[0015] The fifth aspect of the present invention provides a method for constructing a multi-gene scoring model for IgA nephropathy, the method comprising using all or part of the SNP site information described in the first aspect of the present invention for construction.

[0016] The sixth aspect of this invention provides a multi-gene scoring model for IgA nephropathy, wherein the multi-gene scoring model for IgA nephropathy is constructed by the construction method described in the seventh aspect of this invention; In some implementations, the IgA nephropathy multigene scoring model is as follows: .

[0017] In some implementations, the IgA nephropathy polygenic scoring model is an IgA nephropathy polygenic scoring model for Asian populations, and the risk scoring formula for the IgA nephropathy polygenic scoring model for Asian populations is GRS=(PRS-9.6856) / 0.6812.

[0018] The seventh aspect of the present invention provides any of the following methods: (1) A method for diagnosing whether a subject has IgA nephropathy, the method comprising: Obtain all or part of the SNP site information described in the first aspect of this invention from the subject; The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Based on the GRS, the subjects are classified to obtain a classification result of whether the subjects have IgA nephropathy or not. If the GRS is higher than the threshold, the subject is classified as having IgA nephropathy; if the GRS is lower than the threshold, the subject is classified as not having IgA nephropathy. (2) A method for assessing the risk of a subject having IgA nephropathy, the method comprising obtaining information on all or part of the SNP sites described in the first aspect of the present invention in the subject; Obtain all or part of the SNP site information described in the first aspect of this invention from the subject; The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Based on the GRS, the subjects are classified to obtain a classification result of whether the subjects have IgA nephropathy or not. If the GRS > threshold 1, the risk of the subject having IgA nephropathy is high; if the GRS ≤ threshold 2, the risk of the subject having IgA nephropathy is low; if the GRS > threshold 2 and ≤ threshold 1, the risk of the subject having IgA nephropathy is medium. In some implementations, the threshold 1 is 0.9000038148; In some implementations, the threshold 2 is -0.807558072; (3) A method for predicting the prognosis of IgA nephropathy, the method comprising obtaining all or part of the SNP site information described in the first aspect of the present invention in the subject; Obtain all or part of the SNP site information described in the first aspect of this invention from the subject; The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Based on the GRS, the subjects are classified to obtain either a classification result indicating a good prognosis or a classification result indicating a poor prognosis. If the GRS is higher than the threshold, the subject is classified as having a poor prognosis; if the GRS is lower than the threshold, the subject is classified as having a good prognosis.

[0019] The eighth aspect of the present invention provides any of the following systems: (1) A system for diagnosing whether a subject has IgA nephropathy, the system comprising: Data acquisition unit: Acquires all or part of the SNP site information described in the first aspect of this invention for the subject; Scoring unit: The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Classification unit: Based on the GRS, the subjects are classified to obtain a classification result of whether the subjects have IgA nephropathy or not. If the GRS is higher than the threshold, the subject is classified as having IgA nephropathy; if the GRS is lower than the threshold, the subject is classified as not having IgA nephropathy. Result output unit: Outputs classification results; (2) A system for assessing the risk of a subject having IgA nephropathy, the system comprising acquiring information on all or part of the SNP sites described in the first aspect of the present invention; Data acquisition unit: Acquires all or part of the SNP site information described in the first aspect of this invention for the subject; Scoring unit: The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Classification unit: Based on the GRS, the subjects are classified to obtain a classification result of whether the subjects have IgA nephropathy or not. If the GRS > threshold 1, the risk of the subject having IgA nephropathy is high; if the GRS ≤ threshold 2, the risk of the subject having IgA nephropathy is low; if the GRS > threshold 2 and ≤ threshold 1, the risk of the subject having IgA nephropathy is medium. Result output unit: Outputs classification results; In some implementations, the threshold 1 is 0.9000038148; In some implementations, the threshold 2 is -0.807558072; (3) A system for predicting the prognosis of IgA nephropathy, the system comprising acquiring SNP site information of all or part of the SNPs described in the first aspect of the present invention in a subject; Data acquisition unit: Acquires all or part of the SNP site information described in the first aspect of this invention for the subject; Scoring unit: The SNP site information is input into the IgA nephropathy multigene scoring model described in the sixth aspect of the present invention to calculate GRS; Classification unit: Based on the GRS, the subjects are classified to obtain a classification result of good prognosis or a classification result of poor prognosis; If the GRS is higher than the threshold, the subject is classified as having a poor prognosis; if the GRS is lower than the threshold, the subject is classified as having a good prognosis.

[0020] Output unit: Outputs classification results.

[0021] The ninth aspect of the present invention provides a computer device or program product or computer-readable storage medium, the computer device or program product or computer-readable storage medium including a memory and a processor, the device including the memory and the processor; the memory is used to store program instructions; the processor is used to invoke the program instructions, when the program instructions are executed, to implement the steps of the method for diagnosing whether a subject has IgA nephropathy as described in the seventh aspect of the present invention, or the steps of the method for assessing the risk of a subject having IgA nephropathy as described in the seventh aspect of the present invention, or the steps of the method for predicting the prognosis of IgA nephropathy as described in the seventh aspect of the present invention.

[0022] It should be understood that the terms "system," "device," and "unit" used herein are a method of distinguishing different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0023] Those skilled in the art will recognize that this invention can be implemented as an apparatus, method, or computer program product. Therefore, this disclosure can be embodied in the following forms: it can be entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, generally referred to herein as a "unit" or "system." Furthermore, in some embodiments, the invention can also be implemented as a computer program product contained in one or more computer-readable media, which includes computer-readable program code.

[0024] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0025] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0026] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0027] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0028] The advantages and beneficial effects of this invention are as follows: This invention provides a primer composition, detection kit, multi-gene scoring model, and risk prediction method for IgA nephropathy risk assessment. It has the following advantages and beneficial effects: 1. This invention provides the first systematic identification of 47 genetic susceptibility loci for IgA nephropathy, 17 of which are novel loci reported globally for the first time, significantly expanding our understanding of the genetic basis of IgA nephropathy. The established model integrates common and specific high-effect SNP loci from Asian and European populations and has been optimized for the Chinese population, resulting in significantly improved prediction accuracy.

[0029] 2. Based on a multi-ancestor GWAS meta-analysis including 11,931 patients with IgA nephropathy and 43,772 control individuals, this invention constructed and validated the first PRS model specifically for IgA nephropathy in Chinese and East Asian populations. This model achieved an AUC of 0.94 in the training cohort and a maximum AUC of 0.92 in the external validation cohort, significantly outperforming the diagnostic and predictive performance of existing publicly available models.

[0030] 3. The model effect estimation adopts the cross-ethnicity and cross-platform Bayesian Shrinkage algorithm (PRS-CSx), which is significantly better than the traditional PRS algorithm in terms of linkage disequilibrium (LD) correction and effect sparsity. It effectively overcomes the signal drift and heterogeneity problems between different ancestral samples, ensuring the cross-ethnicity applicability and stability of the model.

[0031] 4. The new model not only achieves accurate identification of disease risk but also enables high-resolution risk stratification and clinical application. This PRS model can simultaneously predict key clinical phenotypes (such as proteinuria levels, serum IgA concentration, and eGFR changes) and the risk of progression to end-stage renal failure, providing quantitative evidence for early disease screening and precise intervention.

[0032] 5. This invention takes into account both functional and regulatory sites in SNP screening, combines eQTL, pQTL and mQTL annotation, and integrates multi-omics information such as single-cell transcriptomics and proteomics causal inference to reveal the biological mechanisms of disease susceptibility at the molecular functional level, significantly enhancing the explanatory power and scientific value of the model.

[0033] 6. This invention breaks through the limitations of traditional kidney disease genetic risk scoring based only on a single population or a single omics. It has originality and systematic innovation in locus screening strategies, algorithm design and multi-omics integration, and realizes a highly accurate prediction model with pan-ancestry and functional weighting.

[0034] 7. This invention innovatively combines genetic risk score results with clinical phenotype and prognostic data, and uses a linear mixed model and Logistic / Cox regression method to achieve risk stratification prediction of disease progression in IgA nephropathy patients for the first time, and can provide guidance for precision intervention.

[0035] 8. In terms of experimental detection implementation, this invention employs an improved risk assessment amplicon detection system. Unlike conventional amplicon PCR, this method allows for the second reaction step without purification after the first round of amplification, significantly simplifying the experimental process, reducing costs, and shortening detection time, making it feasible for both research and clinical applications. Attached Figure Description

[0036] Figure 1 This is a Manhattan plot of GWAS results. The X-axis represents chromosome location, and the Y-axis represents -log10 (P-value) for each SNP; these results are based on 11,931 patients with IgA nephropathy and 43,772 healthy controls. Figure 2 This is a heatmap showing the correlation between a multigene risk score constructed from identified genome-wide significant loci and the clinical phenotype of IgA nephropathy. The results are based on an analysis of data from 3,783 patients with IgA nephropathy. Figure 3 This is the ROC curve of GRS in the training set. The AUC reaches 0.94, the sensitivity is 0.86, and the specificity is 0.88. Figure 4 This is the ROC curve of GRS validated in an external cohort. This external cohort included 551 patients with IgA nephropathy and 2698 healthy controls. The AUC reached 0.92, with a sensitivity of 0.84 and a specificity of 0.87. Figure 5 The graph shows the ROC curve of the GRS score predicting prognosis in the training set. The black graph represents the AUC of the international IgA nephropathy prognosis prediction model (based on patient clinical indicators), the red graph represents the prognostic AUC of PRS, and the purple graph represents the combined AUC of the two. Figure 6 This is a survival analysis curve for the top 20% of IgA nephropathy patients with poor prognosis based on the GRS score distribution. The X-axis represents follow-up time, and the Y-axis represents the survival probability without end-stage renal disease (ESKD). Detailed Implementation

[0037] The present invention will be further described below with reference to embodiments. The following description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make equivalent modifications to the disclosed technical content to create equivalent embodiments. Any simple modifications or equivalent changes made to the following embodiments based on the technical essence of the present invention without departing from the scope of the invention are all within the protection scope of the present invention.

[0038] Example 1: Screening of genetic susceptibility loci for kidney disease This invention first collected 11,931 IgA nephropathy patients and 43,772 control samples from three populations: China, the United States, and Europe. High-quality SNP genotyping data were obtained through whole-genome microarray sequencing. Sequencing data from different ancestral origins and platforms were analyzed using PLIN and METAL software, ultimately identifying 47 genetic susceptibility loci closely related to IgA nephropathy (see Manhattan plot of GWAS results). Figure 1 Furthermore, the latest Bayesian Shrinkage (PRS-CSx) algorithm was used to calculate the multigene risk score (see the heatmap of the correlation between PRS and clinical indicators). Figure 2 Table 1 below lists some key SNP loci, risk alleles, Bayesian effect values ​​(β values), and genotype distributions: Table 1 Key SNP loci information

[0039] The PRS-CSx algorithm significantly improves the performance of interancestry models. The algorithm formula is:

[0040] Where β^i is the Bayesian estimated ancestor effect value, and Gij is the genotype value of the ii-th SNP in the jj-th sample (the genotype value is assigned based on the number of risk alleles of the SNP; when there are 2 risk alleles, the value is 2; when there is 1 risk allele, the value is 1; otherwise, the value is 0. For example, the risk allele of rs2477134 is T, so the genotype values ​​of TT;TG;GG are 2,1,0).

[0041] Multi-omics integration and functional weighted scoring: Integrating GWAS significance, eQTL, protein MR, drug target and single-cell tissue expression, a multidimensional weighting method is used, and the weighted scoring formula is as follows:

[0042] The optimal SNPs and genes were selected by multi-factor weighting.

[0043] Example 2: Establishment of a Risk Assessment Method for IgA Nephropathy (1) Primer design for 47 IgA nephropathy susceptibility gene SNPs For the 47 IgA nephropathy susceptibility loci SNPs in Table 1, 200bp sequences containing the SNP sites were downloaded from databases such as UCSC and NCBI. Primers were designed following these principles: (1) The primer length should be designed to be 18-25 bp; (2) The primer annealing temperature should be controlled between 58-62℃, and the annealing temperature difference between primer pairs targeting 47 SNP sites should not exceed 2℃; (3) The GC content should be controlled between 40%-60%, and there should not be more than 4 consecutive G / C or A / T; (4) Each primer pair should be blast aligned to avoid non-specific amplification; (5) Primers should avoid complementarity between primer pairs and primers should avoid forming stable hairpin structures.

[0044] Primers were designed based on the above principles, and 47 pairs of primers that do not interfere with each other were finally selected. Universal primers were then added to these primer pairs, and the universal primer sequences are as follows: Universal primer 1: SEQ ID NO.1: CCCTACACGACGCTCTTCCGATCT Universal primer 2: SEQ ID NO.2: CCCTACACGACGCTCTTCCGATCT Table 2. Primer sequences for 47 SNP synthesis

[0045] (2) Assessment methods for IgA nephropathy The IgA nephropathy assessment kit includes a 47SNP primer pool, amplification enzyme for first-round PCR amplification, amplification buffer for second-round PCR amplification, a mixture of amplification enzymes for second-round PCR amplification, second-round amplification tag primers, and DNA purification magnetic beads. The experimental method for glomerulonephritis is as follows: (1) Genomic DNA extraction After obtaining informed consent, 2 mL of peripheral blood was collected from the individuals to be tested. Genomic DNA was extracted using a nucleic acid extraction kit (magnetic bead method) (manufacturer: Maikino (Chongqing) Gene Technology Co., Ltd.; registration number: Yuxiebei 20190138) according to the instructions for use. The concentration and purity of the genomic DNA were determined by Nanodrop. The concentration was greater than 20 ng / μL and the A260 / 280 was within 1.7-2.0, which met the quality control requirements.

[0046] (2) Primer pool: After the above primers are synthesized by commercial manufacturers, each primer is diluted to 100 μM and all primers are mixed to form a primer pool. The final concentration of each primer in the primer pool is 25-50 nM. (3) First step PCR amplification: Add 2 μL of amplification enzyme, 12.5 μL of amplification buffer, 2 μL of primer pool, and 40 ng of genomic DNA to a PCR tube. Add enzyme-free water to a total volume of 25 μL, mix well, and then proceed with amplification. The amplification program is shown in Table 3 below: Table 3 Amplification Procedure

[0047] (4) Second step PCR amplification: Add 10 μL of amplification enzyme mixture, 2 μL of tag primer, and 8 μL of the first round PCR product to the new PCR solution, mix well and perform PCR amplification. The amplification program is shown in Table 4 below: Table 4 Amplification Procedure

[0048] Tag primer sequence: Tag primer 1: SEQ ID NO.97 CAAGCAGAAGACGGCATACGAGATNNNNNNNNGTGACTGGAGTTCAGACGTGTGCTCTTCCGATC; Tag primer 2: SEQ ID NO.98 AATGATACGGCGACCACCGAGATCTACACNNNNNNNNNACACTCTTTCCCTACACGAGCTCTTCCGATCT NNNNNNNNN: any base of A, T, C, or G. Tag primer 1 and tag primer 2 were mixed to form a final concentration of 10 μM.

[0049] (5) DNA purification: Add 16 μL of purification magnetic beads to the PCR product from the previous step, mix well and let stand for 5 min; place on a magnetic rack for 5 min and discard the supernatant; wash twice with 80% ethanol, mix well with 33 μL of enzyme-free water and let stand for 5 min; place on a magnetic rack for 5 min and transfer the supernatant to a new EP tube.

[0050] (6) Library quality control: Use qubit to perform concentration quality control on the library in (5). The concentration should be greater than 10 ng / μL and the size of the tested fragments should be around 180-300 bp. That is, the library quality control is qualified.

[0051] (7) High-throughput sequencing: The libraries that passed the quality inspection in (6) were sequenced on sequencing platforms such as Illumina Novaseq6000, MGISEQ-T7, SURFSEQ 5000, and Uniseq2000.

[0052] (8) Data analysis: The raw sequencing data were cut and adapted to the corresponding positions of the reference genome hg19 (GRCh37) using bwa software after removing adapter sequences, low-quality bases (base quality lower than Q15) and short sequences (length less than 40bp). Single nucleotide variants and insertion / deletion variants were detected using GATK software. The mutation frequency, site depth, and site genotype of the target SNP site were output as a link file. The genetic risk score (GRS) was obtained by standardizing the data for the Asian population according to formula 1. The calculation formula is shown in formula 2.

[0053] Formula 1: PRSi: PRS score of the i-th individual; βj: effect size of the risk allele of the j-th SNP; Gij: genotype of the j-th SNP of the i-th individual (assigned values ​​of 0, 1, and 2 depending on the number of risk alleles):

[0054] Calculation formula 2: Genetic risk score (GRS) = (PRS - 9.6856) / 0.6812 IgA nephropathy patients were further divided into five groups based on GRS, as shown in Table 5 below. Table 5 Grouping of IgA Nephropathy Patients

[0055] The genetic risk score thresholds obtained from the GRS susceptibility stratification analysis are shown in Table 6 below: Table 6. Genetic risk score thresholds

[0056] Further prognostic analysis based on susceptibility risk (Table 7) revealed that patients at high susceptibility risk had a significantly increased risk of developing ESKD or a ≥50% decrease in eGFR (studies have shown that they have a 39% increased risk of developing ESKD or a ≥50% decrease in eGFR compared to other IgAN patients).

[0057] Table 7 Prognostic Analysis Results

[0058] Example 3: Application of IgA Nephropathy Assessment Method With informed consent, peripheral blood samples were collected from 11 subjects. The genetic risk score (GRS) for IgA nephropathy was calculated from the 11 samples using the kit and method described in Example 2 to assess the subjects' risk of developing the disease.

[0059] Eleven samples were tested using the kit and method described in Example 2. The depth of the 47 SNP loci was greater than 500x, which met the quality control requirements and allowed for the next step of genetic risk score calculation.

[0060] Table 8. In-depth statistics of 47 SNP sites in some samples

[0061] Of the 11 samples tested, 2 had genetic risk scores (GRS) within the intermediate-risk threshold, and 1 had a genetic risk score within the high-risk threshold. This was consistent with the clinical diagnosis of IgA nephropathy (diagnostic criteria for IgA nephropathy: pathological examination of renal tissue revealing IgA or predominantly IgA deposition in the glomerular mesangial area, excluding secondary IgA nephropathy). (See Table 9) The specific information is as follows: 1.25C002942, female, age 52, baseline eGFR 47 ml / min / 1.73 m 2 The baseline proteinuria was 1.53 g / day, systolic blood pressure was 121 mmHg, and diastolic blood pressure was 76 mmHg. The patient denied any family history of genetic diseases or similar conditions. 2. Patient 25C002948, male, age 21 years, baseline eGFR 64 ml / min / 1.73 m 2 The baseline proteinuria was 7.53 g / d, the systolic blood pressure was 140 mmHg, and the diastolic blood pressure was 90 mmHg. The patient denied any family history of genetic diseases or similar illnesses.

[0062] 3.25C002951, male, age 30, baseline eGFR 58 ml / min / 1.73 m2 The baseline proteinuria was 0.34 g / d, the systolic blood pressure was 138 mmHg, and the diastolic blood pressure was 99 mmHg. The patient denied any family history of genetic diseases or similar illnesses.

[0063] Table 9

[0064] Example 4: GRS diagnosis of IgA nephropathy patients The training set included 11,931 patients with IgA nephropathy and 43,772 control samples. Using the aforementioned primers, detection methods, and calculation methods, GRS was calculated, and ROC curves were plotted. The AUC reached 0.94, the sensitivity was 0.86, and the specificity was 0.88. The results are as follows: Figure 3 As shown.

[0065] In the validation set, 551 patients with IgA nephropathy and 2698 healthy controls from an Asian population were included. Using the aforementioned primers and detection methods, and computational methods, GRS was calculated, and ROC curves were plotted. The AUC reached 0.92, the sensitivity was 0.84, and the specificity was 0.87. The results are as follows: Figure 4 As shown.

[0066] Example 5: GRS combined with an international risk prediction model for IgA nephropathy based on clinical indicators to predict poor prognosis in patients with IgA nephropathy. In a follow-up cohort of 2354 patients with IgA nephropathy, the prognosis of IgA nephropathy patients was predicted using GRS combined with an international risk prediction model for IgA nephropathy based on patient clinical indicators. ROC, etc. Figure 5 As shown, the predictive power of the combined GRS assay was improved compared to individual patient clinical indicators. The same results were obtained in the validation set, demonstrating that the predictive power of the combined GRS assay was improved compared to individual patient clinical indicators.

[0067] Figure 6 Survival curves for the top 20% of IgA nephropathy patients with poor prognosis based on the GRS score distribution. The X-axis represents follow-up time, and the Y-axis represents the survival probability without end-stage renal disease (ESKD).

[0068] If only a few SNP sites are used, for example, only 4 SNP sites (rs11150612, rs7634389, rs4823074, rs9275596) are used to construct the GRS, then the GRS is not related to the prognosis, as shown in Table 10 below.

[0069] Table 10: GRS is not correlated with prognosis.

[0070] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A combination of SNP loci, characterized in that, The SNP site combination comprises one or more of the following SNP sites, the reference genome is GRCh37, and the position information of the SNP sites comprises rs2477134, rs530225, rs12754452, rsl 18170123, rsl 1799853, rs1801274, rs4916312, rs6677604, rsl 177206, rs3769684, rs7607437, rs1057258, rs73065118, rs7634389, rs6832151, rs6828610, rs60307531, rs6942325, rs12530084, rs9268557, rs9272105, rs9275355, rs9275596, rs3128927, rsl 14394181, rs3798258, rs3093018, rs2615771, rs75413466, rs548902, rs1347321, rs77134091, rs13300483, rs4077515, rs10821944, rsl 108618, rs7127924, rs10896045, rs2187461, rs2273393, rs1806882, rsl 1150612, rsl 879210, rs3803800, rs4561508, rs1865097, rs4823074.

2. The combination of SNP sites according to claim 1, wherein, The information of each SNP site is as follows: 。 3. A primer or probe, characterized in that, A primer or probe for amplifying or detecting the SNP site according to all or part of claims 1 or 2; Preferably, the primer comprises one or more of the primers shown in SEQ ID NO. 3-SEQ ID NO.

96.

4. A reagent or kit characterized in that, A primer or probe as claimed in claim 3; Preferably, the kit comprises instructions, buffers, containers.

5. Any of the following applications: (1) The use of a reagent for detecting the SNP site according to all or part of claims 1 or 2 in the preparation of a product for diagnosing IgA nephropathy; Preferably, the reagent comprises a probe that recognizes the SNP site, a primer that amplifies the SNP site; Preferably, the product comprises a chip, an array, a kit, a high-throughput sequencing platform, or a nucleic acid membrane strip; (2) The use of a reagent for detecting the SNP site according to all or part of claims 1 or 2 in the preparation of a product for predicting the risk of IgA nephropathy; (3) The use of a reagent for detecting the SNP site according to all or part of claims 1 or 2 in the preparation of a product for predicting the prognosis of IgA nephropathy; (4) The use of the SNP site according to all or part of claims 1 or 2 in the construction of a multi-gene scoring model for IgA nephropathy.

6. A method for constructing an IgA nephropathy polygenic scoring model, characterized in that, The construction method comprises using the SNP site information according to all or part of claims 1 or 2 to construct.

7. An IgA nephropathy polygenic score model, characterized in that, The IgA nephropathy polygenic scoring model is constructed by the method of claim 8. Preferably, the IgA nephropathy polygenic scoring model is: ； Preferably, the IgA nephropathy polygenic scoring model is an IgA nephropathy polygenic scoring model for Asian population, and a risk score formula of the IgA nephropathy polygenic scoring model for Asian population is GRS=(PRS-9.6856) / 0.6812.

8. Any one of the following methods: (1) A method of diagnosing whether or not a subject has IgA nephropathy, characterized by, The method comprises: obtaining SNP site information of a subject, all or part of which is described in claim 1 or 2; inputting the SNP site information into the IgA nephropathy polygenic scoring model described in claim 7 to calculate GRS classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is higher than a threshold value, the classification result that the subject has IgA nephropathy is obtained, and if the GRS is lower than the threshold value, the classification result that the subject does not have IgA nephropathy is obtained; (2) A method for evaluating the risk of IgA nephropathy in a subject, characterized in that the method comprises obtaining SNP site information of a subject, all or part of which is described in claim 1 or 2; obtaining SNP site information of a subject, all or part of which is described in claim 1 or 2; inputting the SNP site information into the IgA nephropathy polygenic scoring model described in claim 7 to calculate GRS; classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is greater than threshold value 1, the risk of IgA nephropathy of the subject is high, if the GRS is less than or equal to threshold value 2, the risk of IgA nephropathy of the subject is low, and if the GRS is greater than threshold value 2 and less than or equal to threshold value 1, the risk of IgA nephropathy of the subject is medium; Preferably, the threshold value 1 is 0.9000038148; Preferably, the threshold value 2 is -0.807558072; (3) A method for predicting the prognosis of IgA nephropathy, characterized in that the method comprises obtaining SNP site information of a subject, all or part of which is described in claim 1 or 2; obtaining SNP site information of a subject, all or part of which is described in claim 1 or 2; inputting the SNP site information into the IgA nephropathy polygenic scoring model described in claim 7 to calculate GRS; classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is greater than threshold value 1, the risk of IgA nephropathy of the subject is high, if the GRS is less than or equal to threshold value 2, the risk of IgA nephropathy of the subject is low, and if the GRS is greater than threshold value 2 and less than or equal to threshold value 1, the risk of IgA nephropathy of the subject is medium; 9. Any one of the following systems: (1) A system for diagnosing whether or not a subject has IgA nephropathy, characterized by, The system comprises: a data acquisition unit for acquiring SNP site information of a subject, all or part of which is described in claim 1 or 2; a scoring unit: inputting the SNP site information into the IgA nephropathy polygenic scoring model in claim 7 to calculate GRS; a classification unit: classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is higher than a threshold value, the classification result that the subject has IgA nephropathy is obtained, and if the GRS is lower than the threshold value, the classification result that the subject does not have IgA nephropathy is obtained; a result output unit: outputting the classification result; (2) A system for evaluating the risk of IgA nephropathy in a subject, characterized in that the system comprises obtaining SNP site information of the subject in all or part of claims 1 or 2; a data acquisition unit: acquiring SNP site information of the subject in all or part of claims 1 or 2; a scoring unit: inputting the SNP site information into the IgA nephropathy polygenic scoring model in claim 7 to calculate GRS; a classification unit: classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is greater than threshold value 1, the risk of IgA nephropathy in the subject is high, if the GRS is less than or equal to threshold value 2, the risk of IgA nephropathy in the subject is low, and if the GRS is greater than threshold value 2 and less than or equal to threshold value 1, the risk of IgA nephropathy in the subject is medium; a result output unit: outputting the classification result; Preferably, the threshold value 1 is 0.9000038148; Preferably, the threshold value 2 is -0.807558072; (3) A system for predicting the prognosis of IgA nephropathy, characterized in that the system comprises obtaining SNP site information of the subject in all or part of claims 1 or 2; a data acquisition unit: acquiring SNP site information of the subject in all or part of claims 1 or 2; a scoring unit: inputting the SNP site information into the IgA nephropathy polygenic scoring model in claim 7 to calculate GRS; a classification unit: classifying the subject based on the GRS to obtain a classification result that the subject has IgA nephropathy or a classification result that the subject does not have IgA nephropathy; if the GRS is greater than threshold value 1, the risk of IgA nephropathy in the subject is high, if the GRS is less than or equal to threshold value 2, the risk of IgA nephropathy in the subject is low, and if the GRS is greater than threshold value 2 and less than or equal to threshold value 1, the risk of IgA nephropathy in the subject is medium; a result output unit: outputting the classification result.

10. A computer device or program product or computer readable storage medium, characterized by, The computer device or program product or computer readable storage medium comprises a memory and a processor, the device comprises a memory and a processor; the memory is used to store program instructions; the processor is used to call program instructions, when the program instructions are executed, the steps of the method for diagnosing whether a subject has IgA nephropathy in claim 8, or the steps of the method for evaluating the risk of IgA nephropathy in a subject in claim 8, or the steps of the method for predicting the prognosis of IgA nephropathy in claim 8 are implemented.

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