Risk SNP-Mediated Promoter-Enhancer Switching Drives Prostate Cancer through lncRNA PCAT19.

The prostate cancer (PCa) risk-associated SNP rs11672691 is positively associated with aggressive disease at diagnosis. We showed that rs11672691 maps to the promoter of a short isoform of long noncoding RNA PCAT19 (PCAT19-short), which is in the third intron of the long isoform (PCAT19-long). The risk variant is associated with decreased and increased levels of PCAT19-short and PCAT19-long, respectively. Mechanistically, the risk SNP region is bifunctional with both promoter and enhancer activity. The risk variants of rs11672691 and its LD SNP rs887391 decrease binding of transcription factors NKX3.1 and YY1 to the promoter of PCAT19-short, resulting in weaker promoter but stronger enhancer activity that subsequently activates PCAT19-long. PCAT19-long interacts with HNRNPAB to activate a subset of cell-cycle genes associated with PCa progression, thereby promoting PCa tumor growth and metastasis. Taken together, these findings reveal a risk SNP-mediated promoter-enhancer switching mechanism underlying both initiation and progression of aggressive PCa.

A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression.

Genome-wide association studies (GWASs) implicate the PHACTR1 locus (6p24) in risk for five vascular diseases, including coronary artery disease, migraine headache, cervical artery dissection, fibromuscular dysplasia, and hypertension. Through genetic fine mapping, we prioritized rs9349379, a common SNP in the third intron of the PHACTR1 gene, as the putative causal variant. Epigenomic data from human tissue revealed an enhancer signature at rs9349379 exclusively in aorta, suggesting a regulatory function for this SNP in the vasculature. CRISPR-edited stem cell-derived endothelial cells demonstrate rs9349379 regulates expression of endothelin 1 (EDN1), a gene located 600 kb upstream of PHACTR1. The known physiologic effects of EDN1 on the vasculature may explain the pattern of risk for the five associated diseases. Overall, these data illustrate the integration of genetic, phenotypic, and epigenetic analysis to identify the biologic mechanism by which a common, non-coding variant can distally regulate a gene and contribute to the pathogenesis of multiple vascular diseases.

A bioinformatics toolbox to prioritize causal genetic variants in candidate regions.

This review addresses the significant challenge of identifying causal genetic variants within quantitative trait loci (QTLs) for complex traits and diseases. Despite progress in detecting the ever-larger number of such loci, establishing causality remains daunting. We advocate for integrating bioinformatics and multiomics analyses to streamline the prioritization of candidate genes' variants. Our case study on the Pla2g4e gene, identified previously as a positional candidate obesity gene through genetic mapping and expression studies, demonstrates how applying multiomic data filtered through regulatory elements containing SNPs can refine the search for causative variants. This approach can yield results that guide more efficient experimental strategies, accelerating genetic research toward functional validation and therapeutic development.

An integrative framework to prioritize genes in more than 500 loci associated with body mass index.

Obesity is a major risk factor for a myriad of diseases, affecting >600 million people worldwide. Genome-wide association studies (GWASs) have identified hundreds of genetic variants that influence body mass index (BMI), a commonly used metric to assess obesity risk. Most variants are non-coding and likely act through regulating genes nearby. Here, we apply multiple computational methods to prioritize the likely causal gene(s) within each of the 536 previously reported GWAS-identified BMI-associated loci. We performed summary-data-based Mendelian randomization (SMR), FINEMAP, DEPICT, MAGMA, transcriptome-wide association studies (TWASs), mutation significance cutoff (MSC), polygenic priority score (PoPS), and the nearest gene strategy. Results of each method were weighted based on their success in identifying genes known to be implicated in obesity, ranking all prioritized genes according to a confidence score (minimum: 0; max: 28). We identified 292 high-scoring genes (≥11) in 264 loci, including genes known to play a role in body weight regulation (e.g., DGKI, ANKRD26, MC4R, LEPR, BDNF, GIPR, AKT3, KAT8, MTOR) and genes related to comorbidities (e.g., FGFR1, ISL1, TFAP2B, PARK2, TCF7L2, GSK3B). For most of the high-scoring genes, however, we found limited or no evidence for a role in obesity, including the top-scoring gene BPTF. Many of the top-scoring genes seem to act through a neuronal regulation of body weight, whereas others affect peripheral pathways, including circadian rhythm, insulin secretion, and glucose and carbohydrate homeostasis. The characterization of these likely causal genes can increase our understanding of the underlying biology and offer avenues to develop therapeutics for weight loss.

mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data.

Gene-based association tests aggregate multiple SNP-trait associations into sets defined by gene boundaries and are widely used in post-GWAS analysis. A common approach for gene-based tests is to combine SNPs associations by computing the sum of χ2 statistics. However, this strategy ignores the directions of SNP effects, which could result in a loss of power for SNPs with masking effects, e.g., when the product of two SNP effects and the linkage disequilibrium (LD) correlation is negative. Here, we introduce "mBAT-combo," a set-based test that is better powered than other methods to detect multi-SNP associations in the context of masking effects. We validate the method through simulations and applications to real data. We find that of 35 blood and urine biomarker traits in the UK Biobank, 34 traits show evidence for masking effects in a total of 4,273 gene-trait pairs, indicating that masking effects is common in complex traits. We further validate the improved power of our method in height, body mass index, and schizophrenia with different GWAS sample sizes and show that on average 95.7% of the genes detected only by mBAT-combo with smaller sample sizes can be identified by the single-SNP approach with a 1.7-fold increase in sample sizes. Eleven genes significant only in mBAT-combo for schizophrenia are confirmed by functionally informed fine-mapping or Mendelian randomization integrating gene expression data. The framework of mBAT-combo can be applied to any set of SNPs to refine trait-association signals hidden in genomic regions with complex LD structures.

Enhancer variants on chromosome 2p14 regulating SPRED2 and ACTR2 act as a signal amplifier to protect against rheumatoid arthritis.

Genome-wide association studies (GWASs) have repeatedly reported multiple non-coding single-nucleotide polymorphisms (SNPs) at 2p14 associated with rheumatoid arthritis (RA), but their functional roles in the pathological mechanisms of RA remain to be explored. In this study, we integrated a series of bioinformatics and functional experiments and identified three intronic RA SNPs (rs1876518, rs268131, and rs2576923) within active enhancers that can regulate the expression of SPRED2 directly. At the same time, SPRED2 and ACTR2 influence each other as a positive feedback signal amplifier to strengthen the protective role in RA by inhibiting the migration and invasion of rheumatoid fibroblast-like synoviocytes (FLSs). In particular, the transcription factor CEBPB preferentially binds to the rs1876518-T allele to increase the expression of SPRED2 in FLSs. Our findings decipher the molecular mechanisms behind the GWAS signals at 2p14 for RA and emphasize SPRED2 as a potential candidate gene for RA, providing a potential target and direction for precise treatment of RA.

Polygenic risk prediction: why and when out-of-sample prediction R2 can exceed SNP-based heritability.

In polygenic score (PGS) analysis, the coefficient of determination (R2) is a key statistic to evaluate efficacy. R2 is the proportion of phenotypic variance explained by the PGS, calculated in a cohort that is independent of the genome-wide association study (GWAS) that provided estimates of allelic effect sizes. The SNP-based heritability (hSNP2, the proportion of total phenotypic variances attributable to all common SNPs) is the theoretical upper limit of the out-of-sample prediction R2. However, in real data analyses R2 has been reported to exceed hSNP2, which occurs in parallel with the observation that hSNP2 estimates tend to decline as the number of cohorts being meta-analyzed increases. Here, we quantify why and when these observations are expected. Using theory and simulation, we show that if heterogeneities in cohort-specific hSNP2 exist, or if genetic correlations between cohorts are less than one, hSNP2 estimates can decrease as the number of cohorts being meta-analyzed increases. We derive conditions when the out-of-sample prediction R2 will be greater than hSNP2 and show the validity of our derivations with real data from a binary trait (major depression) and a continuous trait (educational attainment). Our research calls for a better approach to integrating information from multiple cohorts to address issues of between-cohort heterogeneity.

Neurog2 regulates Isl1 to modulate horizontal cell number.

The population sizes of different retinal cell types vary between different strains of mice, and that variation can be mapped to genomic loci in order to identify its polygenic origin. In some cases, controlling genes act independently, whereas in other instances, they exhibit epistasis. Here, we identify an epistatic interaction revealed through the mapping of quantitative trait loci from a panel of recombinant inbred strains of mice. The population of retinal horizontal cells exhibits a twofold variation in number, mapping to quantitative trait loci on chromosomes 3 and 13, where these loci are shown to interact epistatically. We identify a prospective genetic interaction underlying this, mediated by the bHLH transcription factor Neurog2, at the chromosome 3 locus, functioning to repress the LIM homeodomain transcription factor Isl1, at the chromosome 13 locus. Using single and double conditional knockout mice, we confirm the countervailing actions of each gene, and validate in vitro a crucial role for two single nucleotide polymorphisms in the 5'UTR of Isl1, one of which yields a novel E-box, mediating the repressive action of Neurog2.

A high-dimensional omnibus test for set-based association analysis.

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.

Neural Network Connectivity Following Opioid Dependence is Altered by a Common Genetic Variant in the µ-Opioid Receptor, OPRM1 A118G.

Opioid use disorder is a chronic, relapsing disease associated with persistent changes in brain plasticity. A common single nucleotide polymorphism (SNP) in the µ-opioid receptor gene, OPRM1 A118G, is associated with altered vulnerability to opioid addiction. Reconfiguration of neuronal connectivity may explain dependence risk in individuals with this SNP. Mice with the equivalent Oprm1 variant, A112G, demonstrate sex-specific alterations in the rewarding properties of morphine and heroin. To determine whether this SNP influences network-level changes in neuronal activity, we compared FOS expression in male and female mice that were opioid-naive or opioid-dependent. Network analyses identified significant differences between the AA and GG Oprm1 genotypes. Based on several graph theory metrics, including small-world analysis and degree centrality, we show that GG females in the opioid-dependent state exhibit distinct patterns of connectivity compared to other groups of the same genotype. Using a network control theory approach, we identified key cortical brain regions that drive the transition between opioid-naive and opioid-dependent brain states; however, these regions are less influential in GG females leading to sixfold higher average minimum energy needed to transition from the acute to the dependent state. In addition, we found that the opioid-dependent brain state is significantly less stable in GG females compared to other groups. Collectively, our findings demonstrate sex- and genotype-specific modifications in local, mesoscale, and global properties of functional brain networks following opioid exposure and provide a framework for identifying genotype differences in specific brain regions that play a role in opioid dependence.

Critical amino acid residues regulating TRPA1 Zn2+ response: A comparative study across species.

Cellular zinc ions (Zn2+) are crucial for signal transduction in various cell types. The transient receptor potential (TRP) ankyrin 1 (TRPA1) channel, known for its sensitivity to intracellular Zn2+ ([Zn2+]i), has been a subject of limited understanding regarding its molecular mechanism. Here, we used metal ion-affinity prediction, three-dimensional structural modeling, and mutagenesis, utilizing data from the Protein Data Bank and AlphaFold database, to elucidate the [Zn2+]i binding domain (IZD) structure composed by specific AAs residues in human (hTRPA1) and chicken TRPA1 (gTRPA1). External Zn2+ induced activation in hTRPA1, while not in gTRPA1. Moreover, external Zn2+ elevated [Zn2+]i specifically in hTRPA1. Notably, both hTRPA1 and gTRPA1 exhibited inherent sensitivity to [Zn2+]i, as evidenced by their activation upon internal Zn2+ application. The critical AAs within IZDs, specifically histidine at 983/984, lysine at 711/717, tyrosine at 714/720, and glutamate at 987/988 in IZD1, and H983/H984, tryptophan at 710/716, E854/E855, and glutamine at 979/980 in IZD2, were identified in hTRPA1/gTRPA1. Furthermore, mutations, such as the substitution of arginine at 919 (R919) to H919, abrogated the response to external Zn2+ in hTRPA1. Among single-nucleotide polymorphisms (SNPs) at Y714 and a triple SNP at R919 in hTRPA1, we revealed that the Zn2+ responses were attenuated in mutants carrying the Y714 and R919 substitution to asparagine and proline, respectively. Overall, this study unveils the intrinsic sensitivity of hTRPA1 and gTRPA1 to [Zn2+]i mediated through IZDs. Furthermore, our findings suggest that specific SNP mutations can alter the responsiveness of hTRPA1 to extracellular and intracellular Zn2+.

Whole-genome resequencing facilitates the development of a 50K single nucleotide polymorphism genotyping array for Scots pine (Pinus sylvestris L.) and its transferability to other pine species.

Scots pine (Pinus sylvestris L.) is one of the most widespread and economically important conifer species in the world. Applications like genomic selection and association studies, which could help accelerate breeding cycles, are challenging in Scots pine because of its large and repetitive genome. For this reason, genotyping tools for conifer species, and in particular for Scots pine, are commonly based on transcribed regions of the genome. In this article, we present the Axiom Psyl50K array, the first single nucleotide polymorphism (SNP) genotyping array for Scots pine based on whole-genome resequencing, that represents both genic and intergenic regions. This array was designed following a two-step procedure: first, 192 trees were sequenced, and a 430K SNP screening array was constructed. Then, 480 samples, including haploid megagametophytes, full-sib family trios, breeding population, and range-wide individuals from across Eurasia were genotyped with the screening array. The best 50K SNPs were selected based on quality, replicability, distribution across the draft genome assembly, balance between genic and intergenic regions, and genotype-environment and genotype-phenotype associations. Of the final 49 877 probes tiled in the array, 20 372 (40.84%) occur inside gene models, while the rest lie in intergenic regions. We also show that the Psyl50K array can yield enough high-confidence SNPs for genetic studies in pine species from North America and Eurasia. This new genotyping tool will be a valuable resource for high-throughput fundamental and applied research of Scots pine and other pine species.

SNP rs4971059 predisposes to breast carcinogenesis and chemoresistance via TRIM46-mediated HDAC1 degradation.

Identification of the driving force behind malignant transformation holds the promise to combat the relapse and therapeutic resistance of cancer. We report here that the single nucleotide polymorphism (SNP) rs4971059, one of 65 new breast cancer risk loci identified in a recent genome-wide association study (GWAS), functions as an active enhancer of TRIM46 expression. Recreating the G-to-A polymorphic switch caused by the SNP via CRISPR/Cas9-mediated homologous recombination leads to an overt upregulation of TRIM46. We find that TRIM46 is a ubiquitin ligase that targets histone deacetylase HDAC1 for ubiquitination and degradation and that the TRIM46-HDAC1 axis regulates a panel of genes, including ones critically involved in DNA replication and repair. Consequently, TRIM46 promotes breast cancer cell proliferation and chemoresistance in vitro and accelerates tumor growth in vivo. Moreover, TRIM46 is frequently overexpressed in breast carcinomas, and its expression is correlated with lower HDAC1 expression, higher histological grades, and worse prognosis of the patients. Together, our study links SNP rs4971059 to replication and to breast carcinogenesis and chemoresistance and support the pursuit of TRIM46 as a potential target for breast cancer intervention.

Partitioning gene-mediated disease heritability without eQTLs.

Unknown SNP-to-gene regulatory architecture complicates efforts to link noncoding GWAS associations with genes implicated by sequencing or functional studies. eQTLs are often used to link SNPs to genes, but expression in bulk tissue explains a small fraction of disease heritability. A simple but successful approach has been to link SNPs with nearby genes via base pair windows, but genes may often be regulated by SNPs outside their window. We propose the abstract mediation model (AMM) to estimate (1) the fraction of heritability mediated by the closest or kth-closest gene to each SNP and (2) the mediated heritability enrichment of a gene set (e.g., genes with rare-variant associations). AMM jointly estimates these quantities by matching the decay in SNP enrichment with distance from genes in the gene set. Across 47 complex traits and diseases, we estimate that the closest gene to each SNP mediates 27% (SE: 6%) of heritability and that a substantial fraction is mediated by genes outside the ten closest. Mendelian disease genes are strongly enriched for common-variant heritability; for example, just 21 dyslipidemia genes mediate 25% of LDL heritability (211× enrichment, p = 0.01). Among brain-related traits, genes involved in neurodevelopmental disorders are only about 4× enriched, but gene expression patterns are highly informative, as they have detectable differences in per-gene heritability even among weakly brain-expressed genes.

Estrogen receptor alpha and NFATc1 bind to a bone mineral density-associated SNP to repress WNT5B in osteoblasts.

Genetic factors and estrogen deficiency contribute to the development of osteoporosis. The single-nucleotide polymorphism (SNP) rs2887571 is predicted from genome-wide association studies (GWASs) to associate with osteoporosis but has had an unknown mechanism. Analysis of osteoblasts from 110 different individuals who underwent joint replacement revealed that the genotype of rs2887571 correlates with WNT5B expression. Analysis of our ChIP-sequencing data revealed that SNP rs2887571 overlaps with an estrogen receptor alpha (ERα) binding site. Here we show that 17ß-estradiol (E2) suppresses WNT5B expression and further demonstrate the mechanism of ERα binding at the enhancer containing rs2887571 to suppress WNT5B expression differentially in each genotype. ERα interacts with NFATc1, which is predicted to bind directly at rs2887571. CRISPR-Cas9 and ChIP-qPCR experiments confirm differential regulation of WNT5B between each allele. Homozygous GG has a higher binding affinity for ERα than homozygous AA and results in greater suppression of WNT5B expression. Functionally, WNT5B represses alkaline phosphatase expression and activity, decreasing osteoblast differentiation and mineralization. Furthermore, WNT5B increases interleukin-6 expression and suppresses E2-induced expression of alkaline phosphatase during osteoblast differentiation. We show that WNT5B suppresses the differentiation of osteoblasts via receptor tyrosine kinase-like orphan receptor 1/2 (ROR1/2), which activates DVL2/3/RAC1/CDC42/JNK/SIN3A signaling and inhibits ß-catenin activity. Together, our data provide mechanistic insight into how ERα and NFATc1 regulate the non-coding SNP rs2887571, as well as the function of WNT5B on osteoblasts, which could provide alternative therapeutic targets for osteoporosis.

DeLIVR: a deep learning approach to IV regression for testing nonlinear causal effects in transcriptome-wide association studies.

Transcriptome-wide association studies (TWAS) have been increasingly applied to identify (putative) causal genes for complex traits and diseases. TWAS can be regarded as a two-sample two-stage least squares method for instrumental variable (IV) regression for causal inference. The standard TWAS (called TWAS-L) only considers a linear relationship between a gene's expression and a trait in stage 2, which may lose statistical power when not true. Recently, an extension of TWAS (called TWAS-LQ) considers both the linear and quadratic effects of a gene on a trait, which however is not flexible enough due to its parametric nature and may be low powered for nonquadratic nonlinear effects. On the other hand, a deep learning (DL) approach, called DeepIV, has been proposed to nonparametrically model a nonlinear effect in IV regression. However, it is both slow and unstable due to the ill-posed inverse problem of solving an integral equation with Monte Carlo approximations. Furthermore, in the original DeepIV approach, statistical inference, that is, hypothesis testing, was not studied. Here, we propose a novel DL approach, called DeLIVR, to overcome the major drawbacks of DeepIV, by estimating a related but different target function and including a hypothesis testing framework. We show through simulations that DeLIVR was both faster and more stable than DeepIV. We applied both parametric and DL approaches to the GTEx and UK Biobank data, showcasing that DeLIVR detected additional 8 and 7 genes nonlinearly associated with high-density lipoprotein (HDL) cholesterol and low-density lipoprotein (LDL) cholesterol, respectively, all of which would be missed by TWAS-L, TWAS-LQ, and DeepIV; these genes include BUD13 associated with HDL, SLC44A2 and GMIP with LDL, all supported by previous studies.

Human Genetic Determinants of Viral Diseases.

Much progress has been made in the identification of specific human gene variants that contribute to enhanced susceptibility or resistance to viral diseases. Herein we review multiple discoveries made with genome-wide or candidate gene approaches that have revealed significant insights into virus-host interactions. Genetic factors that have been identified include genes encoding virus receptors, receptor-modifying enzymes, and a wide variety of innate and adaptive immunity-related proteins. We discuss a range of pathogenic viruses, including influenza virus, respiratory syncytial virus, human immunodeficiency virus, human T cell leukemia virus, human papilloma virus, hepatitis B and C viruses, herpes simplex virus, norovirus, rotavirus, parvovirus, and Epstein-Barr virus. Understanding the genetic underpinnings that affect infectious disease outcomes should allow tailored treatment and prevention approaches in the future.

A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants.

Quantifying an individual's risk for common diseases is an important goal of precision health. The polygenic risk score (PRS), which aggregates multiple risk alleles of candidate diseases, has emerged as a standard approach for identifying high-risk individuals. Although several studies have been performed to benchmark the PRS calculation tools and assess their potential to guide future clinical applications, some issues remain to be further investigated, such as lacking (i) various simulated data with different genetic effects; (ii) evaluation of machine learning models and (iii) evaluation on multiple ancestries studies. In this study, we systematically validated and compared 13 statistical methods, 5 machine learning models and 2 ensemble models using simulated data with additive and genetic interaction models, 22 common diseases with internal training sets, 4 common diseases with external summary statistics and 3 common diseases for trans-ancestry studies in UK Biobank. The statistical methods were better in simulated data from additive models and machine learning models have edges for data that include genetic interactions. Ensemble models are generally the best choice by integrating various statistical methods. LDpred2 outperformed the other standalone tools, whereas PRS-CS, lassosum and DBSLMM showed comparable performance. We also identified that disease heritability strongly affected the predictive performance of all methods. Both the number and effect sizes of risk SNPs are important; and sample size strongly influences the performance of all methods. For the trans-ancestry studies, we found that the performance of most methods became worse when training and testing sets were from different populations.

A genomics perspective of personalized prevention and management of obesity.

This review discusses the landscape of personalized prevention and management of obesity from a nutrigenetics perspective. Focusing on macronutrient tailoring, we discuss the impact of genetic variation on responses to carbohydrate, lipid, protein, and fiber consumption. Our bioinformatic analysis of genomic variants guiding macronutrient intake revealed enrichment of pathways associated with circadian rhythm, melatonin metabolism, cholesterol and lipoprotein remodeling and PPAR signaling as potential targets of macronutrients for the management of obesity in relevant genetic backgrounds. Notably, our data-based in silico predictions suggest the potential of repurposing the SYK inhibitor fostamatinib for obesity treatment in relevant genetic profiles. In addition to dietary considerations, we address genetic variations guiding lifestyle changes in weight management, including exercise and chrononutrition. Finally, we emphasize the need for a refined understanding and expanded research into the complex genetic landscape underlying obesity and its management.

Polygenic subtype identified in ACCORD trial displays a favorable type 2 diabetes phenotype in the UKBiobank population.

INTRODUCTION: We previously identified a genetic subtype (C4) of type 2 diabetes (T2D), benefitting from intensive glycemia treatment in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. Here, we characterized the population of patients that met the C4 criteria in the UKBiobank cohort. RESEARCH DESIGN AND METHODS: Using our polygenic score (PS), we identified C4 individuals in the UKBiobank and tested C4 status with risk of developing T2D, cardiovascular disease (CVD) outcomes, and differences in T2D medications. RESULTS: C4 individuals were less likely to develop T2D, were slightly older at T2D diagnosis, had lower HbA1c values, and were less likely to be prescribed T2D medications (P < .05). Genetic variants in MAS1 and IGF2R, major components of the C4 PS, were associated with fewer overall T2D prescriptions. CONCLUSION: We have confirmed C4 individuals are a lower risk subpopulation of patients with T2D.