Systems and methods for analysis of samples associated with insulin resistance

Abstract

Provided herein are systems and methods for analysis of biological samples to identify biomarkers associated with insulin resistance. For example, provided herein are molecular signatures that find use in characterizing samples to facilitate research, drug discovery, and treatment associated with insulin resistance.

Description

[0001] Atty. Docket No. UM-42504.601 SYSTEMS AND METHODS FOR ANALYSIS OF SAMPLES ASSOCIATED WITH INSULIN RESISTANCE PRIORITY STATEMENT This application claims priority to U.S. Provisional Application No. 63 / 600,491, filed November 17, 2023, the entire contents of which are incorporated herein by reference for all purposes. STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under DK107904, DK131787, and DK106621 awarded by the National Institutes of Health. The government has certain rights in the invention. FIELD Provided herein are systems and methods for analysis of biological samples to identify biomarkers associated with insulin resistance. For example, provided herein are molecular signatures that find use in characterizing samples to facilitate research, drug discovery, and treatment associated with insulin resistance. BACKGROUND Insulin resistance (IR) is closely linked to numerous cardiometabolic risk factors and is thought to be the origin of many metabolic diseases1-3. Insulin resistance is characterized by a diminished cellular response to insulin, leading to dyslipidemia4and higher circulating levels of insulin and glucose5. The gold standard method to measure insulin resistance requires the usage of glucose clamp – an invasive, expensive, and time-consuming technique that is impractical for routine clinical use6. Simpler methods, such as the Insulin Sensitivity Index or the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), involve the direct measurement of insulin and / or glucose levels and have been shown to correlate strongly with the gold-standard glucose- clamp technique7,8. Atty. Docket No. UM-42504.601 What is needed are systems and methods to better analyze the disease to facilitate drug discovery and disease prevention and treatment. SUMMARY Provided herein are systems and methods for analysis of biological samples to identify biomarkers associated with insulin resistance. For example, provided herein are molecular signatures that find use in characterizing samples to facilitate research, drug discovery, and treatment associated with insulin resistance. Insulin resistance (IR) is a well-established risk factor for metabolic disease. However, as insulin resistance is challenging to quantify, genetic studies of this trait have been limited in scale. The ratio of triglycerides:high-density lipoprotein cholesterol is a surrogate marker of insulin resistance. Experiments described herein used a genome-wide association study of the TG:HDL-C ratio to identify 369 independent SNPs associated with IR (Table 2). Of these 369 loci, 114 had an FDR-adjusted p-value<0.05 in other genome-wide studies of insulin resistance making them high-confidence IR-associated loci (Table 3). These 114 loci cluster into 5 distinct groups upon phenome-wide analysis and are enriched for candidate genes expressed in tissues / cell types associated with insulin resistance pathology. To evaluate the combined effect of the high-confidence SNPs, a polygenic risk score was generated using the 114 SNPs shown in Table 3. In some embodiments compositions, kits, systems, and methods are provided for analyzing the one or more variants. Variants are detected directly or indirectly. In some embodiments, direct methods comprise use of a molecular assay such as a hybridization assay (e.g., using one or more allele-specific primers or probes), a sequencing assay, a microarray, a cleavage assay, or the like. In some embodiments, indirect methods comprising detection of variants in linkage equilibrium with a variant, detections of altered gene expression relative to wild-type, or the like. In some embodiments, one or more of these variants is detected in combination with one or more other variants. In some such embodiments, the total number of variants detected or analyzed is less than 500, less than 200, less than 100, less than 50, or less than 25. In some embodiments, at least 10 of the listed variants are analyzed. In some embodiments, at least fifteen of the variants listed are analyzed. In some embodiments, at least 20 of the variants listed Atty. Docket No. UM-42504.601 are analyzed. In some embodiments, only variants from the listed variants are analyzed. In other embodiments, additional variants not listed are analyzed in combination with one or more of the listed variants. The present disclosure is not limited to particular variants. In some embodiments, those shown in Tables 2 or 3 are detected. For example, in some embodiments, 10 or more (e.g., 10, 20, 50, 100, 150, 200, 250, 300, or all 359) of the 369 variants shown in Table 2 are detected. In some embodiments, 10 or more (e.g., 10, 20, 50, 100, or all 114) of the 114 variants shown in Table 3 are detected. In some embodiments, 10 or more (e.g., 10, 20, 50, 100, 150, 200, 250, 300, or all) of the variants shown in Table 8 are detected. In some embodiments, the 10 or more comprises rs2943645, rs7012814, rs13389219, rs1538742, rs392794, rs1461729, rs2800709, rs2067819, rs114760566, rs9687846, and rs998584. Any suitable sample may be used that contains nucleic acid amenable to analysis. In some embodiments, the biological sample is selected from the group consisting of blood, serum, plasma, saliva, tissue, hair, semen, and urine. In some embodiments, a biological sample is obtained from a subject suspected of having insulin resistance. Such suspicion may arise from any of any number of factors including, but not limited to, family history, obesity, signs or symptoms of disease, and a positive imaging or diagnostic test indicating disease. In some embodiments, the variants are associated with one or more of increased triglycerides:high- density lipoprotein ratio, LDL cholesterol levels, systolic blood pressure, hyperlipidemia, hyperglyceridemia, hypertension, waist to hip ratio, type 2 diabetes, alanine aminotransferase levels, or body mass index. Also provided herein are methods of managing insulin resistance, comprising: analyzing a biological sample from a subject for one or more of the variants from the list; generating an insulin resistance risk score based on the presence or absence of said variants; and treating the subject with an insulin resistance intervention if said risk score indicates a predisposition to or presence of insulin resistance. In some embodiments, the risk score is calculated using an algorithm that accounts for each of the analyzed variants. The present disclosure is not limited to particular insulin resistance interventions. Examples include but are not limited to, applying a weight loss regime, exercise, and one or more active agents selected from, for example, a thiazolidinedione, a statin, a blood pressure lowering agent; or any combination thereof. Atty. Docket No. UM-42504.601 Further provided herein are systems (e.g., kits, reactions mixtures, etc.) comprising: a set or reagents that specifically detect one or more variants from those described in Tables 2 or 3. In some embodiments, the system detects a total of less than 500, less than 200, less than 100, less than 50, or less than 25 variants. In some embodiments, the reagents comprise one or more primers or probe specific for the variants (e.g., primers or probes useful in allele-specific PCR or similar assays). In some embodiments, the reagents comprising nucleic acid sequence reagents. In some embodiments, the reagents comprise a microarray (e.g., a hybridization based microarray). Also provided herein is a non-transitory computer-readable storage medium comprising an instruction, wherein when the instruction is run by at least one computer processor, wherein the at least one processor performs operations comprising one or more or each of the steps: a) receiving data identifying the presence or absence of a variant from Table 2 or 3 in a biological sample; b) generating a insulin resistance risk score from the data; and c) displaying or reporting said risk score. The displaying may comprise generating a written or electronic report for use by a physician, a researcher, a patients, or any other desired format. Further provided herein are methods of diagnosing insulin resistance or predisposition to insulin resistance comprising: analyzing a biological sample from a subject for one or more variant from the list of those shown in Tables 2 or 3. Additional embodiments are described herein. BRIEF DESCRIPTION OF FIGURES Fig. 1. Study Design. Inclusion and exclusion criteria for individuals, variants, and selection of the 114 high-confidence IR-associated loci. Includes information on novel genes and non-synonymous variants. Fig. 2. Overlap of the 114 high-confidence IR-associated loci with insulin-related traits. (A) Overlap of each SNP with insulin traits and annotations. (B) Overlap of high-confidence 114 high-confidence IR-associated loci with insulin related traits in an aggregate form. Fig. 3. Effects of 114 high-confidence IR-associated loci with IR-related traits. (A) Heatmap and clustering of two-tailed Z-scores of high-confidence variants for metabolic (T2D, Fasting Insulin*, Fasting Glucose*), body composition (Waist Hip Ratio(WHR)*, BMI), endocrine (Polycystic Ovarian Syndrome (PCOS)), kidney function (estimated glomerular Atty. Docket No. UM-42504.601 filtration rate(eGFR)), cardiovascular (triglycerides, LDL-C, Myocardial Infarction(MI), Systolic Blood Pressure(SBP), HDL-C) and liver (ALT, NAFLD) traits from public GWASes. (B) Forest plots showing the association of a subgroup’s polygenic risk score (PRS) and IR-related traits among individuals in the top and bottom quartile of the PRS distribution. Fig. 4. Tissue, cell type, and physiological system enrichment of the 114 high-confidence IR-associated loci. DEPICT enrichment for high-confidence insulin resistance-associated loci for cell types (A), (B) tissues, and (C) physiological systems. Fig. 5. Gene-set enrichment of the 114 high-confidence IR-associated loci. Gene-sets reaching statistical significance (p<1e-04) in the gene-set enrichment analysis of the 114 high- confidence IR-associated loci. Fig. 6. Polygenic risk score analysis in the Michigan Genomics Initiative using the 114 high-confidence IR-associated loci. PheWAS Manhattan plot showing the association between the 114 SNP polygenic risk score and traits in the Michigan Genomics Initiative using a Firth’s logistic regression model. Fig. 7. Effects of 114 high-confidence IR-associated loci with IR-related traits. Fig. 8. Table 8 DETAILED DESCRIPTION Provided herein are systems and methods for analysis of biological samples to identify biomarkers associated with insulin resistance. For example, provided herein are molecular signatures that find use in characterizing samples to facilitate research, drug discovery, and treatment associated with insulin resistance. DEFINITIONS The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of,” and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not. Atty. Docket No. UM-42504.601 For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated. Unless otherwise defined herein, scientific, and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear; in the event, however of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. As used herein, “nucleic acid” or “nucleic acid sequence” refers to a polymer or oligomer of pyrimidine and / or purine bases, preferably cytosine, thymine, and uracil, and adenine and guanine, respectively (See Albert L. Lehninger, Principles of Biochemistry, at 793-800 (Worth Pub. 1982)). The present technology contemplates any deoxyribonucleotide, ribonucleotide, or peptide nucleic acid component, and any chemical variants thereof, such as methylated, hydroxymethylated, or glycosylated forms of these bases, and the like. The polymers or oligomers may be heterogenous or homogenous in composition and may be isolated from naturally occurring sources or may be artificially or synthetically produced. In addition, the nucleic acids may be DNA or RNA, or a mixture thereof, and may exist permanently or transitionally in single-stranded or double-stranded form, including homoduplex, heteroduplex, and hybrid states. In some embodiments, a nucleic acid or nucleic acid sequence comprises other kinds of nucleic acid structures such as, for instance, a DNA / RNA helix, peptide nucleic acid (PNA), morpholino nucleic acid (see, e.g., Braasch and Corey, Biochemistry, 41(14): 4503-4510 (2002)) and U.S. Pat. No. 5,034,506), locked nucleic acid (LNA; see Wahlestedt et al., Proc. Natl. Acad. Sci. U.S.A., 97: 5633-5638 (2000)), cyclohexenyl nucleic acids (see Wang, J. Am. Chem. Soc., 122: 8595-8602 (2000)), and / or a ribozyme. Hence, the term “nucleic acid” or “nucleic acid sequence” may also encompass a chain comprising non-natural nucleotides, modified nucleotides, and / or non- nucleotide building blocks that can exhibit the same function as natural nucleotides (e.g., “nucleotide analogs”); further, the term “nucleic acid sequence” as used herein refers to an oligonucleotide, nucleotide or polynucleotide, and fragments or portions thereof, and to DNA or RNA of genomic or synthetic origin, which may be single or double- Atty. Docket No. UM-42504.601 stranded, and represent the sense or antisense strand. The terms “nucleic acid,” “polynucleotide,” “nucleotide sequence,” and “oligonucleotide” are used interchangeably. They refer to a polymeric form of nucleotides of any length, either deoxyribonucleotides or ribonucleotides, or analogs thereof. “Probes” are nucleic acids capable of binding in a base-specific manner to a complementary strand of nucleic acid. Such probes include nucleic acids and peptide nucleic acids. Hybridization is usually performed under stringent conditions. The term “primer” refers to a single-stranded oligonucleotide capable of acting as a point of initiation of template-directed DNA synthesis under appropriate conditions, in an appropriate buffer and at a suitable temperature. The appropriate length of a primer depends on the intended use of the primer, but typically ranges from 15 to 30 nucleotides. A primer sequence need not be exactly complementary to a template, but must be sufficiently complementary to hybridize with a template. The term “primer site” refers to the area of the target DNA to which a primer hybridizes. The term “primer pair” means a set of primers including a 5′ upstream primer, which hybridizes to the 5′ end of the DNA sequence to be amplified and a 3′ downstream primer, which hybridizes to the complement of the 3′ end of the sequence to be amplified. The nucleic acids, including any primers, probes and / or oligonucleotides can be synthesized using a variety of techniques currently available, such as by chemical or biochemical synthesis, and by in vitro or in vivo expression from recombinant nucleic acid molecules, e.g., bacterial or retroviral vectors. For example, DNA can be synthesized using conventional nucleotide phosphoramidite chemistry or other methodologies well known in the art. In addition, nucleic acids can comprise uncommon and / or modified nucleotide residues or non-nucleotide residues. The terms “polymorphism” or “variant” refers to the occurrence of two or more genetically determined alternative sequences or alleles in a population. Each divergent sequence is termed an allele, and can be part of a gene or located within an intergenic or non-genic sequence. A diallelic polymorphism has two alleles, and a triallelic polymorphism has three alleles. Diploid organisms can contain two alleles and may be homozygous or heterozygous for allelic forms. The first identified allelic form is arbitrarily designated the reference form or allele; other allelic forms are designated as alternative or variant alleles. The most frequently occurring allelic form in a selected population is typically referred to as the wild-type form. Atty. Docket No. UM-42504.601 As used herein, “treat,” “treating,” and the like means a slowing, stopping, or reversing of progression of a disease or disorder. The term also means a reversing of the progression of such a disease or disorder. As such, “treating” means an application or administration of methods to a subject, where the subject has a disease or a symptom of a disease, where the purpose is to cure, heal, alleviate, relieve, alter, remedy, ameliorate, improve, or affect the disease or symptoms of the disease. Exemplary methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present disclosure. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting. ANALYZING POLYMORPHISMS Provided herein are methods comprising analyzing a biological sample from a subject for one or more polymorphisms described in Tables 2 or 3. The analysis described herein identified several genome-wide significant variants associated with insulin resistance (e.g., those shown in Tables 2, 3, or 8). In some embodiments, the variants are one or more of rs2943645, rs7012814, rs13389219, rs1538742, rs392794, rs1461729, rs2800709, rs2067819, rs114760566, rs9687846, or rs998584. Insulin resistance (IR) is closely linked to numerous cardiometabolic risk factors and is thought to be the origin of many metabolic diseases1-3. Insulin resistance is characterized by a diminished cellular response to insulin, leading to dyslipidemia4and higher circulating levels of insulin and glucose5. The gold standard method to measure insulin resistance requires the usage of glucose clamp – an invasive, expensive, and time-consuming technique that is impractical for routine clinical use6. Simpler methods, such as the Insulin Sensitivity Index or the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), involve the direct measurement of insulin and / or glucose levels and have been shown to correlate strongly with the gold-standard glucose- clamp technique7,8. These methods have been previously applied to identify 130 loci independently associated with insulin resistance across several studies9-14. These loci have been linked to genes with functions important in insulin receptor signaling (GRB14, IRS1), glycogen metabolism (PPP1R3B), and adipogenesis (LYPLAL1, FAM13A) among other pathways. However, these genetic studies of insulin resistance have been limited in scale and thus utility. Atty. Docket No. UM-42504.601 The ratio of triglyceride to high-density lipoprotein cholesterol (TG:HDL-C) has been previously validated as a surrogate measure of insulin resistance15-19. To further investigate the genetic basis of insulin resistance, experiments described herein utilized a genome-wide association study (GWAS) of the TG:HDL-C to identify a set of high-confidence IR-associated SNPs that reach significance in external studies of insulin resistance. These high confidence loci were explored in the context of known insulin biology, interrogated for previously uncharacterized roles in insulin resistance, and examined for their contribution to disease in external datasets. Collectively, this study identifies numerous previously uncharacterized loci for insulin resistance in the context of metabolic pathways, traits, and diseases. Accordingly, the present disclosure provides a method for diagnosing insulin resistance or related diseases or conditions by assaying for the presence of one or more variants related to IR. The presence of such a polymorphisms or mutations can be regarded as indicative of an individual's risk (increased or decreased) for the disease, especially in individuals who lack other predisposing or protective polymorphisms for the IR. Even in cases where the predictive contribution of a given polymorphism is relatively minor by itself, overall assessment of the polymorphisms allows diagnosis with a much higher degree of certainty and reliability. The present disclosure further provides a method of managing insulin resistance (e.g., by calculating a risk score). The risk score may be calculated using an algorithm that accounts for one or more or each of the analyzed polymorphisms. The risk score may be calculated using non- weighted or weighted sums of risk polymorphisms using effect sizes from genome-wide association studies as their weights or effects of the particular polymorphism on the score. For example, those polymorphisms with inherently higher risk are weighted differently than those polymorphisms with lower individual risk. The risk score may be based on other factors outside of the genetic polymorphisms described herein. Other factors may include the general health of the subject, previously identified disease in close family members, or other related identified disease or disorders. For example, risk factors may include high cholesterol, high levels of triglycerides in the blood, obesity, high blood pressure, age, and concentration or abundance of abdominal body fat. The risk score may be a measure of an individual risk of insulin resistance or related diseases in comparison to an average individual of a population or subset of population. For Atty. Docket No. UM-42504.601 example, the score may be in comparison to any other individual or an individual with a similar ethnic background, age, sex, or prior health condition. The risk score may be used to align a subject’s level of disease with appropriate treatments. For examples, subjects with a specific disease phenotype may be linked to specific treatments for that subtype which results in the best management of the disease or lacks unwanted side effects or long-term complications. The risk score may be output or displayed in any number of formats, including reports with bins, a color or grayscale gradient, a thermometer, a gauge, a histogram, or a bar graph. The risk score may provide a numerical output which is associated with low, medium, or high risk of insulin resistance. Alternatively, or in addition, the risk score may be output as a rank score in a populations, such as a percentile of risk within a certain population. The risk score may be output with any proposed treatment recommendations or follow-up procedures to further assess risk. The risk score may further indicate the need or the type of treatment for an individual suspected to have or at risk of developing insulin resistance. Treatments for insulin resistance include those known in the art to reduce risk and include lifestyle changes, surgery, or medicament regimes. In some embodiments, the treatments include adoption of a healthy diet and exercise program, optionally as part of a weight loss regime, control of blood sugar, etc. In some embodiment, treating comprises administration of one or more active agents. In some embodiments, the active agent is metformin, thiazolidinedione, a statin, a blood pressure lowering agent; or any combination thereof. In some embodiments, the treatments include modulating transcription, and thereby expression, of one or more target genes. For example, the treatments may include activation or repression of transcription of one or more target genes as listed in Tables 2 or 3. In some embodiments, the treatments include knocking out one or more target genes. For example, the treatments may include knocking out one or more target genes as listed in Tables 2 or 3. In some embodiments, transcription of the target gene is modulated by administering a clustered regularly interspaced short palindromic repeats (CRISPR) / CRISPR associated (Cas) protein system for use in CRISPR interference (CRISPRi) or CRISPR activation (CRISPRa) (see, e.g., Konermann et al. Nature. 2014 Dec. 10. doi: 10.1038 / nature14136; Qi, L. S., et al. (2013). Cell. 152 (5): 1173-83; Gilbert, L. A., et al., (2013). Cell. 154 (2): 442-51; and Maeder et al. Nat Methods 10(10):977-979 (2013)). Atty. Docket No. UM-42504.601 Cas proteins binding of specific DNA sequences through guide RNA can naturally result in a transcription block, a process termed CRISPR interference (CRISPRi). For use in mammalian cells, CRISPRi is even more effective when transcriptional repressor domains are tethered to the Cas protein. Transcriptional repressors may inhibit transcription via: recruitment of other transcription factor proteins; modification of target DNA such as methylation; recruitment of a DNA modifier; modulation of histones associated with target DNA; recruitment of a histone modifier such as those that modify acetylation and / or methylation of histones; or a combination thereof. For example, transcriptional repressors such as the Kriippel associated box (KRAB or SKD); KOX1 repression domain; the Mad mSIN3 interaction domain (SID); the ERF repressor domain (ERD); histone lysine methyltransferases such as Pr-SET7 / 8, SUV4-20H1, RIZ1, and the like; histone lysine demethylases such as JM JD2 A / JHDM3 A, JMJD2B, JMJD2C / GASC1, JMJD2D, JARID 1 A / RBP2, JARIDlB / PLU-1, JARIDIC / SMCX, JARIDID / SMCY; histone lysine deacetylases such as HDAC1, HDAC2, HDAC3, HDAC8, HDAC4, HD AC 5, HDAC7, HDAC9, SIRT1, SIRT2, HDAC11; DNA methylases such as Hhal DNA m5c-methyltransferase (M.Hhal), DNA methyltransferase 1 (DNMT1), DNA methyltransferase 3a (DNMT3a), DNA methyltransferase 3b (DNMT3b), METI, ZMET2, CMT1; periphery recruitment elements such as Lamin A and Lamin B; and functional domains thereof. CRISPR / Cas systems can also be used to activate gene expression, in an approach termed CRISPR activation (CRISPRa). CRISPRa constructs generally utilize a Cas protein to recruit more than one transcription activation domain with a single gRNA. The activation domains may promote transcription via: recruitment of other transcription factor proteins; modification of target DNA such as demethylation; recruitment of a DNA modifier; modulation of histones associated with target DNA; recruitment of a histone modifier such as those that modify acetylation and / or methylation of histones; or a combination thereof. For example, VP 16; VP64; VP48; VP 160; p65 subdomain (e.g., from NFkB); an activation domain of EDLL; TAL activation domain; histone lysine methyltransferases such as SET1A, SET1B, MLL1 to 5, ASH1, SYMD2, NSD1; histone lysine demethylases such as JHDM2a / b, UTX, JMJD3; histone acetyltransferases such as GCN5, PCAF, CBP, p300, TAF1, TIP60 / PLIP, MOZ / MYST3, MORF / MYST4, SRC1, ACTR, PI 60, CLOCK; DNA demethylases such as Ten-Eleven Atty. Docket No. UM-42504.601 Translocation (TET) dioxygenase 1 (TET1CD), TET1, DME, DML1, DML2, and ROS1; and functional domains thereof. The Cas protein can recruit repressor or activation domains using direct fusions or protein linkers (e.g., SunTag). Alternatively, activation domains can be recruited using nucleic acid approaches, a guide RNA having binding motifs (e.g., MS2) recruits effector domains fused to RNA-motif binding proteins. Any Cas protein that employs gRNA specific binding to bind to a specific target sequence can be utilized with the systems for CRISPRa and CRISPRi. Usually, a nuclease deficient version of a Cas protein is utilized, for example dCas9, a nuclease-dead Cas9 protein, but other Cas proteins can also be utilized in the methods herein, such as Cas3 and Cas12a. In some embodiments, transcription of the target gene is knocked out by administering a CRISPR / nuclease protein system, e.g., CRISPR / Cas9, referred to as CRISPR-KO. An insertion or deletion induced by a single guide RNA (gRNA) is often used to generate knock-out cells. For example, a guide RNA targets Cas9 to a target gene, where it creates a double-stranded break (DSB). Cells can survive a DSB when an error-prone repair mechanism like nonhomologous end joining (NHEJ) results in insertion or deletion of one or more base pairs, precluding further binding of the gRNA. Such repairs can result in frameshift mutations and thereby disrupt gene function, oftentimes resulting in functional knockouts. The CRISPR / Cas systems comprise a guide RNA specific to a target gene to be modulated. The target gene may be any of those listed in Tables 2 or 3, and the CRISPR / Cas system may comprise any of those gRNAs for CRISPRa, CRISPRi, and CRISPR-KO as indicated in Tables 2 or 3. The CRISPR / Cas systems, including Cas proteins and gRNAs, or polynucleotides encoding thereof, may be delivered by any suitable means. Methods of delivering polypeptides and polynucleotides to cells are well known in the art and may include DNA or RNA electroporation, transfection reagents such as liposomes or nanoparticles to delivery DNA or RNA; delivery of DNA, RNA, or protein by mechanical deformation (see, e.g., Sharei et al. Proc. Natl. Acad. Sci. USA (2013) 110(6): 2082-2087, incorporated herein by reference); or viral transduction. Nucleic acids can be delivered as part of a larger construct, such as a plasmid or viral vector, or directly, e.g., by electroporation, lipid vesicles, viral transporters, microinjection, and biolistics (high-speed particle bombardment). Similarly, polynucleotides can be delivered by Atty. Docket No. UM-42504.601 any method appropriate for introducing nucleic acids into a cell. In some embodiments, the polynucleotide is a DNA molecule. In some embodiments, the CRISPR / Cas system is provided in a DNA vector. In some embodiments, the CRISPR / Cas system is provided as an RNA molecule. Additionally, delivery vehicles such as nanoparticle- and lipid-based polynucleotide or protein delivery systems can be used. Further examples of delivery vehicles include lentiviral vectors, ribonucleoprotein (RNP) complexes, lipid-based delivery system, gene gun, hydrodynamic, electroporation or nucleofection microinjection, and biolistics. Various gene delivery methods are discussed in detail by Nayerossadat et al. (Adv Biomed Res. 2012; 1: 27) and Ibraheem et al. (Int J Pharm. 2014 Jan 1;459(1-2):70-83), incorporated herein by reference. The risk score may also be used for selection (e.g., inclusion or exclusion) for a clinical trial. For example, subjects with a specific risk score may be included for a clinical trial to specifically study those individuals at an increased risk for insulin resistance, e.g., a genetic enrichment trial. Alternatively, subjects with a specific risk score may be excluded for a clinical trial to avoid potential interference with clinical trial analysis. In some embodiments, the presence of such a polymorphisms or mutations can be regarded as indicative of an individual's risk (increased or decreased) for other diseases and conditions. The biological sample for analysis in the disclosed methods may be obtained from any suitable biological source, such as, a swab or brush, a physiological fluid including, but not limited to, whole blood, serum, plasma, interstitial fluid, saliva, ocular lens fluid, cerebral spinal fluid, sweat, urine, milk, ascites fluid, mucous, synovial fluid, peritoneal fluid, vaginal fluid, menses, amniotic fluid, semen, feces, and the like, or a tissue or cell sample including, but not limited to, hair, skin, blood, biopsies of the kidney, or liver or other organs or tissues, or sources such as saliva, cheek scrapings, urine, amniotic fluid or CVS samples. In some embodiments, the biological sample is selected from the blood, serum, plasma, saliva, tissue, hair, semen, or urine. The sample can be obtained from a subject using routine techniques known to those skilled in the art, and the sample may be used directly as obtained from the biological source or following a pretreatment to modify the character of the sample. Such pretreatment may include, for example, preparing plasma from blood, diluting viscous fluids, filtration, precipitation, Atty. Docket No. UM-42504.601 dilution, distillation, mixing, concentration, inactivation of interfering components, the addition of reagents, lysing, and the like. A “subject” or “patient” may be human or non-human and may include, for example, animal strains or species used as “model systems” for research purposes, such a mouse model as described herein. Likewise, patient may include either adults or juveniles (e.g., children). Moreover, patient may mean any living organism, preferably a mammal (e.g., human or non- human). Examples of mammals include, but are not limited to, any member of the Mammalian class: humans, non-human primates such as chimpanzees, and other apes and monkey species; farm animals such as cattle, horses, sheep, goats, swine; domestic animals such as rabbits, dogs, and cats; laboratory animals including rodents, such as rats, mice and guinea pigs, and the like. Examples of non-mammals include, but are not limited to, birds, fish, and the like. In one embodiment of the methods and compositions provided herein, the mammal is a human. In some embodiments, the subject is suspected of having insulin resistance. A polymorphism as described herein may be detected directly or indirectly. Direct detection methods may include inspecting a data set indicative of genetic characteristics derived from analysis of the individual's genome. A data set of genetic characteristics of the individual may include, for example, a listing of single nucleotide polymorphisms in the individual's genome or a complete or partial sequence of the individual's genomic DNA. Inspection of the data set including all or part of the individual's genome may optimally be performed by computer inspection. Screening may further comprise the step of producing a report identifying the individual and the identity of alleles at the site of at least one or more polymorphisms. Alternatively, the methods include obtaining and analyzing a nucleic acid sample (e.g., DNA or RNA) from an individual to determine whether the DNA contains informative polymorphisms, such as by combining a nucleic acid sample from the subject with one or more polynucleotide probes capable of hybridizing selectively to a nucleic acid carrying the polymorphism or sequencing the region of the DNA containing the polymorphisms. One skilled in the art will recognize that any one of the commonly available hybridization, amplification and array assay formats can readily be adapted to detect the polymorphisms disclosed herein. In some embodiments, the polymorphisms are detected by a sequencing assay. The sequence assay may be conducted by any means known in the art, such as the dideoxy chain termination method. In some embodiments, the sequencing assay is performed using high- Atty. Docket No. UM-42504.601 throughput sequence methods. Following sequencing, the data may be aligned or other analyzed for the presence of the polymorphisms. Any suitable method of alignment of sequences for comparison purposes may be utilized. In some embodiments, the polymorphisms may be detected by an amplification-based assay in which a polymorphism-specific primer hybridizes to a region on a target nucleic acid molecule that overlaps the polymorphism and only primes amplification of that form to which the primer exhibits perfect complementarity. This primer is used in conjunction with a second primer that hybridizes at a distal site. Amplification proceeds from the two primers, producing a detectable product that indicates the polymorphism is present in the test sample. A control is usually performed with a second pair of primers, one of which shows one or more mismatches at the polymorphic site and the other of which exhibits perfect complementarity to a distal site. The mismatches prevent amplification or substantially reduce amplification efficiency, so that either no detectable product is formed or it is formed in lower amounts or at a slower pace. Amplification assays are well-known in the art including polymerase chain reaction, ligase chain reactions, strand displacement assays, and the like. In a hybridization-based assay, probes can be designed that hybridize to a segment of target DNA from one individual but do not hybridize to the corresponding segment from another individual due to the presence of different polymorphic forms in the respective DNA segments. Hybridization conditions should be sufficiently stringent that there is a significant detectable difference in hybridization intensity, and preferably an essentially binary response, whereby a probe hybridizes to only one of the loci or significantly more strongly to one loci. A probe may be designed to hybridize to a target sequence that contains a polymorphism anywhere along the sequence of the probe. However, the probe is preferably designed to hybridize to a segment of the target sequence such that the polymorphism aligns with a central position of the probe (e.g., a position within the probe that is at least three nucleotides from either end of the probe). This design of probe generally achieves good discrimination in hybridization between different allelic forms. Indirect detection refers to determining the presence or absence of a specific polymorphism identified in the genetic profile by detecting a surrogate or proxy polymorphism that is in linkage disequilibrium with the SNP in the individual's genetic profile. Detection of a proxy polymorphism is indicative of a polymorphism of interest and is increasingly informative Atty. Docket No. UM-42504.601 to the extent that the polymorphisms are in linkage disequilibrium, e.g., at least 50%, 60%, 70%, 80%, 90%, 95%, 98%, or about 100% LD. Another indirect method involves detecting allelic variants of proteins accessible in a sample from an individual that are consequent of a risk- associated or protection-associated allele in DNA that alters a codon. Based on the polymorphisms and associated sequence information disclosed herein, detection reagents can be developed and used to assay any polymorphism of the present disclosure individually or in combination, and such detection reagents can be readily incorporated into a kit or system. The terms “kits” and “systems,” as used herein in the context of polymorphism detection reagents, are intended to refer to such things as combinations of multiple polymorphism detection reagents, or one or more polymorphism detection reagents in combination with one or more other types of elements or components (e.g., other types of biochemical reagents, containers, packages, substrates, electronic hardware components, etc.). Accordingly, the present disclosure further provides polymorphism detection kits and systems, including but not limited to, packaged probe and primer, arrays / microarrays of nucleic acid molecules, and beads that contain one or more probes, primers, or other detection reagents for detecting one or more polymorphisms of the present disclosure. The kits / systems can optionally include various electronic hardware components; for example, arrays (“DNA chips”) and microfluidic systems (“lab-on-a-chip” systems) provided by various manufacturers typically comprise hardware components. In some embodiments, a polymorphism detection kit typically contains one or more detection reagents and other components (e.g., a buffer, enzymes such as DNA polymerases or ligases, chain extension nucleotides such as deoxynucleotide triphosphates, and in the case of Sanger-type DNA sequencing reactions, chain terminating nucleotides, positive control sequences, negative control sequences, and the like) necessary to carry out an assay or reaction, such as amplification and / or detection of a polymorphism-containing nucleic acid molecule. A kit may further contain means for determining the amount of a target nucleic acid, and means for comparing the amount with a standard, and can comprise instructions for using the kit to detect the polymorphism-containing nucleic acid molecule of interest. In one embodiment of the present disclosure, kits are provided which contain the necessary reagents to carry out one or more assays to detect one or more polymorphisms disclosed herein. In a preferred embodiment Atty. Docket No. UM-42504.601 of the present disclosure, polymorphism detection kits / systems are in the form of nucleic acid arrays, or compartmentalized kits, including microfluidic / lab-on-a-chip systems. Polymorphism detection kits or systems may contain, for example, one or more probes, or pairs of probes, that hybridize to a nucleic acid molecule at or near each target position. Multiple pairs of allele-specific probes may be included in the kit / system to simultaneously assay large numbers of polymorphisms, at least one of which is a polymorphism of the present disclosure. In some kits / systems, the allele-specific probes are immobilized to a substrate such as an array or bead. For example, the same substrate can comprise allele-specific probes for detecting any or all of the polymorphisms described herein. A polymorphism detection kit or system of the present disclosure may include components that are used to prepare nucleic acids from a test sample for the subsequent amplification and / or detection of a polymorphism-containing nucleic acid molecule. Such sample preparation components can be used to produce nucleic acid extracts (including DNA and / or RNA), proteins or membrane extracts from any biological sample, as described herein. The terms “arrays,” “microarrays,” and “DNA chips” are used herein interchangeably to refer to an array of distinct polynucleotides affixed to a substrate, such as glass, plastic, paper, nylon or other type of membrane, filter, chip, or any other suitable solid support. The polynucleotides can be synthesized directly on the substrate, or synthesized separate from the substrate and then affixed to the substrate by methods known in the art. Any number of probes, such as allele-specific probes, may be implemented in an array, and each probe or pair of probes can hybridize to a different polymorphism position. In the case of polynucleotide probes, they can be synthesized at designated areas (or synthesized separately and then affixed to designated areas) on a substrate using a chemical process. Each DNA chip can contain, for example, thousands to millions of individual synthetic polynucleotide probes arranged in a grid-like pattern and miniaturized (e.g., to the size of a dime). Preferably, probes are attached to a solid support in an ordered, addressable array. Another form of kit contemplated by the present disclosure is a compartmentalized kit. A compartmentalized kit includes any kit in which reagents are contained in separate containers. Such containers include, for example, small glass containers, plastic containers, strips of plastic, glass or paper, or arraying material such as silica. Such containers allow one to efficiently transfer reagents from one compartment to another compartment such that the test samples and Atty. Docket No. UM-42504.601 reagents are not cross-contaminated, or from one container to another vessel not included in the kit, and the agents or solutions of each container can be added in a quantitative fashion from one compartment to another or to another vessel. Such containers may include, for example, one or more containers which will accept the test sample, one or more containers which contain at least one probe or other polymorphism detection reagent for detecting one or more polymorphisms of the present disclosure, one or more containers which contain wash reagents (such as phosphate buffered saline, Tris-buffers, etc.), and one or more containers which contain the reagents used to reveal the presence of the bound probe or other polymorphism detection reagents. The kit can optionally further comprise compartments and / or reagents for, for example, nucleic acid amplification or other enzymatic reactions such as primer extension reactions, hybridization, ligation, electrophoresis (preferably capillary electrophoresis), mass spectrometry, and / or laser- induced fluorescent detection. The kit may also include instructions for using the kit. Exemplary compartmentalized kits include microfluidic devices known in the art. In such microfluidic devices, the containers may be referred to as, for example, microfluidic “compartments,” “chambers,” or “channels.” Microfluidic devices and systems miniaturize and compartmentalize processes such as probe / target hybridization, nucleic acid amplification, and capillary electrophoresis reactions in a single functional device. Such microfluidic devices typically utilize detection reagents in at least one aspect of the system, and such detection reagents may be used to detect one or more polymorphisms of the present disclosure. Exemplary microfluidic systems comprise a pattern of microchannels designed onto a glass, silicon, quartz, or plastic wafer included on a microchip. The movements of the samples may be controlled by electric, electroosmotic, or hydrostatic forces applied across different areas of the microchip to create functional microscopic valves and pumps with no moving parts. Varying the voltage can be used as a means to control the liquid flow at intersections between the micro-machined channels and to change the liquid flow rate for pumping across different sections of the microchip. For genotyping polymorphisms, an exemplary microfluidic system may integrate, for example, nucleic acid amplification, primer extension, capillary electrophoresis, and a detection method such as laser induced fluorescence detection. In the first step of an exemplary process for using such an exemplary system, nucleic acid samples are amplified, preferably by PCR. Then, the amplification products are subjected to automated primer extension reactions using ddNTPs Atty. Docket No. UM-42504.601 (specific fluorescence for each ddNTP) and the appropriate oligonucleotide primers to carry out primer extension reactions which hybridize just upstream of the targeted polymorphism. Once the extension at the 3′ end is completed, the primers are separated from the unincorporated fluorescent ddNTPs by capillary electrophoresis. The separation medium used in capillary electrophoresis can be, for example, polyacrylamide, polyethyleneglycol or dextran. The incorporated ddNTPs in the single nucleotide primer extension products are identified by laser- induced fluorescence detection. The present disclosure also provides non-transitory computer-readable media. The non- transitory computer-readable media stores instructions that when executed by one or more processors performs some or all of the operations described in the disclosed methods. In some embodiments, the one or more processors perform operations comprising receiving data identifying the presence or absence of a polymorphism in a biological sample, generating a insulin resistance risk score from said data, and displaying or reporting said risk score. The methods described herein can be implemented by one or more processors and a computer-readable medium storing instructions executable by the one or more processors to perform operations, as described above. The least one computer system may comprise the one or more processors and / or the computer-readable media. The computer system may further comprise one or more local servers or databases connected to or integrated with the computer system. The one or more processors may be configured to communicate via wired or wireless communications with each other or other processors. The one or more processors may be configured to operate on one or more processor-controlled devices that can be similar or different devices. The readable media described herein may protect the confidentiality and security of protected health information (PHI) in compliance with various privacy standards (e.g., Health Insurance Portability and Accountability Act (HIPAA)). Thus, the readable media may be considered HIPAA-compliant. The readable media and / or the one or more processors may provide or allow one or all of: means of access control, mechanisms to authenticate electronic PHI, functionalities for encryption / decryption, and mechanisms to log activity and implement audits. Data may be communicated using known encryption / decryption and security techniques. For example, DICOM imaging standards support encryption. The system and methods may anonymize any protected subject data. Atty. Docket No. UM-42504.601 EXPERIMENTAL The following examples are provided to demonstrate and further illustrate certain embodiments of the present disclosure and are not to be construed as limiting the scope thereof. Example 1 Methods Data and genotyping. The UK Biobank (UKBB) contains genotype, clinical and demographic data of over 400,000 individuals aged 40-69 at the time of study recruitment. Protocols for participant genotyping, data collection and quality control have been described previously20,88. In brief, participants were genotyped on one of two purpose-designed arrays (UK BiLEVE Axiom Array (n = 50,520) and UKBB Axiom Array (n = 438,692)) with 95% maker overlap. The Haplotype Reference Consortium was used as a reference panel to phase and impute the data. EasyQC (ver. 9.2) was used for quality using an imputation quality cutoff of 0.85. The Michigan Genomics Initiative (MGI) is a hospital-based cohort containing genetic data and clinical phenotypes89. Participants were genotyped using the University of Michigan Advanced Genomics Core on one of two customized versions of the Illumina Infinium CoreExome-24 bead array platform. Imputation has been previously described90. Briefly, genotypes were imputed to both the Haplotype Reference Consortium (HRC) reference panel and the Trans-Omics for Precision Medicine (TOPMed) reference panel. Genome-wide association study (GWAS) and Conditional and Joint multiple-SNP analysis (COJO). The TG:HDL-C was calculated based on the serum triglycerides and HDL-C at time of enrollment respectively in the European individuals from the UKBB. European ancestry was genetically defined. No statistical method was used to predetermine sample size. First, a subgroup of individuals was chosen as European using the field 22006 of the UKBB (n1=409,605). This subgroup consisted of a list of subjects self-identified as ‘White British’ with similar genetic ancestry based on principal components. The individuals of the UKBB excluded Atty. Docket No. UM-42504.601 at the previous step were then projected, based on their genotype data, on a common ancestry space together with a reference sample of individuals of different ancestries using TRACE91. The application of a k-nearest neighbors algorithm by TRACE classified a further subset of individuals as European (n2=52,702). The primary cohort was the sum of the two groups (n1+ n2=462,307). Europeans were included in the analysis if their records did not show any missing information about triglycerides, HDL-C, age, sex and principal components 1-10, and if the genetic data were available (n=402,398). A linear mixed model was fit using SAIGE21(ver. 0.29) with rank-based inverse normal transformed TG:HDL-C as the dependent variable and age, age squared, sex and principal components 1-10 and SNPs as independent variables. The distribution of the TG:HDL-C was Normal given the rank-based inverse normal transformation. The effects of the SNPs were tested under an additive genetic model. Variants with an imputation cutoff < 0.85 or minor allele count < 3.5 were excluded. The LD Score regression intercept was quantified using ldsc92(ver. 1.0.1) and used to adjust the test statistic of the GWAS for inflation. After excluding multiallelic or ambiguous SNPs, indels, variants with a minor allele frequency < 0.01, and variants not available in the MGI, a COJO analysis22using GCTA (ver. 1.91.2) was used to extract independent SNPs having r2< 0.1. All data is presented for the TG:HDL-C increasing allele. Another linear mixed model for the rank-based inverse normal transformed TG:HDL-C was fit using the same variables and including also the 11-20 PCs. The LD Score regression intercept for the latter model was estimated as described above. Independent synonymous and intergenic SNPs in high LD with non-synonymous variants Starting from the results of the GWAS, variants having p>5e-08, that were > 500Kb from lead independent synonymous and intergenic SNPs, that had an imputation cutoff < 0.85, or that were multiallelic. Remaining variants were annotated using ANNOVAR93(build hg19, dbSNP150) were filtered out and the r2between non-synonymous variants and the lead independent synonymous or intergenic SNPs was calculated. Two variants were considered in high LD if r2> 0.8. In case an independent synonymous or intergenic SNP was in high LD with more non-synonymous variants, only the non-synonymous variant with the highest r2was reported. To check whether the total number of non-synonymous loci in high LD was due to chance, a number of SNPs throughout the genome equal to the number of intergenic and synonymous SNPs in the study 100 times were randomly sampled. Rare variants (MAF<0.01) Atty. Docket No. UM-42504.601 were excluded. For each iteration, the randomly selected SNPs were treated as if they were lead independent variants, repeated the process previously described with the exception of the p-value filter, and quantified the number of nonsynonymous SNPs in high LD. Mean and 2.5 and 9.5 quantiles were estimated from the empirical distribution of the number of nonsynonymous SNPs in high LD. Variant and gene annotation. The nearest gene to each variant was assigned using ANNOVAR. Prioritized genes were reported by DEPICT (FDR < 0.05), whether the gene is expressed in adipose subcutaneous, adipose visceral, adrenal gland, liver, muscle-skeletal, pancreas and uterus, whether the SNP had an eQTL with the indicated gene in subcutaneous fat, visceral fat, internal mammary artery, liver, aortic wall, skeletal muscle and blood, and whether the SNP was non-synonymous (or in high LD with a non-synonymous variant). A gene was considered expressed in a tissue if the median expression of the gene in the tissue was greater than twice the median of the expression of the gene across all the tissues. Median expression of the genes was obtained by the GTeX project94(v8). A SNP was considered in eQTL with a gene in a given tissue if the adjusted p- value provided by STARNET95(dbGaP accession phs001203.v1.p1) was less than 0.05. For each variant, the most likely causal gene was assigned using the following algorithm. Firstly, non-synonymous variants were assigned to their constituent gene. In case the SNP was synonymous or intergenic and not in LD with a non-synonymous variant, a candidate list was constructed consisting of the nearest gene and the genes prioritized by DEPICT. Subsequently, if DEPICT prioritized the nearest gene this gene was selected. Alternatively, each gene within the gene list was assigned 1 point (maximum 16 possible points) for A) expression in adipose subcutaneous, adipose visceral, adrenal gland, liver, muscle-skeletal, pancreas, and uterine tissue, or B) an eQTL in adipose subcutaneous, adipose visceral, adrenal gland, liver, muscle- skeletal, aortic wall, blood, or internal mammary artery tissue. The gene with the most points was selected and in case of ties, both genes were reported. Overlap between the variants associated with TG-HDL-C ratio and other genetic studies. To identify high-confidence IR-associated loci, it was determined which independent TG:HDL-C SNPs associated with insulin-related traits from the MAGIC and GENESIS Atty. Docket No. UM-42504.601 Consortia9-14,30. Studies were chosen where at least one trait under investigation was a quantity related to insulin, insulin resistance, insulin sensitivity, or insulin secretion, and the summary statistics for individuals of European ancestry were available. The analyzed traits were fasting insulin12-14, fasting insulin adjusted for BMI9,12,13, HOMA-IR13,14, HOMA-IR adjusted for BMI13, Insulin Sensitivity Index11, modified Stumvoll Insulin Sensitivity Index10, insulin sensitivity measured by hyperinsulinemic-euglygemic clamp30, insulin sensitivity measured by hyperinsulinemic-euglygemic clamp adjusted for BMI30, Corrected Insulin Response11and overall insulin response to glucose estimated as area under the curve for insulin over a total area under curve for glucose11. If a SNP associated with TG:HDL-C was not available in the summary statistics of a study, SNPs with an r2> 0.8 were identified in UKBB and the one with the high r2that was also present in the study was used as a proxy. To verify which allele of a proxy paired with the effect allele of a missing SNP, LDlink96was used. Once the proxies were identified, the p-values of proxies and available SNPs in the summary statistics were FDR- adjusted. A SNP was considered to overlap with a trait of the other study if its FDR-adjusted p- value was less than 0.05 in the other study. Overlap between the reported variants related to insulin in the MAGIC Consortium studies and the SNPs associated with TG:HDL-C. To check whether the previously reported variants9-14implicated with insulin-related traits from the MAGIC Consortium associated with TG:HDL-C, the summary statistics of those variants was examined in the GWAS. Since the reported variants of the GENESIS Consortium study did not reach genome-wide significance, the GENESIS Consortium study was excluded. All other reported variants were available except for rs73343765, rs200172871, and rs200678953, which were derived from Chan et al.9, a multi-ancestry meta-analysis. No proxy SNPs were found through LDlink therefore, the analysis was continued with the remaining 127. The summary statistics of the available variants were extracted from the GWAS results and the p-values adjusted using FDR. A variant was considered to overlap with the TG:HDL-C if its FDR-adjusted p-value in the GWAS was less than 0.05. Novelty of the SNPs for insulin resistance traits. Atty. Docket No. UM-42504.601 A conditional analysis of the inverse normal transformed TG:HDL-C for the TG:HDL-C independent SNPs running SAIGE (ver. 0.29) was performed including age, age squared, sex, principal components 1-10 and the dosages of the 127 previous reported variants associated with fasting insulin, HOMA-IR or any other index used to measure insulin resistance, insulin sensitivity, insulin secretion or insulin resistance from the MAGIC Consortium9-14as independent variables of the model. The reported variants of the GENESIS Consortium study were not included as none of them reached genome-wide significance. The independent SNPs reaching genome-wide significance after the conditional analysis were considered potentially novel. To confirm the novelty of the SNPs, previous studies of fasting insulin, HOMA-IR, insulin sensitivity, insulin secretion or other surrogate measures of insulin resistance were reviewed for the presence of those SNPs and the non-synonymous SNPs with an r2>0.8 to the lead SNPs. Enrichment analysis of the high-confidence SNPs associated with the TG:HDL-C. The high-confidence IR-associated loci were analyzed using DEPICT (ver. 1, release 173) to highlight the enrichment of tissues, cell types and gene sets and carry out a pathway analysis. Tissue and gene set enrichments with a false discovery rate FDR < 0.20 and pathways with a p-value < 1e-04 were considered statistically significant. The results of the pathway analysis were plotted using Cytoscape97(ver. 3.7.1). Associations between the high-confidence SNPs and cardiometabolic traits. It was assessed whether the high-confidence IR-associated variants associated with cardiometabolic traits related to insulin resistance. The GLGC31Consortium was used for TG, HDL-C and LDL-C, the GIANT32Consortium for body mass index (BMI) and waist hip ratio adjusted for BMI, the GOLDPlus38Consortium for NAFLD, the DIAGRAM33Consortium for type 2 diabetes (T2D), the MAGIC9Consortium for fasting glucose adjusted for BMI (Glucose) and FI adjusted for BMI (Insulin), and the summary statistics from Chen34et al., Hartiala35et al., Evangelou36et al.,Stanzick37et al. and Day39et al. respectively for alanine transaminase (ALT), myocardial infarction (MI), systolic blood pressure (SBP) estimated glomerular filtration rate (eGFR) and polycystic ovarian syndrome (PCOS). A Z-score was calculated for all the available variants in a study. If a variant was not available, the Z-score was set equal to 0. To cluster Atty. Docket No. UM-42504.601 variants, complete-linkage hierarchical clustering was applied using Pearson correlation as distance metric. A GWAS was performed for the rank-based inverse normal transformed TG:HDL-C in MGI to extract the effect sizes for the 114 high-confidence loci using SAIGE. Age, age squared, sex and 1-10 principal components were included in the model. For each cluster of variants, a PRS was created summing the dosages of the unrelated European individuals of the UKBB weighted by the effect sizes from MGI. In case of relatedness, only one subject per family was randomly chosen to create the PRS. Relatedness up to the second-degree was estimated using KING98(ver. 2.2.6). Individuals were subdivided based on the quartiles of PRS, and only the subjects in the top quartile were compared to the bottom quartile for associations with traits adjusted for sex, age, age squared, and principal components 1-10. Outcomes were reported in standard deviation and log odds ratio for continuous and binary traits, respectively. A gene-set enrichment analysis was performed on the genes of each cluster using FUMA99and used to assign a name to each cluster. Only the relevant databases for the name assignments were reported. TG:HDL-C polygenic risk score in the Michigan Genomics Initiative. The high-confidence IR-associated SNPs were combined in a polygenic risk score (PRS), which was calculated summing the dosages of 51,550 unrelated individuals of European ancestry from MGI weighted by the effect sizes of the loci from UKBB. A rank-based inverse normal transformation was applied to the PRS. The association between the PRS and the Phecodes in MGI was investigated by fitting a Firth’s logistic regression model using the PheWAS R package (ver. 0.99.5). The Phecodes were created from ICD codes. Age, age squared, sex and the first 10 principal components were included as predictors. An association was considered significant using a Bonferroni level significance adjusted for the number of traits tested (α*=0.05 / 1659=3e- 05). Variance explained by PRS in Michigan Genomics Initiative. To estimate the percentage of variance explained by the high-confidence TG:HDL-C loci, a linear regression was fit with inverse normal transformed TG:HDL-C as the outcome and the inverse normal transformed 114 SNP PRS from above and principal components 1-10 as the predictors. The adjusted R2of the model was used as an estimate of the explained variance. Atty. Docket No. UM-42504.601 Sex-specific analysis. To verify whether the SNPs might have a heterogeneous effect between male and female individuals, a sex stratified GWAS was performed separately for males (N=185,749) and females (N=216,649) in the UKBB using SAIGE. The outcome of the models was the inverse normal transformed TG:HDL-C, and the predictors were age, age squared and the 1-10 PCs. In both the GWASes, the intercept from the LD score regression was used to adjust the p-values for population stratification. The results were then meta-analyzed using METAL100(08 / 28 / 2018 release). A SNP was considered to have a heterogeneous effect if the heterogeneous p-value of the Cochran’s Q-test was less than 0.05. The genes of the SNPs which showed sex- heterogeneous effect were annotated separately by sex using FUMA99. Non-European ancestry analysis in the UKBB. A GWAS of the TG:HDL-C was performed for the individual of South Asian (N=8,158), African (N=6,632) and Chinese (N=1,300) ancestries in the UKBB. The same outcome and predictors described for the GWAS of the Europeans was used, and the same quality control were applied for both the individuals and the variants. For each GWAS, the genomic inflation factor was estimated and used to adjust the p-values. The independent SNPs for the South Asian and African ancestries were extracted using COJO. The independent SNPs for the Chinese ancestry were extracted using a 500Kb distance criterion, given that the small sample size of the cohort (n < 4,000) did not meet the recommended size to apply COJO. The r2was calculated among the independent SNPs having a distance < 500Kb across the different ancestries using LDlink. When more independent SNPs had a distance < 500Kb within the same ancestry, the closest SNP to the hits of the other ancestries was chosen. Linkage disequilibrium between two SNPs was defined as r2> 0.1. Statistics and reproducibility All significant variants in a UK Biobank cohort reached genome-wide significance (p<5e-8). To be reported as significant in a non-UK Biobank studies, a variant must reach an FDR-adjusted p-value<0.05. The p-values were adjusted using the Benjamini-Hochberg FDR procedure. Z-scores were used to visualize the significance of variants in external studies. Z- Atty. Docket No. UM-42504.601 scores were calculated from unadjusted p-values across all reported variants. Polygenic-risk scores were calculated for the UK Biobank as described in the Methods. The effect size for each PRS represents the association in standard deviation and log odds ratio for continuous and binary traits, respectively. PRS significance in the UK Biobank was determined using linear and logistic regression models. To identify significant tissues, cell types, and pathways an FDR-adjusted cutoff of 0.20 was used. A cutoff of p<1e-4 was used for DEPICT enriched gene sets. A polygenic-risk score was calculated for the MGI as described in the Methods. Associations between the PRS and MGI Phecodes were assessed using Firth’s logistic regression model. A Bonferroni-adjusted cutoff (α=3e-05) on a -log10 scale was used to assess significance. Results GWAS identifies 369 independent loci for TG:HDL-C Serum triglycerides and HDL-C levels were obtained around the time of enrollment for 402,398 Europeans from the UKBB (Fig. 1). These values were used to calculate the TG:HDL-C ratio (1.07 [0.66, 1.73])(Median [Q1 - Q3]) (Table 1). A GWAS fitting a linear mixed model using Scalable and Accurate Implementation of GEneralized mixed model21(SAIGE) was performed. The dependent variable was the rank-based inverse normalized TG:HDL-C and the independent variables were age, age-squared, sex, and the first 10 principal components. The estimated intercept of the LD score regression for this model was 1.5188. The inflation was GC- corrected for in the test statistic. Notably, incorporating 20 principal components into the model did not substantially reduce the value of the LD score regression intercept (1.4622). 32,573 variants reached genome-wide significance (p<5e-08) for TG:HDL-C after exclusion of INDELs, multiallelic / ambiguous SNPs, and SNPs not available in the Michigan Genomics Initiative (MGI). Table 8 shows genes with p values down to 1 x 10-4and their effects on related phenotypes. To extract a list of independent SNPs, Conditional and Joint multiple-SNP analysis22(COJO) was applied. COJO identified 369 genome-wide significant SNPs with a minor allele frequency ≥ 0.01 (Table 2). For each of these 369 SNPs the most likely causal gene was determined using an algorithm incorporating proximity, DEPICT prioritization, tissue expression, and eQTLs. 22 of these 369 SNPs were non-synonymous. For the remaining 347 SNPs, it was checked whether they were in high linkage disequilibrium (LD) with a non- Atty. Docket No. UM-42504.601 synonymous variant associated with TG:HDL-C at genome-wide significance. Using an r2> 0.8 and a distance criterion of 500Kb as threshold, 35 synonymous and intergenic SNPs were identified in high LD with a non-synonymous variant. To determine whether these 35 non- synonymous loci in high LD with a synonymous variant were identified by chance, 347 SNPs with EAF≥0.01 and EAF≤0.99 were randomly sample and the number of non-synonymous SNPs with an r2>0.8 and within 500kb was quantified. Across 100 iterations of this process, 19.63 (95% CI: 12.47-28.53) non-synonymous SNPs were recovered on average. This indicates that A) the likelihood of recovering 35 non-synonymous variants in high LD at random is very low (p<0.01), and B) that many of these non-synonymous SNPs are biologically relevant variants. Thus, 57 total non-synonymous loci were identified. 3 of these 57 SNPs lie within genes previously identified as likely causal genes through GWAS of other insulin resistance markers9-14. This includes GCKR, FAM13A, JMJD1C. FAM13A is closely tied to the regulation of adipogenesis and adipocyte function23,24. GCKR is known to regulate glucose metabolism25. Additionally, many of the identified non-synonymous variants represent links between glucogenic and lipogenic metabolism. APOA4, APOB, and PNPLA2 regulate triglyceride levels and are directly controlled by insulin26-29. Thus, many of the 57 non-synonymous loci correspond to genes with prominent roles in nutrient metabolism. 318 of 369 SNPs have not been previously reported for IR To establish which of the 369 identified TG:HDL-C loci have previously unreported associations with insulin resistance, a conditional analysis incorporating the 130 variants from IR-associated traits reported by the MAGIC Consortium studies was performed9-14. SAIGE was run on the inverse normal transformed TG:HDL-C including the MAGIC variants as independent variables in the model in addition to the same covariates used in the original analysis. The estimated intercept of the LD score regression for this model was 1.4975, and the distribution of the test statistic was adjusted accordingly. 322 of the 369 SNPs remained genome-wide significant (p<5e-08) after conditional analysis. To confirm none of these 322 SNPs were previously reported for insulin resistance, previous studies of fasting insulin, HOMA-IR, insulin sensitivity, insulin secretion, or other surrogate measures of insulin resistance were reviewed for the presence of these 322 SNPs. No associations were found between 319 of the 322 SNPs, while 3 SNPs (rs13107325, rs72959041, rs8101064) were reported in literature. Furthermore, Atty. Docket No. UM-42504.601 rs3810291 may be in LD with rs200172871, a variant previously associated with fasting insulin adjusted for BMI9. Thus, of the 369 original SNPs reaching genome-wide significance for TG:HDL-C, 318 have not been previously reported for insulin resistance. 114 of the 369 independent loci are high-confidence IR loci To verify the ability of the TG:HDL-C to capture known insulin resistance biology, it was tested whether the TG:HDL-C encompassed the 130 IR-associated loci previously reported in the MAGIC Consortium studies9-14. Of the 130 loci, 127 were present in the UKBB. 92 of the 127 (72%) previously reported variants showed an FDR-adjusted p-value<0.05 in the summary statistics of the study. 57 of the 127 variants (45%) reached genome-wide significance. Next, it was tested whether any of the 369 independent TG:HDL-C variants met an FDR-adjusted p- value<0.05 in the summary statistics of the MAGIC or GENESIS consortia studies9-14,30. If a SNP was not available in the summary statistics, a proxy was used when available. 114 of the 369 independent loci met an FDR-adjusted p-value<0.05 in at least one of the analyzed traits related to insulin resistance. These 114 SNPs are thus high-confidence IR-associated loci having met genome-wide significance (p<5e-08) for TG:HDL-C and an FDR-adjusted p-value<0.05 in at least one independent study of an IR-related trait (Fig. 1A, Fig. 2A, Table 2). 111 of these 114 high-confidence loci overlapped specifically with fasting insulin, fasting insulin adjusted for BMI, or HOMA-IR (Fig. 2B, Table 3). Of these 114 loci, 72 have not been previously reported for insulin resistance. Predicted causal genes have been previously associated with the constituent traits of metabolic syndrome (MetS) including obesity, waist hip ratio, dyslipidemia, and Type 2 Diabetes. Additionally, it was examined how many of the 114 SNPs had a Bonferroni-adjusted p-value<0.05 in other publicly available studies of metabolic traits9,31-38. Over 40% of these SNPs were significantly associated with LDL-C (47 / 114), T2D (62 / 114), waist hip ratio (69 / 114), ALT (61 / 114), and systolic blood pressure (49 / 114) (Table 4). Although these 114 high-confidence IR-associated loci reached significance in the summary statistics of the MAGIC / GENESIS consortia, the remaining 255 SNPs not meeting significance remain useful, given their association with numerous metabolic traits. Thus, while all 369 SNPs represent targets for insulin resistance biology, the 114 high-confidence IR-associated SNPs were prioritized for further analysis. Atty. Docket No. UM-42504.601 PheWAS identifies distinct effects within metabolic traits Although these 114 high-confidence IR-associated loci are associated with an increased TG:HDL-C ratio, whether they display uniform effects on other IR-associated phenotypes was unknown. A Phenome-wide association study (PheWAS) of the 114 SNPs on 14 traits related to metabolism, body composition, and metabolic syndrome-associated phenotypes in publicly available cohorts9,31-39was performed (Table 4). Unsupervised clustering was used to find 5 distinct subgroups of SNPs with variable effects on subsets of these traits (Fig. 3A, Fig. 8, Table 2). The biological function of these clusters was interrogated using Gene Set Enrichment Analysis in several databases and named based on the significant biological processes identified. Next, for each subgroup of SNPs a polygenic risk score (PRS) summing the dosages of the European individuals in the UKBB weighted by the effect sizes of the SNPs in MGI was generated. Subsequently, the ability of those PRSs to predict the constituent traits in the UKBB was examined (Fig. 3B). It was found that the subgroups had different patterns of effects on insulin related traits such as BMI, waist hip ratio, serum lipids, liver fat, and estimated glomerular filtration rate. All subgroups were associated with increased triglycerides and lowered HDL which was the primary phenotype investigated. The insulin / growth group associated with increased LDL Cholesterol (LDL-C), systolic blood pressure (SBP), waist to hip ratio (WHR), Type 2 diabetes (T2D), and alanine aminotransferase (ALT) but decreased body mass index (BMI). The carbohydrate homeostasis subgroup had a similar pattern but associated with decreased T2D risk. The adipogenesis subgroup was also similar to the insulin / growth subgroup but had non-significant effects on LDL-C and WHR. The Lipid homeostasis and Brain processes subgroups associated with increased BMI, T2D, and ALT. However, the Lipid homeostasis subgroup and Brain processes associated with increased and decreased WHR respectively. The differential effects of each variant across traits recapitulates the known complexity of disease within insulin resistant individuals. High-confidence IR loci enrich for insulin-related biology The 114 high-confidence IR-associated SNPs were annotated using DEPICT to identify the enrichment in tissues, cell types, gene sets (Fig. 4). Consistent with previous findings for SNPs associated with FI9, the 114 loci show robust enrichment in adipose tissues (Figs. 4A-B). Atty. Docket No. UM-42504.601 Enriched physiological systems correspond to those traditionally affected by MetS including the cardiovascular system (aortic / heart valves), digestive system (liver / pancreas), musculoskeletal system (joints / synovial membrane), and the female urogenital system (myometrium / uterus / fallopian tubes) (Fig. 4C). Unlike previous genetic studies of insulin resistance, the liver was identified in tissue enrichment. As insulin resistance is the result of an altered response of hepatocytes to insulin and insulin resistance is critical in the pathogenesis of NAFLD, this association points toward a shared genetic underpinning. DEPICT was also used to perform pathway analysis which identified 3 distinct sub- networks related to growth, metabolism, and abnormal lipid homeostasis (Fig. 5, Supp. Table 16). Insulin is a key regulator of energy metabolism and growth. The growth-related sub-network is enriched for several pro-growth protein-protein interaction networks including EP300, CREBBP, SMAD1, SMAD3, ESR1, and IGF1R. Another subnetwork present contains nodes related to metabolism of proteins and phosphorus containing compounds. Protein synthesis is traditionally promoted by insulin in an insulin sensitive state47. However, this anabolic process can be impaired in individuals with obesity48. Finally, the last subnetwork centers on abnormal lipid homeostasis and encompasses established links between glucogenic metabolism, lipogenic metabolism, and body mass. Collectively, these enrichment studies highlight the biological mechanisms underlying insulin resistance. Using the 369 TG:HDL-C SNPs provided increased power further highlighting enrichment in these pathways and tissues in addition to others. 31 high-confidence loci have sex-specific effects To evaluate whether any of the 114 high-confidence IR-associated loci displayed sex- specific effects, GWASs was run for TG:HDL-C in males and females separately. The estimated intercept of the LD score regression was 1.1985 and 1.3047 for males and females, respectively. The relative distributions of the test statistics were adjusted for each sex. The GWAS results were then meta-analyzed (Table 5). 76 of the 369 independent TG:HDL-C loci showed a statistical heterogeneous effect (HetPVal < 0.05) while 31 of the 114 high-confidence IR- associated loci met this criterion. 24 of the 31 (77%) high-confidence IR-associated loci with sex-specific effects displayed a stronger effect on TG:HDL-C in females when compared to males. The top loci showing a stronger sex-specific effect in females mapped to genes including KLF14, ZCCHC8, LINC01625, and RSPO3. Conversely, loci mapping to LPL / SLC18A1, Atty. Docket No. UM-42504.601 LOC646736, ARL15, and FNIP1 showed a stronger effect in males. The 24 SNPs with stronger sex-specific effects in females were enriched for loci significantly associated with WHR adjusted for BMI (FDR-adjusted p = 7.59e-20). One of these loci (rs10260148) maps to the transcription factor KLF14 and also shows the strongest sex-specific effect in females. Previous studies of other SNPs mapping to KLF14 have reported sex-specific associations with metabolic traits including T2D, WHR, triglycerides, HDL-C, and LDL32,33,49-51. The stronger association in females is hypothesized to be driven by modulation of KLF14 expression, rather than through hormonal means49,52. 11 TG:HDL-C SNPs identified non-European ancestries To determine the role of the 114 high-confidence IR-associated SNPs in non-European ancestries, a GWAS of the TG:HDL-C was performed for individuals of South Asian (SAS), African (AFR) and Chinese (CHI) ancestry in the UKBB (Tables 5-7). The estimated genomic inflation factor was 1.0494 (SAS), 1.0393 (AFR) and 1.0007 (CHI) and p-values were adjusted accordingly. The independent SNPs were identified using COJO for the GWAS results of the South Asian and African cohort. As the Chinese cohort is below the recommended sample size for applying COJO, a distance criterion of 500Kb was used instead. Between the 3 ancestries 11 total loci (SAS:6, AFR:4, CHI:1) were identified. All the 11 loci identified in non-European ancestries were located within 500Kb of one of the 369 independent loci for TG:HDL-C and 6 / 11 were within 500kb of a high-confidence IR-associated loci indicating that one is identifying loci common for effects on insulin resistance across ancestries. The majority (7 / 11) of the SNPs identified from non-European ancestries were in LD (r2> 0.1) with the nearest of the European ancestry-derived independent loci for TG:HDL-C). The four loci (rs15285, rs326, rs3135506, rs12721054) which failed to meet the r2threshold for linkage disequilibrium did not associate with insulin traits in the MAGIC Consortium (p>0.05), so they do not meet criteria for being high-confidence IR-associated loci. Therefore, there were no statistically significant ancestry specific high-confidence loci identified. High-confidence PRS associates with cardiometabolic traits To evaluate the joint effect of the high-confidence 114 IR-associated loci overall on the risk of disease a polygenic risk score (PRS) was created and tested for its association with Atty. Docket No. UM-42504.601 Phecodes on 51,550 individuals of European ancestry of the Michigan Genomics Initiative (MGI) (Fig. 6). The PRS was significantly associated with phenotypes used to identify MetS including hyperglyceridemia (padj=8.91e-41), hyperlipidemia (padj=1.26e-30), and hypertension (padj=1.27e-17). Additionally, the PRS was associated with well-established sequelae of MetS including coronary atherosclerosis (padj=1.27e-07) and chronic liver disease / cirrhosis (padj=1.43e- 07). Obesity failed to reach significance (p=1). Other notable associations included disorders of lipid metabolism (padj=7.01e-31) and T2D (padj=1.07e-19). The association of the PRS with insulin resistance and metabolic syndrome-related sequelae in an independent cohort further solidifies the contribution of these loci to metabolic disease and its subsequent morbidity. Table 1. Baseline characteristics of the European population in the UK Biobank (A) and the Michigan Genomic Initiative (B). A Trait Units N Median (Q1,Q3) Frequency (N)DemographicsFemale % 402398 - 53.84 (216649)Age year 402398 68 (61, 73) - B Trait Units N Median (Q1,Q3) Frequency (N)DemographicsFemale % 51550 - 52.77 (27205) Table 2. Summary statistics of the 369 independent SNPs associated with the TG:HDL-C at a genome-wide level of significance in the European population. rsIDaEA OA P Locus Annotation HERPUD1(N, D); CETP(D, EAS, EAG, rs72786786 G A 0.0028 HERPUD1 EMS, EL, EAV, QL) LPL(D, EAS, EMS); rs7015766 C T 0.0050 LPL,SLC18A1 SLC18A1(N, EAG, EAV) Atty. Docket No. UM-42504.601 CETP(N, D, EAS, EAG, EMS, EL, EAV, QL); rs7499892 T C 0.0033 CETP HERPUD1(D) rs11824135 T G 0.0051 LINC02702 LINC02702(N) rs254 C G 0.0036 LPL LPL(N, EAS, EMS, QB) rs6589567 A C 0.0041 APOA5 APOA5(N, EP, EU, EL) rs480823 C T 0.0048 LINC02702 LINC02702(N) rs6999569 A G 0.0026 TRIB1 TRIB1(N, D, EL) GCKR(N, X, D, EAS, EAG, EMS, EL, EAV, QL); CAD(D, EU); EMILIN1(D, EU); ENSG00000234945(D); GTF3C2(D); KHK(D, EU, EL); NRBP1(D, QAW); PREB(D, EU); SLC5A6(D, EU, EL); rs1260326 T C 0.0026 GCKR ZNF513(D) MLXIPL(N, D, EAS, EAG, EMS, EL, EAV, QAS, QAV); BAZ1B(D); rs17145750 C T 0.0035 MLXIPL BCL7B(D) APOC1(N, D, EAS, EAG, EMS, EL, EAV); APOE(D, EAS, EAG, EMS, EL, EAV); rs483082 T G 0.0030 APOC1 PVRL2(D) APOB(N, X, D, EAS, rs676210 G A 0.0032 APOB EMS, EL) rs10779835 T C 0.0026 GALNT2 GALNT2(N, D, QL) rs268 G A 0.0095 LPL LPL(N, X, EAS, EMS) rs276 C T 0.0096 LPL LPL(N, D, EAS, EMS) PCIF1(D, EU); PLTP(N, EAS, EAG, EMS, EU, EAV, QMS, QL, QAW); rs6073958 C T 0.0032 PLTP ZNF335(D) FADS1(D, EAG, EAV, QAS, QMS, QL, QAV, QAW, QB, QIMA); FADS2(D, EAG, EU, EAV, QL, QAW, QB, QIMA); TMEM258(N, rs102275 C T 0.0027 FADS1 QAV) ANGPTL4(N, X, D, EAS, rs116843064 G A 0.0092 ANGPTL4 EMS, EL); RAB11B(D) Atty. Docket No. UM-42504.601 CD300LG(N, X, D, EAS, EMS, EU); HDAC5(D); LSM12(D); NAGS(D, EL); PPY(D, EP, EAG, rs72836561 T C 0.0073 CD300LG EAV); SOST(D) ANGPTL3(D, EU, EL, QL); DOCK7(N, QAW, rs10889333 G A 0.0027 ANGPTL3 QB); USP1(D) CATSPER2P1(N); CTDSPL2(D); MAP1A(X*); rs139974673 C T 0.0080 MAP1A ZSCAN29(D) rs998584 A C 0.0026 VEGFA VEGFA(N, D, EU, EL) TM6SF2(N, X, D, EL); ATP13A1(D, QAS); GATAD2A(D); GMIP(D); MAU2(D, rs58542926 C T 0.0048 TM6SF2 QMS) APOC1(D, EAS, EAG, EMS, EL, EAV); APOC1P1(N, EAS, EAG, EMS, EL, EAV, QL); APOE(D, EAS, EAG, EMS, EL, EAV); rs8106813 G A 0.0027 APOC1P1 PVRL2(D) rs2943645 T C 0.0027 LOC646736 LOC646736(N) COBLL1(N, EAS, EAG, EMS, EL, EAV); GRB14(D, EP, EAG, EL, rs13389219 C T 0.0026 GRB14 EAV, QAS, QAV) APOA4(N, X, EP, EAG, rs12721043 C A 0.0121 APOA4 EU, EL, EAV) C5orf67(N); MAP3K1(D); MIER3(D, rs9687846 A G 0.0032 MIER3 EU) ACP2(X*, D, QMS, QL, QAV, QB); DDB2(N, QB); MYBPC3(D, EAG, EAV); NR1H3(D, EAS, rs326222 C T 0.0028 ACP2 EMS, EL, QAS, QL) rs72647336 A G 0.0059 TRIB1 TRIB1(N, D, EL) rs2925979 T C 0.0028 CMIP CMIP(N, D, QAS) AFF1(N, D, EU, QAS, rs3775228 T C 0.0026 AFF1 QIMA) rs10773049 T C 0.0026 ZNF664-RFLNA ZNF664-RFLNA(N) LPL(D, EAS, EMS); rs73667496 C T 0.0058 LPL,SLC18A1 SLC18A1(N, EAG, EAV) Atty. Docket No. UM-42504.601 MLXIPL(N, D, EAS, EAG, EMS, EL, EAV); rs799157 T C 0.0062 MLXIPL BAZ1B(D); BCL7B(D) SIK3(N, D); APOA1(D, EU, EL); APOA4(D, EP, EAG, EU, EL, EAV); APOA5(D, EP, EU, EL); rs61907602 G C 0.0071 SIK3 APOC3(D, EL) rs117794084 T G 0.0101 LINC02702 LINC02702(N) CCDC92(X*, QAS, QMS, QL, QAV); DNAH10(N, QAS, QAV, rs11057397 C T 0.0027 CCDC92 QAW, QIMA) rs9472125 C T 0.0042 VEGFA VEGFA(N, EU, EL) rs72655677 G A 0.0061 TRIB1 TRIB1(N, D, EL) CITED2(D, EAS, EMS, EU); LINC01625(N, EAS, EP, EAG, EMS, EL, rs632057 T G 0.0026 LINC01625 EAV) rs2074493 C A 0.0030 HLA-C HLA-C(N, X) rs392794 T C 0.0029 C5orf67C5orf67(N)rs2068888 G A 0.0026 CYP26A1 CYP26A1(N, EL) ARID1A(N, D); FCN3(D, EAS, EP, EAG, EMS, EL, EAV); NR0B2(D, EP, EAG, EL, EAV); rs114165349 C G 0.0085 ARID1A WDTC1(D) LPL(D, EAS, EMS); rs55682243 C G 0.0083 LPL,SLC18A1 SLC18A1(N, EAG, EAV) APOC1(D, EAS, EAG, EMS, EL, EAV); APOC1P1(N, EAS, EAG, EMS, EL, EAV); APOE(D, EAS, EAG, EMS, EL, EAV); rs60049679 C G 0.0052 APOC1,APOC1P1,APOE PVRL2(D) LPA(N, D, EP, EAG, EU, EL, EAV, QL); MAP3K4(D, EU); PLG(D, EU, EL, QL); SLC22A3(D, EAG, EU, rs11751347 T C 0.0042 LPA EL, EAV) PINX1(N); SOX7(D, rs7821812 C G 0.0031 SOX7 EAS, EMS) rs7826687 G C 0.0028 TRIB1 TRIB1(N, D, EL) KLF14(N, EAS, EP, EAG, rs10260148 T C 0.0028 KLF14 EMS, EU, EAV) Atty. Docket No. UM-42504.601 PCIF1(D, EU); PLTP(N, EAS, EAG, EMS, EU, EAV, QAS, QMS, QAV, QAW, QIMA); rs435306 T G 0.0029 PLTP ZNF335(D) HLA-DQA1(N, EAS, EMS, QAS, QMS, QL, QAV, QAW, QB, QIMA); HLA-DQB1(X*, EAS, EMS, QAS, QMS, QL, QAV, QAW, QB, rs114786106 G A 0.0066 HLA-DQB1 QIMA) rs3768321 T G 0.0032 PABPC4-AS1 PABPC4-AS1(N) rs7012814 G A 0.0026 LOC157273 LOC157273(N) JMJD1C(N, X*, D, rs7924036 G T 0.0025 JMJD1C QIMA); REEP3(D) VEGFB(N, D, EAS, EMS); FERMT3(D, EAS, EMS); MACROD1(D); rs56271783 C G 0.0062 VEGFB PRDX5(D); STIP1(D) rs13107325 T C 0.0048 SLC39A8 SLC39A8(N, X, D, EP) PCCB(N, D, EAG, EL, EAV, QAS, QL, QAV); MSL2(D, QL); rs684773 C A 0.0030 PCCB STAG1(D, EU) rs519000 T C 0.0034 LINC02702 LINC02702(N) rs632557 A G 0.0064 GALNT2 GALNT2(N, D) SIK3(N, D); APOA1(D, EU, EL); APOA4(D, EP, EAG, EU, EL, EAV); APOA5(D, EP, EU, EL); rs11216236 T C 0.0062 SIK3 APOC3(D, EL) HLA-DQA1(N, EAS, rs11751024 A C 0.0026 HLA-DQA1 EMS, QAS, QAV) rs12928099 C A 0.0028 PDXDC1 PDXDC1(N, QAS, QMS) INHBC(X*, D, EL); INHBE(D, EL); rs2122982 G A 0.0030 INHBC R3HDM2(N); STAC3(D) GPAM(N, X, D, EAS, rs2792751 C T 0.0029 GPAM EAG, EMS, EL, EAV) CAD(D, EU); EMILIN1(D, EU); ENSG00000234945(D); GCKR(D, EAS, EAG, EMS, EL, EAV); GTF3C2(D); KHK(D, EU, rs6714780 A G 0.0057 GCKR EL); MAPRE3(N); Atty. Docket No. UM-42504.601 NRBP1(D); PREB(D, EU); SLC5A6(D, EU, ; rs6800707 G C 0.0033 NISCH ; ; EU, rs62131877 C T 0.0058 GCKR rs73243877 G A 0.0034 RBPJ ; rs72836567 A G 0.0035 CD300LG rs62117489 C A 0.0055 ANGPTL4 rs11045171 A G 0.0032 PDE3A rs355849 T C 0.0041 COBLL1 rs2267373 T C 0.0026 MAFF ; ; rs115961201 G A 0.0068 GCKR Atty. Docket No. UM-42504.601 NRBP1(D); PREB(D, EU); SLC5A6(D, EU, EL); ZNF513(D) LINC01214(N, EL); rs62271373 A T 0.0055 LINC01214,TSC22D2 TSC22D2(D, EU) APOC1(N, D, EAS, EAG, EMS, EL, EAV); APOE(D, EAS, EAG, EMS, EL, EAV); rs1064725 T G 0.0066 APOC1 PVRL2(D, QL) LPA(N, D, EP, EAG, EU, EL, EAV); MAP3K4(D, EU); PLG(D, EU, EL); SLC22A3(D, EAG, EU, rs140570886 T C 0.0105 LPA EL, EAV) PSKH1(N, D); CENPT(D); CTRL(D, EP); EDC4(D); ESRP2(D, EP, EL); LCAT(D, EU, EL, QB); NFATC3(D, QIMA); NRN1L(X*); PLA2G15(D, QAS, QMS, QL); RANBP10(D); SLC12A4(D, EU); rs55781197 A G 0.0040 NRN1L SLC7A6(D, EAS, EMS) NTAN1(X*, EAS, EMS, QIMA); PDXDC1(N, rs4074872 A G 0.0032 NTAN1 QMS, QB) CLIP1(D); HCAR1(D, EAS, EMS, QAS, QAV, QIMA); MIR9902-1(N); rs526276 C T 0.0033 HCAR1 RSRC2(D); ZCCHC8(D) HLA-B(N, EAS, EMS, rs2394987 A C 0.0040 HLA-B EU, QAW, QIMA) rs4722551 T C 0.0035 MIR148A MIR148A(N) INSR(N, D, EP, EU, rs4804413 T C 0.0026 INSR QIMA) SUGP1(N); rs188247550 C T 0.0116 TM6SF2 TM6SF2(X*, EL) SCARB1(N, D, EAS, rs921919 A G 0.0028 SCARB1 EAG, EMS, EL, EAV) rs10242866 T C 0.0026 SNX13 SNX13(N, QAV) PPARG(N, D, EAS, EMS, EU); TIMP4(D, rs2067819 G A 0.0031 PPARG EAS, EMS) Atty. Docket No. UM-42504.601 GAS6(D, EU, QL); rs7140110 C T 0.0028 GAS6 GAS6-AS1(N, EU) C12orf65(N, EU, QAV); PITPNM2(D); SBNO1(D, QAW, QIMA); SETD8(D, QAS, rs10773000 G T 0.0027 SETD8 QMS, QIMA) rs11215942 A G 0.0061 LINC02702LINC02702(N)RSPO3(N, D, EAG, EU, rs72959041 A G 0.0060 RSPO3 EAV, QAS) FFAR4(N, EAS, EP, EAG, EMS, EAV, QAS); rs10786114 C T 0.0038 FFAR4 O3FAR1(D) rs1970696 T C 0.0033 LIPG LIPG(N, D, EL) rs1030472 G A 0.0031 EBF1 EBF1(N, EAS, EMS, EU) ENSG00000228536(D); rs1538742 A C 0.0026 LYPLAL1-AS1 LYPLAL1-AS1(N, EU) rs4646250 G A 0.0028 NAT2 NAT2(N, D, EU, EL) GATA4(N, D, EP, EAG, rs7386288 T C 0.0029 GATA4 EU, EL, EAV)rs4969179 T G 0.0026 PGS1PGS1(N, QAW, QIMA)rs62102718 T A 0.0028 PEPD PEPD(N, QAS, QMS) LACTB(D, EAG, EAV, QAS, QMS, QAV, QAW, QB); TPM1(N, rs2937859 G A 0.0032 LACTB EU, QAS, QIMA) rs13108218 A G 0.0026 HGFAC HGFAC(N, D, EL) rs114760566 A C 0.0062 HMGA1 HMGA1(N) MLX(X*, D, QB); COASY(D); FAM134C(D, QIMA); NAGLU(D, QAS, QL, QAV, QAW); PLEKHH3(D, EU); PTRF(D, EAS, EMS, rs12938909 G C 0.0028 MLX EU); TUBG1(N) DR1(N, D, QL, QAW, QB, QIMA); CCDC18(X*, EAS, EMS, QIMA); TMED5(D, rs1365297 A G 0.0033 CCDC18 QMS, QIMA) TNFAIP8(N, D, EAS, rs1045241 C T 0.0029 TNFAIP8 EMS, QAS, QAV, QB) GPAM(N, D, EAS, EAG, rs4306236 T G 0.0036 GPAM EMS, EL, EAV) Atty. Docket No. UM-42504.601 CEP68(N, EAS, EMS, EU, QAS, QMS, QL, rs2723065 A G 0.0026 CEP68 rs3820897 C T 0.0033 COLEC11 ; ; rs10750766 A C 0.0028 KAT5 rs6882076 C T 0.0026 TIMD4 rs9817452 G T 0.0026 LINC00880 rs7786102 G A 0.0028 MIR148A rs12451511 G T 0.0032 BPTF (D, rs7215055 G A 0.0053 PEMT rs2773469 A G 0.0029 ADRB1 rs71603401 G A 0.0037 LCORL rs367070 A G 0.0031 LILRA3 rs1534696 C A 0.0026 SNX10 rs646776 T C 0.0031 CELSR2 rs77348928 G A 0.0036 LPA rs11664369 T C 0.0029 PMAIP1 rs3794695 T C 0.0032 HP rs61888800 T G 0.0029 BDNF rs3934667 G T 0.0028 MAU2 Atty. Docket No. UM-42504.601 MTARC1(N, X); rs2642438 A G 0.0028 MTARC1 MOSC1(D) USP3(N, D); HERC1(D, rs17184382 A C 0.0026 USP3 QAS, QAV) APOA1(D, EU, EL); APOA4(D, EP, EAG, EU, EL, EAV); APOA5(D, EP, EU, EL); APOC3(D, rs142334475 G A 0.0119 APOA4 EL); PCSK7(N); SIK3(D) rs75073260 T G 0.0037 LINC00452 LINC00452(N) TAOK2(N, D, QB); INO80E(D, QAS, QAW, rs3814883 T C 0.0026 TAOK2 QB); PPP4C(D) rs2000813 C T 0.0028 LIPG LIPG(N, X, D, EL) CYP27A1(D, EAG, EL, EAV); TTLL4(N); rs148358468 A G 0.0059 CYP27A1 VIL1(D, EP, EL) FGF21(N, D, EAG, EL, EAV); RASIP1(D, EAS, rs838133 A G 0.0026 FGF21 EMS, EU) LPA(N, D, EP, EAG, EU, EL, EAV); MAP3K4(D, EU); PLG(D, EU, EL); SLC22A3(D, EAG, EU, rs10455872 A G 0.0047 LPA EL, EAV) RAB11B(N, D); ANGPTL4(D, EAS, EMS, rs2967614 G A 0.0026 RAB11B EL) RSPO3(N, D, EAG, EU, rs2800709 G T 0.0025 RSPO3 EAV) ZCCHC8(N, D); CLIP1(D); HCAR1(D, EAS, EMS, QAS, QAV); rs116896792 C T 0.0044 ZCCHC8 RSRC2(D) GPIHBP1(N, D, EAS, rs7832515 A G 0.0033 GPIHBP1 EMS, EU) DDX17(D, EU); FAM227A(N, EU, QAV, QAW); GTPBP1(D, QL); rs5757161 A G 0.0027 FAM227A SUN2(D, EU) AMBRA1(N, D); ARHGAP1(D, EAS, rs55971594 A G 0.0058 AMBRA1 EMS, EU); F2(D, EL) rs2115107 A G 0.0026 MAP2K7MAP2K7(N, D)EML3(X*, D, QAS, QMS, QL, QAV, QB); rs11231156 A G 0.0026 EML3 MTA2(N, D, QAS); Atty. Docket No. UM-42504.601 AHNAK(D, EAS, EMS, EU); GANAB(D); INTS5(D); TUT1(D) rs565108 C T 0.0026 CRTAC1 CRTAC1(N) rs1044808 G C 0.0046 BOLA1 BOLA1(N, X); SF3B4(D) rs4410790 C T 0.0026 AHR AHR(N, D, EAS, EMS) rs1601935 T G 0.0027 LIPC LIPC(N, D, EL, QL) LHCGR(N, EAS, EAG, EMS, EAV, QAS, QAV); STON1-GTF2A1L(N, rs17326656 T G 0.0030 LHCGR EU) ABCA8(N, D, EAS, EAG, rs34931250 T C 0.0053 ABCA8 EMS, EAV) SCARB1(N, D, EAS, rs61941660 C T 0.0045 SCARB1 EAG, EMS, EL, EAV) rs2523159 A G 0.0033 TMEM101 TMEM101(N) rs731450 T C 0.0027 DOK7 DOK7(N, EP) rs11085216 G A 0.0028 INSR INSR(N, D, EP, EU) MAPKBP1(N, QAS, QAV, QIMA); MGA(D); SPTBN5(X*, QL, QAV, rs2925339 G A 0.0027 SPTBN5 QAW, QB, QIMA) ABO(N, EAG, EU, EAV, rs2519093 C T 0.0033 ABO QAS, QMS, QAV, QB) GIP(X*); LOC105371814(N); UBE2Z(D, QMS, QL, rs595767 G A 0.0026 GIP QAW, QB, QIMA) GADD45G(N, D, EAG, rs10797119 C T 0.0026 GADD45G EU, EL, EAV) HNF4A(N, X, D, EP, rs1800961 T C 0.0073 HNF4A EAG, EL, EAV) rs2159935 G A 0.0025 KITKIT(N)ABCA1(N, D, EAS, EAG, rs2275543 C T 0.0042 ABCA1 EMS, EAV) rs6486122 T C 0.0027 ARNTL ARNTL(N, D) CSF1(N, D, EAS, EMS, rs333947 A G 0.0036 CSF1 QAW, QIMA) CYP7A1(N, D, EAS, rs2081687 T C 0.0027 CYP7A1 EMS, EU, EL) ENSG00000253379(D); rs13269725 G A 0.0047 EYA1 EYA1(N, QAS, QAV) rs2282227 G A 0.0028 UBAP2L UBAP2L(N, D) rs447990 G T 0.0026 AFF1 AFF1(N, D, EU) ATXN2(N, D); rs653178 C T 0.0025 SH2B3 SH2B3(X*, D, EAS, Atty. Docket No. UM-42504.601 EMS, QL); ACAD10(D); ALDH2(D, EL); MAPKAPK5(D); PTPN11(D) MIR122(N, D); rs41292412 T C 0.0118 MIR122 MIR3591(N) PIK3R1(N, D, EAS, rs4976033 G A 0.0026 PIK3R1 EMS, EU) GIMAP7(N, X*, EAS, rs11766379 C T 0.0030 GIMAP7 EMS, EU) TRPS1(N, EU, QL, rs2737226 T C 0.0026 TRPS1 QIMA) rs2273967 C T 0.0029 GALNT2 GALNT2(N, D, QMS) ABCA1(N, D, EAS, EAG, rs4149307 C T 0.0035 ABCA1 EMS, EAV) AMBRA1(N, D); ARHGAP1(D, EAS, rs147244995 C T 0.0109 AMBRA1 EMS, EU); F2(D, EL) PPARG(N, D, EAS, EMS, EU); TIMP4(D, rs3103310 G A 0.0030 PPARG EAS, EMS) rs573455 G A 0.0026 CEP164 CEP164(N, X, EU, QL) EIF4A1(D); FXR2(D); SENP3(D); TNFSF12(N, rs9902027 C T 0.0031 TNFSF12 EU, QMS, QB) rs12610709 A G 0.0034 FIZ1 FIZ1(N, D); ZNF524(D) HPN(D, EP, EL); HPN- rs58895965 A C 0.0034 HPN,HPN-AS1 AS1(N, EP, EL)rs55966194 C G 0.0028 EYA2EYA2(N, QAS, QAV)rs117604511 C A 0.0090 DSCAML1 DSCAML1(N, EU) LMF1(N, QAS, QMS, rs12924608 T C 0.0026 LMF1 QAV, QAW) IRF2BP1(D); MYPOP(N); rs34934360 G A 0.0040 IRF2BP1,MYPOP,NANOS2 NANOS2(D) HYOU1(N, D); rs1003081 T C 0.0026 HYOU1 SLC37A4(D, EP, EL) rs79311290 G A 0.0043 FAM92B FAM92B(N) PPP2R1B(X*, D, EAS, EMS, EL); DLAT(D); LAYN(N, EU); SIK2(D, rs117847213 A G 0.0065 PPP2R1B EAS, EMS) MCUB(N, EAS, EMS, rs78025076 T C 0.0090 MCUB EU) rs11605837 T G 0.0028 CPT1A CPT1A(N, D, EU, QL) rs12927305 T C 0.0026 BANP BANP(N, QL) Atty. Docket No. UM-42504.601 LINC02068(N, EAS, EP, EAG, EMS, EL, EAV); rs79287178 A G 0.0077 LINC02068 rs34666276 C T 0.0027 ZNF652 rs1461729 A G 0.0042 LOC157273 rs111831594 G A 0.0046 KANK2 rs2070971 T G 0.0037 GCK ; rs12208357 T C 0.0050 SLC22A1 rs862320 C T 0.0026 NFAT5 rs10774439 G A 0.0033 CHD4,LPAR5 rs12921195 A C 0.0038 MGRN1 rs4776793 T C 0.0027 LINC01169 rs2749026 A G 0.0027 GSTA2 rs6506033 C T 0.0049 THOC1 rs9388767 A G 0.0028 L3MBTL3 rs691066 C T 0.0027 CYP27A1 rs11644475 A G 0.0077 CETP rs4709746 C T 0.0038 LOC102724152 rs2396084 G A 0.0028 VEGFA rs77960347 A G 0.0111 LIPG EU, rs4800392 C A 0.0026 CTAGE1,GATA6 rs12975366 C T 0.0026 LILRB5 rs1801689 A C 0.0074 APOH rs138436155 C G 0.0065 CCDC62 rs61733623 G A 0.0097 GLB1L rs6684677 C T 0.0031 GALNT2 rs72801474 G A 0.0044 HSPA4 rs1509195 C T 0.0028 LINC01230 rs62492368 A G 0.0028 AOC1 Atty. Docket No. UM-42504.601 rs6123685 G A 0.0029 BMP7BMP7(N)CCND2(D, EAG, EU, rs117233107 G A 0.0110 CCND2 rs67981690 G A 0.0038 SLCO1B1 rs8126001 C T 0.0026 OPRL1 rs6018652 G A 0.0031 SULF2 rs61993685 T C 0.0048 SLC25A29,YY1 rs12485478 G A 0.0079 PPARG rs11648592 A G 0.0094 NLRC5 ; rs11650182 A G 0.0050 SKAP1 rs1042521 G A 0.0025 PCK1 (N, rs1622638 A G 0.0026 MIR100HG rs696825 C T 0.0029 RMI1 rs2305746 G A 0.0051 HPN,HPN-AS1 ; rs6678052 G A 0.0029 LYPLAL1-AS1 rs1171615 T C 0.0030 SLC16A9 rs4762753 G T 0.0032 PDE3A rs11113118 A G 0.0029 RIC8B rs13097947 T C 0.0027 BTD rs852388 C G 0.0031 ACTB rs954244 G C 0.0029 LINC01101 rs7966846 C A 0.0028 INHBC,INHBE (D) rs6677420 A G 0.0027 CTSS Atty. Docket No. UM-42504.601 EAS, EMS, QMS, QL, QAV) rs4450871 A G 0.0026 CYTL1 rs3810291 A G 0.0027 ZC3H4 rs79859462 A G 0.0088 LINC02702 rs12686780 T C 0.0034 ECM2 rs56257305 G A 0.0047 FAM100B,RNF157 rs1055582 C T 0.0025 UBE2K rs144033177 C A 0.0104 TCF15 rs112403212 T C 0.0037 SCARB1 rs6700266 G A 0.0027 C1orf220 rs11752394 G C 0.0030 ARMC2 rs10052346 G T 0.0026 JMY rs61781290 G A 0.0029 MYCL rs3903399 C T 0.0031 CNTN2 rs59104589 C T 0.0027 HDLBP rs7298135 A T 0.0033 ACACB rs2227198 A G 0.0026 DNM3 rs62173875 C T 0.0026 GRB14 rs72904737 G A 0.0045 CDKN2C ; rs11581460 G A 0.0026 NR0B2 rs7157785 T G 0.0035 SGPP1 rs8101064 T C 0.0072 INSR rs78309295 G A 0.0086 TRIB1 rs4134963 C T 0.0033 E2F3 rs931992 G T 0.0027 STARD3 Atty. Docket No. UM-42504.601 GRB7(D, EP, EL); MED1(D); PGAP3(D, rs6848050 C T 0.0028 ADH4 rs1805123 T G 0.0030 KCNH2 rs10842703 T A 0.0030 ITPR2 rs607335 C A 0.0026 RGS17 rs3931630 G A 0.0029 USP14 rs41284816 G T 0.0095 DLEU2 ; rs11000468 C T 0.0030 PLA2G12B rs80138475 T C 0.0040 ARL15 rs8102873 T C 0.0026 MIMT1 rs68160490 G A 0.0026 WNT9A ; rs1327645 A G 0.0029 DLEU1,DLEU2,KPNA3 rs10011342 A G 0.0033 FAM13A rs615642 G A 0.0026 PPP2R1B rs60802178 G A 0.0034 TMEM163 rs71538127 G C 0.0039 GPR146 rs7675258 A G 0.0026 SHROOM3 rs2494748 T C 0.0026 AKT1 rs36043408 G A 0.0025 OPTC ; rs72735627 C T 0.0044 GCHFR Atty. Docket No. UM-42504.601rs34820121 C A 0.0027 ASXL2ASXL2(N, D, QAV)rs34345377 G C 0.0032 VPS53 VPS53(N, QB) ; rs141136126 G A 0.0078 MLXIPL rs79983121 T C 0.0032 TPRA1 rs78274649 G A 0.0091 MC4R ; rs34898535 C T 0.0026 ZNF646 ; rs664732 C G 0.0065 FNIP1 rs1149470 T A 0.0030 LINC01289 rs144718240 T C 0.0062 RIN1 rs3808976 G A 0.0031 SLC35C1 rs115912456 A G 0.0064 VCAN rs9919491 A T 0.0029 EXOC6 rs139827355 G A 0.0067 TRIB1 rs11688682 G C 0.0030 LINC01101 rs4760 G A 0.0035 PLAUR rs935328 T A 0.0029 TCF12 rs830620 C T 0.0026 EIF4E3,FOXP1 rs62565259 C T 0.0034 NAMA rs878409 G A 0.0026 FGFR2 rs1938566 C T 0.0034 DPYD rs6465120 A G 0.0025 SSC4D rs7298751 A G 0.0039 SCARB1 (N) rs7631606 T G 0.0029 BCL6 Atty. Docket No. UM-42504.601 ACSS2(D, EAS, EMS); AHCY(N); NCOA6(D, QIMA); TP53INP2(D, rs6088461 T G 0.0026 ACSS2 QIMA) rs117044596 G A 0.0068 HERC1 HERC1(N, D); USP3(D) LINC01524(N); TSHZ2(D, EAS, EAG, rs6068280 G A 0.0027 TSHZ2 EMS, EU, EAV) MST1R(X*, QAS); RBM5(D); RBM6(N, EU, QAS, QMS, QL, QAV, QAW, QB, rs11130227 G A 0.0026 MST1R QIMA); RNF123(D) DOCK6(N, D, EAS, EMS); C19orf80(D, rs138572354 A C 0.0054 DOCK6 QAS, QAV) RNF168(N); UBXN7(D); rs13094241 T G 0.0029 UBXN7-AS1 UBXN7-AS1(D, EU) rs73221948 T G 0.0029 CDCA2 CDCA2(N) PDE3B(D, EAS, EMS, rs79634051 G C 0.0077 PDE3B EL); PSMA1(N) rs12440800 T A 0.0029 LINC02349 LINC02349(N) rs4668313 A G 0.0026 ERICH2 ERICH2(N) P2RX3(N, EAG, EL, EAV); PRG3(D); SSRP1(D); TNKS1BP1(D, EAS, rs10792091 C T 0.0037 P2RX3,TNKS1BP1 EMS, EU) PTGER3(N, D, EAS, rs650194 A G 0.0027 PTGER3 EMS, EU, QAS, QAV) rs6708784 A G 0.0026 BCL2L11 BCL2L11(N, D, QAV) rs62427983 T C 0.0027 BEND3BEND3(N, D)rs837500 C T 0.0027 NCOR2 NCOR2(N, D) PDE3A(N, EAS, EMS, rs11045247 A G 0.0049 PDE3A EU) PEMT(N, D, EAS, EMS, EL); RAI1(D, EU); SMCR5(D, EU); SREBF1(D, EAG, EL, rs56157833 T A 0.0034 PEMT EAV) DGKQ(N, QAS, QL, QAV, QAW, QB, QIMA); GAK(D); IDUA(D, EU, QAS, QMS, QL, QAV, QB); SLC26A1(D, EAG, EL, rs13101828 A G 0.0026 DGKQ,IDUA EAV, QAV) Atty. Docket No. UM-42504.601 POR(N, D, EAG, EL, EAV, QAW, QB); TMEM120A(D, EAG, rs2302429 A G 0.0033 POR EAV) RELB(N, D); APOC4(D, EAG, EL, EAV); rs2376867 T C 0.0027 RELB CLPTM1(D) ALG10(N, QAS, QL, rs11052977 A C 0.0026 ALG10 QAV, QIMA) PNPLA2(N, X, D, EAS, rs140201358 G C 0.0109 PNPLA2 EMS) rs7916761 T C 0.0026 CYP26A1 CYP26A1(N, EL) PDGFC(N, D, EAG, EU, rs1816164 C T 0.0030 PDGFC EAV, QAV, QB) MAFB(N, D, EAS, EMS, rs2207132 A G 0.0071 MAFB QL) PLA2G12B(N, D, EL); rs3829126 T G 0.0044 PLA2G12B OIT3(D, EU, EL) rs11643205 T C 0.0059 NUP93 NUP93(N) DHX38(D, QAW, QIMA); HP(D, EAS, EAG, EMS, EL, EAV, QAS, QB); PMFBP1(N, rs7191623 G A 0.0031 HP EP) ZC3H12C(N, QAS, QL, rs6589112 C T 0.0028 ZC3H12C QAV, QAW, QB, QIMA) rs4802269 A G 0.0027 GIPR GIPR(N, EAS, EP, EMS) rs2289863 T C 0.0030 PIAS4 PIAS4(N, D) AKNA(N, D, EAS, EMS, rs1044531 C T 0.0045 AKNA EU, QAS, QAV, QIMA) GTF2IRD1(N, D, EU); rs111914893 T C 0.0059 GTF2IRD1 GTF2I(D) GPR180(N, QAS, QMS, QL, QAV, QAW, QB, rs9561643 C A 0.0027 GPR180 QIMA) NRP1(N, D, EAS, EMS, rs2776937 A G 0.0030 NRP1 EU) rs3770781 A G 0.0026 STRN STRN(N, QMS, QIMA) ENSG00000238078(D); LINC01352(N, EAS, rs61830291 C A 0.0043 LINC01352 EMS, QAV, QIMA) rs12694933 T C 0.0039 ACVR1C ACVR1C(N, EAS, EMS) rs12863082 C T 0.0030 LINC00348LINC00348(N, EL)SLC2A2(N, D, EP, EL, QL); rs5402 A T 0.0039 SLC2A2 ENSG00000199488(D) Atty. Docket No. UM-42504.601 CERS4(N, D, QAS, QMS, QAV, QAW, rs11880745 T G 0.0031 CERS4 rs275179 A G 0.0036 rs117291242 T C 0.0068 ANO9, B4GALNT4 rs185799410 T G 0.0081 GNAS rs4519367 A G 0.0026 KCNJ2 rs8021491 T C 0.0027 FOXN3 rs113905686 C A 0.0053 DHX8,LINC00910,NBR1 ; rs12638256 A G 0.0026 PAQR9 rs2613505 T C 0.0032 NEGR1 rs498475 G A 0.0026 JAZF1-AS1 rs935168 A G 0.0027 KCNK3 rs7596814 G T 0.0028 PID1 rs9812100 G A 0.0026 ITPR1 ; rs12793175 A G 0.0034 AMPD3 (N, rs282146 A G 0.0026 TCF15 ; rs111406374 A G 0.0082 GATAD2A rs35957544 G T 0.0026 KCNB2 rs6572807 G A 0.0029 NID2 rs1461396 G C 0.0052 ANO3 ; ; ; rs57172722 G C 0.0056 STAB1 Atty. Docket No. UM-42504.601 FAM13A(N, D, EAS, EMS, QAS, QAV, QAW, rs4425336 A G 0.0031 FAM13A QIMA) rs213494 T C 0.0027 SSBP3 SSBP3(N, D, EU) MAP3K1(N, D); rs252913 A G 0.0027 MAP3K1 MIER3(D, EU) BCL2(N, D, EAS, EMS, rs12454712 T C 0.0026 BCL2 EU, QMS) NRIP1(N, D, EAS, rs2223041 T C 0.0026 NRIP1 EMS); C21orf116(D) Table 3. 114 TG:HDL-C SNPs which met an FDR-adjusted p-value<0.05 in at least one of the analyzed MAGIC Consortium Traits. rsID in TG:HDL-C POS study Gene CHR (hg19 / b37) EA OA rs3768321 PABPC4-AS1 1 40035928 T G rs61781290 MYCL 1 40393160 G A rs2613505 NEGR1 1 72835410 T C rs1365297 CCDC18 1 93858292 A G rs1044808 BOLA1 1 149871905 G C rs2227198 DNM3 1 172360164 A G rs1538742 LYPLAL1-AS1 1 219668252 A C rs6678052 LYPLAL1-AS1 1 219755057 G A rs34820121 ASXL2 2 26098259 C A rs6714780 GCKR 2 27189063 A G rs115961201 GCKR 2 27704896 G A rs1260326 GCKR 2 27730940 T C rs62131877 GCKR 2 27740328 C T rs2723065 CEP68 2 65279414 A G rs954244 LINC01101 2 121309231 G C rs11688682 LINC01101 2 121347612 G C rs62173875 GRB14 2 165462543 C T rs13389219 GRB14 2 165528876 C T rs355849 COBLL1 2 165625830 T C rs148358468 CYP27A1 2 219590348 A G rs691066 CYP27A1 2 219678421 C T rs2943645 LOC646736 2 227099180 T C rs12485478 PPARG 3 12351223 G A rs2067819 PPARG 3 12359049 G A rs3103310 PPARG 3 12473045 G A rs11130227 MST1R 3 49993319 G A Atty. Docket No. UM-42504.601 rs6800707 NISCH 3 52516293 G C rs684773 PCCB 3 135956305 C A rs12638256 PAQR9 3 142655290 A G rs62271373 LINC01214,TSC22D2 3 150066540 A T rs9817452 LINC00880 3 156795414 G T rs79287178 LINC02068 3 172294500 A G rs4450871 CYTL1 4 4990298 A G rs7675258 SHROOM3 4 77413179 A G rs4425336 FAM13A 4 89753225 A G rs6848050 ADH4 4 100053733 C T rs13107325 SLC39A8 4 103188709 T C rs1816164 PDGFC 4 157696636 C T rs80138475 ARL15 5 53304991 T C rs392794 C5orf67 5 55808342 T C rs9687846 MIER3 5 55861894 A G rs252913 MAP3K1 5 56195846 A G rs4976033 PIK3R1 5 67714246 G A rs1045241 TNFAIP8 5 118729286 C T rs664732 FNIP1 5 131281458 C G rs1030472 EBF1 5 157994544 G A rs2074493 HLA-C 6 31239776 C A rs114760566 HMGA1 6 34192036 A C rs9472125 VEGFA 6 43756169 C T rs998584 VEGFA 6 43757896 A C rs2396084 VEGFA 6 43804825 G A rs11752394 ARMC2 6 109189664 G C rs2800709 RSPO3 6 127439297 G T rs72959041 RSPO3 6 127454893 A G rs9388767 L3MBTL3 6 130357553 A G rs632057 LINC01625 6 139834012 T G rs4709746 LOC102724152 6 164133001 C T rs7786102 MIR148A 7 25965759 G A rs1534696 SNX10 7 26397239 C A rs17145750 MLXIPL 7 73026378 C T rs10260148 KLF14 7 130430969 T C rs11766379 GIMAP7 7 150208971 C T rs7012814 LOC157273 8 9173358 G A rs1461729 LOC157273 8 9187242 A G rs7015766 LPL,SLC18A1 8 19939049 C T rs2737226 TRPS1 8 116639474 T C rs72647336 TRIB1 8 126445055 A G Atty. Docket No. UM-42504.601 rs1509195 LINC01230 9 1033958 C T rs12686780 ECM2 9 95382297 T C rs1044531 AKNA 9 117098650 C T rs7924036 JMJD1C 10 65191645 G T rs2792751 GPAM 10 113940329 C T rs878409 FGFR2 10 122999550 G A rs79634051 PDE3B 11 14561945 G C rs1461396 ANO3 11 26281664 G C rs61888800 BDNF 11 27722278 T G rs56271783 VEGFB 11 64004723 C G rs6589112 ZC3H12C 11 109971929 C T rs1622638 MIR100HG 11 121800971 A G rs4762753 PDE3A 12 20579969 G T rs10842703 ITPR2 12 26456188 T A rs653178 SH2B3 12 112007756 C T rs116896792 ZCCHC8 12 122948907 C T rs526276 HCAR1 12 123192454 C T rs11057397 CCDC92 12 124419728 C T rs10773049 ZNF664-RFLNA 12 124506631 T C rs7140110 GAS6 13 114544024 C T rs6572807 NID2 14 52480621 G A C15orf54,ENSG000002594 rs275179 50 15 39447529 A G rs12924608 LMF1 16 950007 T C rs12928099 PDXDC1 16 15150505 C A rs11648592 NLRC5 16 57083232 A G rs3794695 HP 16 72097827 T C rs34345377 VPS53 17 489296 G C rs931992 STARD3 17 37821435 G T rs113905686 DHX8,LINC00910,NBR1 17 41469457 C A rs1801689 APOH 17 64210580 A C rs12451511 BPTF 17 65874861 G T rs4519367 KCNJ2 17 68470163 A G rs4800392 CTAGE1,GATA6 18 19908497 C A rs11664369 PMAIP1 18 57739072 T C rs12454712 BCL2 18 60845884 T C rs8101064 INSR 19 7293119 T C rs2115107 MAP2K7 19 7968168 A G rs3934667 MAU2 19 19431423 G T rs62102718 PEPD 19 33891013 T A rs3810291 ZC3H4 19 47569003 A G Atty. Docket No. UM-42504.601 rs838133 FGF21 19 49259529 A G rs144033177 TCF15 20 571467 C A rs55966194 EYA2 20 45599090 C G rs6068280 TSHZ2 20 51235613 G A rs6123685 BMP7 20 55836040 G A rs1042521 PCK1 20 56136536 G A rs2267373 MAFF 22 38600542 T C

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1 69 5 76 77 09 22 62 66 22 16 7 14.10.4 1 713.281.46 9 1 610.09 2 87.22 3 27.45.8 2 8 8 14.845.3 5 6 92 4 116.5 9 6 18 96.4 7 0 02 33.9 09 69.09 3 81 - 5 - 0.5 - 6.1 3.9 - 27 - 5 - 46 - 52 - 67 - 85 - - - 752.272.1.80.81.68.3 0 1.36 6 6.76 3 6.631.06.55 11.2 1 1 3 2 1 32 58 36 12 025 36 26 - 26 - 6 6.76 71 36 33 6 84 66.744.537.3 5 30.28.2 85 36.7 0 3.29 73 60.2 5 54 96.3 77 55.4 51 93.4 6 4.47 0 9.19 6 36 6 8 73 4 77 4 27 56 4 7 68 292 160 309 6346 6346 2772 1413 2222 6676 59 22 28 5 68 93 75 54 43 6 . 3 63 27 29 22 66 8224 2222 505 7 74 26 40 62 85 86 438.6 4 37.2 4 77.7 437.2 426.6 464.1 472.2 130.409 692 091 971 601 421.46 40.419.369.368.358.32 5 5.589244 47 65 8 9 95 26 53 764 -6.7 1 51.52 1 41.44 9 5 8 7 3 71 88 87 87 0 4 14.11 3.1 4 19.7.35.1 1 04.3 16.5 21.4 13.2 1 14.4 14.0 0 . 3 - 33 - 16 - 05 59 89 25 05 11 16 2 - 02 605.0 1 79.8 0 47.3 290.9 416.672508784036 913 094 744 434 1 60.26.7 39.2 66.0 12.5 233.8 662.0 057.055.5 655 - 1 - - - - - - - - - - 2.15 7 2.83 2 7.22 5 8.21 9 3.607.128.58 2 5895.381.76 0 34 - 5 7 05 - 3 6 91 - 6 9 85 - 07.12 85 8 6 77 97 89 020 318.78 418 750.57.53.40.73 514 2.5 95 13 51 57 228298 0255 7 7714 18 1192 112.2 7 792.2 6 935.8 2 994.3.1.6 1 95 7 5 17 4 8 166.3.24.8 1 96 8 6 10 3 1 692.1.9 93 11 4 134.1 2 691.4.5 81 78 4 150 G 1 1 S 0 N05 3 1 5 A S 8A-E,49 2 01 26 1 455 A 1 1 1 F 2 0C 1Lf2 0 3 41FF1L0LSALGR2L ro00 AF G C R C M E NFA T C,LP A X 50 G VILPTINILN DLK M Y N CILPLYLP P S A 1 C00 E V 8 6.64 0 043.34 4 2.35 2 7.61 3 4.62 7 2.26 8 5.79 5 5.47 7 3.33 2 9.80 4 2.91 4 290 3 358 4 891 8 814 7 047 073 09 063 019 070 046 012 07 08.9 05.2 02.7 03.5 075 A T A C G C C G G T A A A G T G C T G A T T A T C G G C A C 2858 4 58862 1 9428 04 904 5 59 29 690 9 16 741 04 66 09 36 50 92 42 31 09 57 5 5 03 2 5 1 8 3 3 3 54 31 32 40 06 09 89 39 79 7 8 4 9 7 0 4 6 4 57 4 01 62 21 71 31 83 94 31 91 12 21 62 93 34 6 91 22 1 7224 6 8 1 3 21 56 -E1 - 21 E1 - 1 E01 003 - 0 E0 - 1 E5 - 3 E4 - 2 E0 - 1 E0 - - - - 4 - E01 E21 E90 0052 E14 5.7 32.8 86.00 1 90.006 4 9 4 5 6 4 E 6 95 8 9 5 9 6 8 00 8 3 00.5.9.2.2.43.80.4 1.1.10.0 00.4 69 9 3 0 48 8 7 6 7 0 0 5 8 3 2 1 2 3 62 2 2 6 5 2 1 7 0 8 5 7 24 515 010 118 228 084 23 41 3 06 62 10 412 7 45 sr80sr91sr72sr68sr9 1 1sr1 2 0sr7 4 3sr7 6 8sr7 7 5sr6 6 7sr5 3 0sr1 3 3sr1 2 0sr9 9 7sr21 106.U y A C 83F 1PDI7PLT 2 8C J A A R A B H A C 3 3 1 C H BF H TP N BMJK F S A N TNIMIM G3LSIE O N D P N F M A B E R C A C G Z E O V P A 9 1.32 93 54.4 8 0.81 2 7.33 3 0.75 3 2.45 9 6.59 1 8.10 9 7.44 6 0.96 1 2.10 6 7.19 9 0.60 4 0.87 6 9.48 0 046 068 078 072 01 047 040 069 063 094 034 071 025 069 T T T T C T G C T C A C T G C G G C C T C A G G G G G C C A 681 4656805 2968 91 9817 5735923 699 6645444 694 98723 85047 1 8 9 9 5 1 0 2 1 0 5 6 9 1 9 8 6 40 74 0 7 78 39 20 30 15 7 82 9 1 9 0 1 6 56 11 11 27 51 31 2 5 5 02 6 7 2 5 9 1 0 2 1 2 0 2 1 46 46 17 1 09 51 97 6 31 2 1 15 61 2 1 1 1 7-E6 - 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0 E4 - - 3 E5 E2 - 2 E7 - 1 E1 - 1 E0 - - - - - - 1 E90 E51 9 E11 E80 E9 E6 31 2.6 14.8 1 53.83.8 49.3 82.2 36.4.3 9 21.2217. -E 9 4 18.5 28.80 44.51 47.6 68 36 86 388 2 3 3 2 3 1 2 0 9 3 4 7 1 6 6 2 2 4 15 8 99 1 2 17 9 72 975 731 414 25 725 165 59 087 3 29 9 5 12 75 sr01sr04sr23sr85sr67sr60sr86sr44sr4 8 8sr3 1 3sr0 6 8sr6 2 6sr3 1 1sr2 3 0sr96 10 - 16.3.8 - 3 2 18 - 5 8 217 6 - 0527352 41 - 7 3 016 5 144 5 454 4 375 5 - 9 8 078 0 73 - - 0 5 158 7244 0 5.02.05.1.0.15.8 12.08.5 05.4 08. .2 0 03.27.2 136.07052 12 - 3 2 0 3 47 33 - -4-8.83.35.2- 01 41533 3 3333- 3 333 3 333 3331- 8 23 1.18.2 13.3 03. .3.3 0 3 633 03M U.- 7 o0.1 - 1 0 58.7 - 8 3 87.47 5 54.69 0 18.2 - 2 7 02.4 - 0 0 16.75 4 39.82 0 15.37 1 48.34 4 31.58 6 64.23 2 26.46 83 15.6 88 11.10N 1 3 0 1t5 07 0 0 33 13 0 0 4 2 2 53 17 27ekc 4 5.4 - 4 2 - - 0.85 3 9.77 1 2.34 4 7.35 7 0.68 9 3.33 4 664 2 379 0 475 8 647 8 191 0 594 6 811 6 136o 144 76 014 658 276 382 16.49.5 47.6 03.9 00.21.2 37.9 49.15D 5.ytt 4.3 -2 2 9 1 7 7 2 9 4-129 .6 06 .82 .47 .90 .48 .39 .56 .21 .33 .17 0. 1 6. 6 0. 2 4.75A 2 2 4 0 0 3 0 0 0 2 1 2 0 0.4 -7.74. 4.61.891141292544221 . 1 -6536 36671 4030 .07 .44 .64 .11 .08 .37 .39112.0 05 .68739 - 3.4 1111 03961.3.9019 3134 0990 54 5 93 - 22 79 61.3053.93.31 22 413.2 66.12.7 8 -4.0 14.0 45.9 8 42 1 47.72.20.6 3 0 1 -25- 15 - 8 - 81 - 53 79 - 49 - 22 8 - - - - 4.11.6 272.16.2 140.6 324.1 267.1 444.2 228.35 53 130.6 35 2 1 21.3 92 142.1 9 53 0 249.3 41 088.1 32 6 - 7.3 22.83 2 9 045.57 4 337.23 8 100.18 4 557215 7353 333 9 712 1 037 3 724 0 740 791 6 00.1 03.2.4.33.3 45.4 38.5 48.6 374.3 16 7.5 69366.5 7131 8922 5083 483 6 15 58.53 61 26.25.53.580.29 31 2 2 6.92 2 2 25.93 0 25.93 9 204.06 601 2 81 1 5 2 952 724 263.221.285.194.18 91-3- - - 6 65 33765. 592 379 286 882 095 597 5568 4884 5 3 6 1 0 1 7 5 1 6 5 32 0 5 2 . 5 8 6 8 2 3 0 0 4 0 6 8 5 5 1 7 25.05.11.03.05.18.07.07.10.05.19.09.04.1 - 2 - . 47 85 75 33 - 89 - 85 93 42 - 7 - - 025 28.3 298.6 036.60532 6 4 8 83 380 411 835 180 958 8 19.7 12.1 107.089.942.7 177.4 4 92.471.8.4.4.7 02 767 04 34 -1.6 23- 3 - - - - 11.40 9 322.46 8 5 15.15 0 618.63 7 515.2 - 6 7 700.6 - 86.5 - 11.4 - 1 8 - 73 418 1152.66 9 41.1 - 3 84 - 74.8 - 2 26 - 9 699 9 49.56 5103.881.63 2 6.24 09 96 39 2 59 66 4815 6 02 842.7 9 757.3 557.0 3 8 584.5 5 605.2 4 883.18.88 181.8 4 25 28 6 6.24.2 872.45.6 193 487 1 81 45 67 6 178.53.7 1 655 707 6AT 3 A MG,5 O 1 1FO 2 2 4 R 1L1 1 0 E 3 B C C R M F YPIBI1X 2C- G B 52JT H S C E G F R G M N F R T N SDIANLA H T C C C S P P N V C K 1 0.93 33 78 58 75 68 48 58 53 69 24 6 85.961.494.435.672.010.110.474.312.382.7 5 94.9 63 07.8 94 61.2 86 54.1 05 0 5 04 05 06 05 00 06 09 0 6 05 05 0 04 045 A G C A T A G G A A A A A C G C A T G C G C A C G C C C G A 767 9 6 3 7 94 191 3 27590 523 10418 055 29 217647365 6637 1 28 95 9 64 4 39 85 2 45 6 0 9 1 4 79 0 93 8 5 2 0 5 47 3 7 5 2 9 2 5 1 6 3 1 0 1 4 3 6 1 2 3 8 1 6 4 2 0 2 25 1 9 9 9 7 3 9 5 1 3 8 4 1 4 86 204 91 02 1 5 8 71 461 831 7 1 7-E0 - 21 E9 - 0 - 9 - 0 - 0 - 9 - 0 - 6 06 - 0 - 1 - 1 - 9 - 8 2.8 0 E21 E50 E 1 E 1 E 0 E 5 E 1 04 E 4 E 1 E 3 E 0 E 0 47.11.38.9 30.8 58.9 42.6 25.7 19.00 8 30.014 3 4 4 9 8 9 5 0 0.6.4.1.1.2 047 57 86 48 71 87 7 4 4 2 4 0 7 4 9 47 6 0 5 0 4 66 3 7 7 0 4 37 1 11 78 218 78 267 169 66 273 56 57 03 82 8 47 57 sr33sr52sr76sr90sr83sr21sr23sr3 1 7sr9 6 6sr0 2 8sr9 4 4sr9 6 3sr3 3 7sr3 4 5sr63 10 - 46.1.81 3 7.49 1 5.92 3 3.33 4 9.4 - 4 7 791 7 03 - 24.1 88 - 1 4 - 1 6 67 - 6 3 333 5 - 6 0 967 4 9444 0 1 125 016 033 04.4 04.3960- .08.0.06.23.1.14.144052 0 3 6 6.766.76 3 3.3 - 3 3 3.33 3 3.33 6 6.76 3 3.3 - 36.3 3338.5 2 3 833 04- 16 3 6 166 133 73 133 066 2333- .13 0.0.23M 3U.3 o2.5 - 3 4 150.37 3 0 54.74 6 283.5 - 5 1 381.29 0 52.93 8 49.29 1 25.96 4 89.3 - 9 4 60.20 6 5.98 1 67.1 - 4 2 64.98 8 97.56 0 82.159Nt2 0 3 1 08 1 37 03 0 19 03 0 02 24ek - - - - - - - - - -c 3 5.33 9 4.14 7 0.36 4 5.30 7 8.20 5 4.51 5 298 0 764 8 463 6 685 4 595 6 891 0 140 3 767 3 712o 1 3 270 215 24 32 35.13.12.10.21.14.01.12.06.41D 7 3.ytt 2.77 9 0-2-1 3-2-5 5-0 8-8 0 603 . 62.8 07 .4 30 .3 95 .5 02 .25 .34 .88 .90 .95 . 5 .86 .27 .32A 2 0 7 1 1 0 0 3 1 7.81.96. 8 6.6 - . 7 -8.38. 9 6.2 -94618146 -1 6-91 53 11245 241172 051 34 .51 .06 .14 .05 .6.0.42277 . 2 8612 1 - 778 - 85 5 36 55 27 79 5 72 4 18 55 27 13.4 7 792.636 454 727 4504.7160.318 454 87.15.2 63.05.22.16.1 24.1 09.30.1 - 7 4.91 7 2.8 - 7 7 4.86 6 7.60 8 8.98 6 3.129.8 - 3 4 026 2 417 8 752 0 136 2 957 6 67 - 6 6 1765.06 67 1 3 07 48 04 412.27.25.01.42.8.6.6 3 7 4 8 8 5 8 5 01 06 2 1 6 . 9 02 45 74 82 33 21 - 75 - 7 - - 31 037.8371038581576 62 5 732 353 510 541 065 2 48.5 23.41.9 32.0 06.9 59.1 044.382.44.092.6 042.0 038.273.455 45 5 - - - - - 54 455 7450 5 59186.2 5891 5 02 64 15 24 54 76 45 54 098.03 05.153.6815 3663 511 41 21 58 0.49.591 082 058 01636551 1 275 3 285 8 224 15 0 4.06.0.01.04.03.05.18.2.21 5 - - - - - - . 51 6 59 796 5 8 17 4 67- 9 3 0 5 5 9 1 4 24 41 6 47 62 0 -0.0 62 7 5.4 13 55 06.81- 11 98 86 88 61 49 50 06.16.20.03.01.37.23.12.04.23.41.1 - 3 9.40 9 799 5 45 - 7 5 97 - 15.28 7 876 9 7215.916.64 8 310 3 170 5 72 - 7 7 592 5 51 - 8 9 720 12.13.8 70.5 20 519.21.3 13 3 6 13 4 7 14.3 24.2 06.7 12.4 40.72.22 -8.4 - 52 - 99 -4. 5-1.9 - 35 - 66 - 86 - 95 - 55 - 41 -2.7 -29 - 44 - 12 07 175.6 5 76.2 1 1 6 02 0274.0 793.8 775.2 171.4 209.3 8 36.0 0.6 6 1 412 5163.4 247.8 95 7 3.37 3 5.43 3 5.73 3 9.328.83 0 9.47 6 8.626.275.71 1 5.76 2 454 5 681 4 8240.72 5 210 536 68 862 27 1 3 153 996 70 7 7 12 8 2 146 98.53.3 18.7 61 1 8 23.4 965 G C2 H 1 2 9 3D 1 2 1 00 5 81 H Z R R M 1 R 6S UAL1 C C R F 3 H Q A AP K GE ARIRLD K N C Z S T A P T S G C P N A G M O B M N C C C G D B 0 7.62 71 66 37 26 66.194.753.67.8 0 5.39 0 8.58 9 2.64 8 6.88 1 9.29 9 3.99 1 0.40 1 8.38 4 9.27 5 2.98 05 0 4 05 013 045 097 052 038 001 058 034 059 065 08 059 T A G T T A C T T C G G G T G C G A G C G T C G G A A A C T 919 7 216320353936 1 0042395 9132 92 28 789 5 559 41 042 56 45 442 41 170 007 23 28 30 22 9 3 0 2 6 2 9 3 3 5 3 8 8 8 5 4 2 1 1 2 5 4 8 1 7 3 1 8 4 4 9 1 0 8 7 7 3 11 65 27 11 91 41 21 75 39 72 72 11 2031 7 1 0 2011 3 1 911 1 1 61 21 1-E8 -E9 - 0 E8 - - - - - - - - - - - - 0 E01 E92 E01 E80 E9 E6 E4 E0 E0 E7 E6 E6 30 3.6 14.4 40.5 36.3 85.3 35.6 1 10.1.71 3 18.2 1 1 1 1 2 1 78.5 4 51.2 46.14.11.2 12.7 98 86 36 9 2 2 3 0 4 4 2 4 5 3 8 5 6 1 9 4 1 4 3 4 2 62 6 17 80 62 060 215 39 721 011 65 10 97 08 68 19 37 27 10 sr11sr82sr28sr29sr57sr2 2 5sr0 7 5sr1 3 1sr6 1 6sr0 1 8sr3 1 6sr5 1 8sr9 6 2sr8 6 1sr88 10 16.1.11 4 081.75 8 388.12 5 072.2 - 3 8 071.2 - 8 7 10.9 - 2 6 56.7 - 6 3 - 61.30 1 2.67 4 19.67 4 11.955.0 04 0 2 3 7 1 0 0 0 0 1 1 17052 5 6 44 21 67 - 6 -4-4-3-2.34.4 044.7 451.6 266.76 3 063.33 676 333 71 236.263.03M 4 8 6 3 3U.0 o2.52 4 0 16.28 8 416.34 0 - 1 039.57 7 05.90 2 110.79 3 34.6 - 1 0 16.1 - 4 4 16.80 9 83.45 6 2.226Nt4 4 5 07 27 0 2 0 0 2 9ek -c3.8 - 48 -75- - 548.1 71 11.8 0 09 38.4 75 14.9 71 79.55.9 0.94 8 4.63 2 5.82 4 7.82o 1 4 3 0 1 08 1 49 4 0 2 49D 2 2 5.ytt 3.3 -500 . 5-9895226225 -86 -6307 -64.240.00 .23 . 5 . 4 . 4 . 0 . 0 . 4 . 016 A 0 4 0 0 2 0 2 1 1.92 1. 1-3.34 2.29- .357.8 -0.2 -3.6 -0.3 -6.4 -4.29.1 4 1 6 3 148 080219753824069 - - 8759 57 6 - 676 5714 5438 276.5 44 18 44 5.3.864 22 8 6293-0.4 4 4 8 618 4 444 0 9.3.0.2.2 51.0.3.3 6.521.1 - - 15.739 451 8125- 381.4.6 - 33 - 92 4 615-3.09.7.35.6.5 1 573.32.1 1 21 32 09 04 3 01 111 03 64 98 2 - - - - - - 6.0 4 33.4 81 90.96.77 71 5.4 2 8.35 0 3 0.30 6 9.79 6 7.60 1 1.563.7 07 32 7 3 2 - 098 03 3 1 674 07 10 - 18 - 59 - 24 - 63 - 33 - 5 - - - - 81 5 82 2 51 174- 74 1 . 8 8224 4617 500 33 45 185.98 75 57 31 1 5 83 333 545 8183284.274.286.238.232.343.31 - 0 8.32 5821.49 38.34 218 - 905- 3 567- 7 - 8 - 7 - 3 - 75 12 33 21 66 1 2 5 5 1 5 6.28 1 4 1 8 008 8.4 8.8 5.3.1 3.5 8.2 0.2 4.1 2 -5.0 0 23- 2 1 3 0.2 61 - 1 . 5 - 21 - 41 41 67 - 07 3 0 6 9 743 393 843 977 219 8376.10 947.019.300.629.7 240.715.4.2.3.9.1 0 357 358 11 227 240 210 -8.5 - 921.9 - 41.3 - 1 6 26 010 1257.5 - 2 1 233.8 - 2 7 - 635.30 92 - 6 43 - 78.9 - 03.70- 63 - 5 782 15.229.70 416 012.75.40 5.3 42 6 4 8 6 94 43 2 99 95 7 35 109.50.1 73 817.5 53 972.5 10 470.12 11 7 295.9 4 669.25.7 99 20 2 146.1 2 754.59.7 95 89 5 796 190 8 0 8 6 C 0 A 1 9PI L0NI8 PI20L,1 8 R 00 4 3 A R C R 8B 1 L F C H 4 R CXLK S M P M C N K GILC X G H N 3 H K DN,0MLILC Z D A C G 7 0.67 6 2.66 3 8.37 5 0.16 3 061 6 9.27 6 0.63 0 6.51 1 6.67 7 6.89 9 6.75 9 3.20 084 007 039 01.6 01 022 02 019 052 084 057 005 C C T G G A A C T G T C T T C A A G C T G A C T 789 8 0702 78 635096 577 440 373401 9 36 09 490 84 49 00 591 09 353 90 3 3 0 7 2 8 2 0 6 0 7 6 0 3 1 7 0 5 3 1 2 7 4 5 6 5 0 7 7 72 71 72 14 9 51 74 01 72 41 87 2 3 21 7 1 631 94 2 -E2 - 3.3 E6 - 1 1 E1 - 4 E8 - 2 E2 - 1 E4 - 2 E80 2 -E7 - - - 1 E01 E01 E64 54.6 57.2 3 26.3 84.9 88.9149. -E 2 2 52.7.9.1 8 2 81.2 1 01 66 41 41 82 951 936 2 7 0 8 0 35 19 0 8 0 8 98 1 1 4 6 12 16 715 760 977 12 16 20 82 81 80 26 sr37sr34sr75sr87sr1 1 7sr1 1 6sr5 1 0sr6 9 4sr5 3 4sr9 6 2sr5 1 0sr23 Atty. Docket No. UM-42504.601 Table 5. TG:HDL-C genome-wide associated loci in the European population of the UKBB stratified by sex. P-value P-value a Heterogeneity rsID Locus EA OA Male Female P-value 01 05 01 02 02 01 01 03 01 02 23 01 01 01 01 04 01 04 01 04 03 01 06 01 03 04 04 02 02 01 22 01 05 01 01 01 Atty. Docket No. UM-42504.601 rs117794084 LINC02702 T G 1.59E-24 3.00E-22 4.63E-01 rs11057397 CCDC92 C T 1.42E-12 6.66E-38 2.25E-05 9 1.33E-01 2 6.05E-01 8 3.06E-07 0 5.12E-01 7 3.42E-01 4 7.63E-01 6 6.88E-02 1 7.05E-01 8 1.95E-01 4 7.45E-01 8 3.18E-01 8 2.21E-01 3 1.90E-09 6 7.64E-03 8 7.50E-01 5 3.90E-01 5 7.37E-03 9 1.81E-03 4 1.63E-01 8 7.16E-01 1 2.35E-01 8 7.59E-01 9 9.95E-01 6 5.66E-01 0 1.02E-01 0 4.98E-01 7 3.93E-01 9 5.45E-01 2 4.92E-02 4 1.27E-03 2 4.49E-02 5 6.58E-05 6 7.29E-01 5 9.42E-01 7 2.46E-01 0 1.93E-02 1 7.80E-02 8 5.19E-02 4 7.19E-01 Atty. Docket No. UM-42504.601 rs1064725 APOC1 T G 3.41E-17 4.15E-09 2.03E-02 rs140570886 LPA T C 2.51E-09 1.78E-16 1.15E-01 Atty. Docket No. UM-42504.601 rs367070 LILRA3 A G 2.78E-07 7.39E-13 1.65E-01 rs1534696 SNX10 C A 1.43E-05 6.63E-17 3.15E-03 Atty. Docket No. UM-42504.601 rs2275543 ABCA1 C T 8.59E-06 6.02E-09 3.61E-01 rs6486122 ARNTL T C 3.09E-05 2.08E-09 2.05E-01 rs333947 CSF1 A G 6.61E-01 rs2081687 CYP7A1 T C 8.30E-01 rs13269725 EYA1 G A 1.18E-02 rs2282227 UBAP2L G A 9.25E-01 rs447990 AFF1 G T 7.58E-02 rs653178 SH2B3 C T 9.76E-01 rs41292412 MIR122 T C 7.51E-01 rs4976033 PIK3R1 G A 9.86E-02 rs11766379 GIMAP7 C T 4.26E-01 rs2737226 TRPS1 T C 2.73E-01 rs2273967 GALNT2 C T 9.85E-01 rs4149307 ABCA1 C T 5.49E-01 rs147244995 AMBRA1 C T 6.26E-01 rs3103310 PPARG G A 3.81E-01 rs573455 CEP164 G A 4.76E-01 rs9902027 TNFSF12 C T 7.65E-01 rs12610709 FIZ1 A G 8.60E-01 rs58895965 HPN,HPN-AS1 A C 3.56E-01 rs55966194 EYA2 C G 4.23E-02 rs117604511 DSCAML1 C A 1.25E-01 rs12924608 LMF1 T C 8.53E-01 rs34934360 IRF2BP1,MYPOP,NANOS2 G A 8.06E-01 rs1003081 HYOU1 T C 9.62E-01 rs79311290 FAM92B G A 6.47E-01 rs117847213 PPP2R1B A G 4.45E-02 rs78025076 MCUB T C 1.13E-01 rs11605837 CPT1A T G 3.52E-02 rs12927305 BANP T C 5.63E-01 rs79287178 LINC02068 A G 6.51E-01 rs34666276 ZNF652 C T 9.51E-01 rs1461729 LOC157273 A G 3.21E-01 rs111831594 KANK2 G A 4.94E-01 rs2070971 GCK T G 3.99E-01 rs12208357 SLC22A1 T C 5.40E-01 rs862320 NFAT5 C T 7.58E-01 rs10774439 CHD4,LPAR5 G A 2.48E-02 rs12921195 MGRN1 A C 8.90E-01 rs4776793 LINC01169 T C 9.10E-01 rs2749026 GSTA2 A G 4.96E-01 Atty. Docket No. UM-42504.601 rs6506033 THOC1 C T 1.48E-07 1.24E-05 4.00E-01 rs9388767 L3MBTL3 A G 2.14E-06 1.82E-06 8.87E-01 rs691066 CYP27A1 C T 5.64E-01 rs11644475 CETP A G 7.35E-02 rs4709746 LOC102724152 C T 1.80E-01 rs2396084 VEGFA G A 9.91E-01 rs77960347 LIPG A G 1.62E-01 rs4800392 CTAGE1,GATA6 C A 4.55E-01 rs12975366 LILRB5 C T 6.48E-02 rs1801689 APOH A C 1.99E-02 rs138436155 CCDC62 C G 3.97E-01 rs61733623 GLB1L G A 3.33E-01 rs6684677 GALNT2 C T 4.97E-01 rs72801474 HSPA4 G A 5.35E-01 rs1509195 LINC01230 C T 7.61E-01 rs62492368 AOC1 A G 2.32E-01 rs6123685 BMP7 G A 6.61E-01 rs117233107 CCND2 G A 9.38E-01 rs67981690 SLCO1B1 G A 3.05E-01 rs8126001 OPRL1 C T 6.26E-02 rs6018652 SULF2 G A 1.08E-01 rs61993685 SLC25A29,YY1 T C 5.59E-01 rs12485478 PPARG G A 5.39E-01 rs11648592 NLRC5 A G 1.05E-01 rs11650182 PNPO A G 3.93E-01 rs1042521 PCK1 G A 8.33E-02 rs1622638 MIR100HG A G 2.80E-01 rs696825 RMI1 C T 1.29E-01 rs2305746 HPN,HPN-AS1 G A 9.70E-01 rs6678052 LYPLAL1-AS1 G A 5.75E-01 rs1171615 SLC16A9 T C 2.45E-01 rs4762753 PDE3A G T 6.37E-01 rs11113118 RIC8B A G 4.16E-01 rs13097947 BTD T C 4.30E-01 rs852388 ACTB C G 2.07E-01 rs954244 LINC01101 G C 2.45E-01 rs7966846 INHBC,INHBE C A 2.52E-01 rs6677420 CTSS A G 3.23E-01 rs4450871 CYTL1 A G 3.57E-03 rs3810291 ZC3H4 A G 9.03E-01 rs79859462 LINC02702 A G 2.28E-01 Atty. Docket No. UM-42504.601 rs12686780 ECM2 T C 1.58E-04 1.24E-07 2.93E-01 rs56257305 FAM100B,RNF157 G A 4.06E-03 9.68E-10 1.45E-02 Atty. Docket No. UM-42504.601 rs34345377 VPS53 G C 5.19E-05 1.03E-05 8.85E-01 rs141136126 MLXIPL G A 2.38E-06 4.50E-06 7.98E-01 Atty. Docket No. UM-42504.601 rs2376867 RELB T C 4.25E-06 1.55E-05 7.12E-01 rs11052977 ALG10 A C 1.37E-02 3.16E-08 2.25E-02 rs140201358 PNPLA2 G C 2.33E-03 rs7916761 CYP26A1 T C 9.53E-01 rs1816164 PDGFC C T 3.09E-01 rs2207132 MAFB A G 4.35E-03 rs3829126 PLA2G12B T G 3.11E-01 rs11643205 NUP93 T C 2.48E-01 rs7191623 HP G A 3.62E-01 rs6589112 ZC3H12C C T 9.99E-01 rs4802269 GIPR A G 3.30E-01 rs2289863 PIAS4 T C 2.64E-01 rs1044531 AKNA C T 1.63E-01 rs111914893 GTF2IRD1 T C 5.83E-01 rs9561643 GPR180 C A 7.30E-01 rs2776937 NRP1 A G 7.26E-01 rs3770781 STRN A G 9.40E-01 rs61830291 LINC01352 C A 5.53E-01 rs12694933 ACVR1C T C 2.07E-01 rs12863082 LINC00348 C T 1.81E-01 rs5402 SLC2A2 A T 9.51E-01 rs11880745 CERS4 T G 4.97E-01 rs275179 C15orf54,ENSG00000259450 A G 8.03E-01 rs117291242 SIGIRR T C 5.97E-02 rs185799410 GNAS T G 2.58E-01 rs4519367 KCNJ2 A G 2.86E-01 rs8021491 FOXN3 T C 1.95E-01 rs113905686 DHX8,LINC00910,NBR1 C A 9.90E-01 rs12638256 PAQR9 A G 9.53E-01 rs2613505 NEGR1 T C 8.43E-01 rs498475 JAZF1-AS1 G A 6.00E-01 rs935168 KCNK3 A G 2.07E-01 rs7596814 PID1 G T 5.24E-01 rs9812100 ITPR1 G A 7.58E-01 rs12793175 AMPD3 A G 7.34E-01 rs282146 TCF15 A G 7.38E-01 rs111406374 GATAD2A A G 2.33E-01 rs35957544 KCNB2 G T 8.80E-01 rs6572807 NID2 G A 7.63E-01 rs1461396 ANO3 G C 5.96E-01 rs57172722 STAB1 G C 9.67E-01 Atty. Docket No. UM-42504.601 rs4425336 FAM13A A G 8.58E-02 2.42E-11 1.59E-04 rs213494 SSBP3 T C 2.38E-04 3.52E-07 3.29E-01 01 01 01 , ancestry in the UKBB. A TraitUnits N Median (Q1,Q3) Frequency (N) Female % 8158 - 5478 (4469) B TraitUnits N Median (Q1,Q3) Frequency (N) F l 2 4 2 77 C Trait Units N Median (Q1,Q3) Frequency (N) Table 6. Summary statistics of the independent SNPs associated with the TG:HDL-C at a genome-wide level in the South Asian, African and Chinese population. High- confiden ce locus within 500kb in rsID the location Nearest Gene Atty. Docket No. UM-42504.601 Europea n ancestry rs96418 4 . UTR3 ZPR1(NM_003904:c.*724G>C,NM_001317086:c.*724G>C) rs70157 rs15285 66 UTR3 LPL(NM_000237:c.*1846T>C) rs12149 rs11648 545 592 intergenic HERPUD1(dist=14385),CETP(dist=2701) rs12603 rs12603 nonsynonymous 26 26 _SNV GCKR rs67542 95 . intergenic LDAH(dist=183293),APOB(dist=18118) rs28597 rs70157 716 66 intergenic LPL(dist=111917),SLC18A1(dist=65679) rs31355 nonsynonymous 06 . _SNV APOA5 rs47839 rs11648 61 592 upstream CETP(dist=968) rs12721 APOC1(NM_001321065:c.*100G>A,NM_001321066:c.*100G>A,NM_ 054 . UTR3 001645:c.*100G>A) rs70157 rs326 66 intronic LPL rs66279 9 . upstream APOA5(dist=571) Table 7. r2 for the independent SNPs associated with TG:HDL-C ratio at a genome-wide level within a 500 Kb range across the different ancestries. Bold rsIDs indicate the SNP is nonsynonymous. Discovery 1 1 1 Atty. Docket No. UM-42504.601 chr8 - LPL rs276 rs15285 s326 European rs276 1 0.0006 0.0024 South Asian rs15285 0.0006 1 0.8707 African rs326 0.0024 0.8707 1 rs2859771 chr8 - LPL / SLC18A1 rs7015766 6 European South Asian 1 rs66279 chr11 - APOA5 rs6589567 rs964184 rs3135506 9 European rs6589567 1 0.1684 0.0076 0.2814 South Asian rs964184 01684 1 01151 02866 African rs3135506 0.0076 0.1151 1 0.0115 Chinese rs662799 0.2814 0.2866 0.0115 1 chr16 - HERPUD1 / rs7278678 rs1214954 rs478396 European South Asian rs12149545 0.6513 1 0.3202 African rs4783961 0.1712 0.3202 1 rs1272105 European African 1 References 1. Brown, A.E., & Walker, M. Genetics of Insulin Resistance and the Metabolic Syndrome. Current cardiology reports 18, 75 (2016). 2. Melvin, A., O'Rahilly, S., & Savage, D. B. Genetic syndromes of severe insulin resistance. Current opinion in genetics & development 50, 60-67 (2018). Atty. Docket No. UM-42504.601 3. Mundi, M.S., Velapati, S., Patel, J., Kellogg, T. A., Abu Dayyeh, B. 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Claims

Atty. Docket No. UM-42504.601 CLAIMS 1. A method comprising: analyzing a biological sample from a subject for ten to 400 variants, wherein at least ten of the variants are from those shown in Table 2.

2. A method of managing insulin resistance, comprising: a) analyzing a biological sample from a subject for at least ten of the variants selected from those listed in Table 2; b) generating a polygenic risk score based on the presence or absence of said variants; and c) treating the subject with an insulin resistance intervention if said risk score indicates a predisposition to insulin resistance.

3. A method of diagnosing insulin resistance or predisposition to insulin resistance comprising: analyzing a biological sample from a subject for at least ten variants from the list of those shown in Table 2.

4. The method of any of the preceding claims, wherein said ten or more variants are 10 to 369 variants.

5. The method of any of the preceding claims, wherein said ten or more variants are 10 to 114 variants.

6. The method of any of the preceding claims, wherein said at least ten of the variants are from those shown in Table 3.

7. The method of any of the preceding claims, wherein said biological sample is obtained from a subject suspected of having insulin resistance.Atty. Docket No. UM-42504.601 8. The method of any of the preceding claims, wherein said biological sample is selected from the group consisting of blood, serum, plasma, saliva, tissue, hair, semen, and urine.

9. The method of any of the preceding claims, wherein said analyzing comprises directly detecting said variants using a molecule assay.

10. The method of claim 9, wherein the molecule assay is a hybridization assay or a sequencing assay.

11. The method of any of the preceding claims, wherein said analyzing comprises indirectly detecting said variants.

12. The method of claim 11, wherein said indirectly detecting comprises assessing gene expression or detecting a mutation in linkage disequilibrium with a variant.

13. The method of claim 2, wherein said polygenic risk score is calculated using an algorithm that accounts for each of the analyzed variants.

14. The method of any of the preceding claims, wherein said variants are associated with one or more of increased triglycerides:high-density lipoprotein ratio, LDL cholesterol levels, systolic blood pressure, hyperlipidemia, hyperglyceridemia, hypertension, waist to hip ratio, type 2 diabetes, alanine aminotransferase levels, or body mass index.

15. The method of any of claims 2-14, wherein said treating comprises applying a weight loss regime.

16. The method of any of claims 2-15, wherein said treating comprises exercise.

17. The method of any of claims 2-16, wherein said treating comprises administration of one or more active agents selected from the group consisting of metformin, a thiazolidinedione, a statin, a blood pressure lowering agent; and any combination thereof.Atty. Docket No. UM-42504.601 18. The method of any of the preceding claims, wherein said variants comprise rs2943645, rs7012814, rs13389219, rs1538742, rs392794, rs1461729, rs2800709, rs2067819, rs114760566, rs9687846, and rs998584.

19. A system comprising: a set or reagents that specifically detect ten to four hundred variants, wherein at least ten of the variants are from those listed in Tables 2 or 3.

20. The system of claim 19, wherein said reagents comprises one or more primers or probe specific for said variants.

21. The system of claim 19 or 20, wherein said reagents comprising sequence reagents.

22. The system of any of claims 19-21, wherein said reagents comprises a microarray.

23. A non-transitory computer-readable storage medium comprising an instruction, wherein when the instruction is run by at least one computer processor, wherein the at least one processor performs operations comprising: a) receiving data identifying the presence or absence of a variant in a biological sample from at least ten of from the list of those shown in Tables 2 or 3; b) generating a polygenic risk score from said data; and c) displaying or reporting said risk score.

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