About

Professor Stephen S. Rich is a faculty member in the Department of Genome Sciences at the University of Virginia School of Medicine. He holds a PhD in Genetics from Purdue University and is engaged in research centered on understanding the genetic epidemiology of complex human diseases. His research focuses on identifying genes that contribute to conditions such as atherosclerosis, stroke, and related risk factors. Dr. Rich's work involves gene mapping, gene discovery, and exploring the functional significance of gene variants, utilizing extensive datasets from studies like the Multi-Ethnic Study of Atherosclerosis (MESA) and others. His research aims to uncover novel genes and pathways that can serve as predictors of disease risk, identify individuals at highest risk, and develop models of disease for potential therapeutic intervention. Dr. Rich employs various genetic analysis methods, including candidate gene studies, genome-wide association scans, and linkage analyses, to investigate the genetic basis of cardiovascular diseases and stroke susceptibility. His contributions are integral to advancing the understanding of genetic factors in complex human diseases.

Research topics

• Biology
• Genetics
• Medicine
• Evolutionary biology
• Endocrinology
• Internal medicine
• Computational biology
• Political Science
• Demography
• Pathology
• Intensive care medicine
• Bioinformatics
• Immunology
• Family medicine
• Computer Science
• Environmental health
• Sociology
• Pediatrics
• Physics
• Astronomy
• Cell biology
• Andrology
• Chemistry
• Physical therapy

Selected publications

• Genetic Predictors of Response to Oral Insulin for Type 1 Diabetes Prevention

  2025-12-09

  articleOpen access

  <p dir="ltr">Objective</p><p dir="ltr">The TrialNet Oral Insulin Prevention Trial (TN07) tested oral insulin to prevent Stage 3 type 1 diabetes in 560 Stage 1 relatives of individuals with type 1 diabetes (T1D). Of the three pre-defined risk strata, participants in Secondary Stratum 1 (SS1), characterized by low first-phase insulin release (n=55), responded significantly better to oral insulin. We aimed to identify genetic factors associated with treatment response.</p><p dir="ltr">Research Design and Methods</p><p dir="ltr">The TEDDY-T1DExomeChip was used to genotype 552 participants with available DNA. Cox models examined associations between response to oral insulin and HLA haplotypes, 33 pre-selected T1D-associated SNPs, the T1D genetic risk score-2 (T1D-GRS2), and type 2 diabetes (T2D)-associated polygenic scores. For primary analyses, p-values were Benjamini-Hochberg (BH)-corrected for multiple comparisons; results not passing correction were considered nominal. </p><p dir="ltr">Results</p><p dir="ltr">GLIS3 rs7020673 was significantly associated with response to oral insulin in SS1 (BH-corrected p-value=0.031 without and p=0.022 with covariate adjustment). Additional nominal associations included better response with HLA-DRB1*04:01-DQA1*03:01-DQB1*03:02 (HR=0.22 vs HR=1.09; unadjusted/adjusted p=0.031/0.045) in SS1, and worse responses with TNFAIP3 and CTLA4 in at least one stratum. In exploratory analyses, participants with T1D-GRS2 >12.5 responded better to oral insulin (HR=0.68) than those with T1D-GRS2 ≤12.5 (HR=2.10; unadjusted/adjusted p=0.003/0.006) in the overall cohort, and lower proinsulin- and obesity-partitioned T2D polygenic scores were associated with greater treatment benefit in SS1 and in another secondary stratum, respectively. </p><p dir="ltr">Conclusions</p><p dir="ltr">Genetic differences distinguish responders from non-responders to oral insulin for T1D prevention. Genetics may enable precision medicine by identifying individuals likely to benefit from T1D-modifying therapies.</p><p><br></p>

  PublisherDOI

• Genetic risk in extremely early onset type 1 diabetes

  medRxiv · 2025-12-19

  preprintOpen access

  Identifying individuals at risk of early onset type 1 diabetes (diagnosed <2 years) would be highly beneficial in reducing risk of severe diabetic ketoacidosis (DKA) for those with extreme autoimmunity. We aimed to investigate whether genetic variation contributes to heterogeneity in age of type 1 diabetes onset, focusing on those diagnosed <2 years and ages previously defined by histological differences. We carried out association testing on 6773 individuals with type 1 diabetes and tested for heterogeneity in Human Leukocyte Antigen (HLA) variants across stratified age groups (594 diagnosed <2 years, 2241 diagnosed 2-7 years, 3094 diagnosed 7-13 years, 844 diagnosed 13+ years). We used a 67 SNP type 1 diabetes genetic risk score (T1D-GRS) to quantify aggregated genetic risk and assessed its utility in screening for type 1 diabetes <2 years. We observed higher T1D-GRSs as age of onset decreased in type 1 diabetes and found that DR3-DQ2 homozygosity was most strongly associated with <2 years onset (log-OR=4.27). The T1D-GRS showed high discriminative ability for <2 years onset type 1 diabetes onset (AUC=0.94) and correctly identified 88% of type 1 diabetes cases at the 85th population centile. We have shown higher genetic risk for very early onset T1D and suggest T1D-GRSs in newborn screening is likely to be particularly sensitive to those with younger type 1 diabetes onset.

  PublisherDOI

• Genetic determinants and genomic consequences of non-leukemogenic somatic point mutations

  UNC Libraries · 2025-12-17

  articleOpen access

  Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as leukemogenic point mutations or mosaic chromosomal alterations (mCAs), are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we use 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantify CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we develop a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We perform a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. We identify seven genes associated with CH-LPMneg (TCL1A, TERT, SMC4, NRIP1, PRDM16, MSRA, SCARB1).Functional analyses of SMC4 and NRIP1 implicated altered hematopoietic stem cell self-renewal and proliferation as the primary mediator of mutation burden in blood. We then perform comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we perform phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.

  PublisherDOI

• &lt;b&gt;Development and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score&lt;/b&gt;

  2025-11-25

  articleOpen access

  &lt;p dir="ltr"&gt;Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we leveraged recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; and T1D GRS&lt;sub&gt;AFR&lt;/sub&gt;. We also developed an updated score (T1D MAPS2) that combines T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; and T1D MAPS. Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; (0.82) and T1D GRS&lt;sub&gt;AFR&lt;/sub&gt; (0.82). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; (0.89 vs. 0.91). However, T1D MAPS2 performed equivalently to T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; in European ancestry (0.91 vs. 0.91) and performed better in non-European ancestry (0.90 vs. 0.82). Overall, these findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.&lt;/p&gt;

  PublisherDOI

• Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants

  Atherosclerosis · 2025-11-26 · 1 citations

  articleOpen access

  BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87 % European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20 % of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: <6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

  PublisherOA PDFDOI

• Association of Epigenetic Age Acceleration and Mitochondrial DNA‐Based Aging Metrics Provides Insights Into Mechanisms of Aging‐Related Diseases

  UNC Libraries · 2025-12-12

  articleOpen access

  Investigating the interplay between mitochondrial DNA (mtDNA) variations and epigenetic aging metrics may elucidate biological mechanisms associated with age-related diseases. We estimated epigenetic age acceleration (EAA) metrics from DNA methylation data and derived mtDNA metrics, including heteroplasmic variants and mtDNA copy number (mtDNA CN) from whole genome sequencing. Linear regressions and meta-analyses were conducted to assess associations between EAA and mtDNA metrics, adjusting for chronological age, self-identified sex, and other covariates in 6,316 participants (58% female, 41% non-White Americans). Mediation analysis was conducted to examine whether EAA mediated the relationship between mtDNA CN and metabolic traits. A higher burden of rare heteroplasmic variants was associated with accelerations of first-generation EAA metrics, while a lower level mtDNA CN was associated with accelerations of second- and third-generation EAA metrics. For example, one standard deviation (SD) higher MSS, a score based on the predicted functions of rare heteroplasmic variants, was associated with a 0.22-year higher EAA by the Hannum method (p&thinsp;=&thinsp;1.3E-6) among all participants, while one SD lower mtDNA CN was associated with higher DunedinPACE (&beta;&thinsp;=&thinsp;-0.005, p&thinsp;=&thinsp;6.0E-4). No significant association was observed between the heteroplasmy burden of common variants and EAAs. Furthermore, we observed DunedinPACE mediated 11.1% and 10.8% of the associations of mtDNA CN with obesity and T2DM in older FHS participants, respectively. Our analysis indicated that higher levels of heteroplasmy burden of rare variants and lower mtDNA CN were associated with accelerated epigenetic aging, and these associations showed stronger magnitudes among older participants.

  PublisherDOI

• Machine learning‐based clustering identifies obesity subgroups with differential multi‐omics profiles and metabolic patterns

  UNC Libraries · 2025-11-06

  articleOpen access

  OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct cardiometabolic disease patterns. METHODS: We performed machine learning-based, integrative unsupervised clustering to identify proteomics- and metabolomics-defined subpopulations of individuals living with obesity (BMI&thinsp;&ge;&thinsp;30&thinsp;kg/m²), leveraging data from 243 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort. Omics that contributed to the observed clusters were functionally characterized. We performed multivariate regression to assess whether the individuals in each cluster demonstrated differential patterns of cardiometabolic traits. RESULTS: We identified two distinct clusters (iCluster1 and 2). iCluster2 had significantly higher average BMI values, fasting blood glucose, and inflammation. iCluster1 was associated with higher levels of total cholesterol and high-density lipoprotein cholesterol. Pathways mediating cell growth, lipogenesis, and energy expenditures were positively associated with iCluster1. Inflammatory response and insulin resistance pathways were positively associated with iCluster2. CONCLUSIONS: Although the two identified clusters may represent progressive obesity-related pathologic processes measured at different stages, other mechanisms in combination could also underpin the identified clusters given no significant age difference between the comparative groups. For instance, clusters may reflect differences in dietary/behavioral patterns or differential rates of metabolic damage.

  PublisherDOI

• &lt;b&gt;Development and Validation of a Type 1 Diabetes Multi-Ancestry Polygenic Score&lt;/b&gt;

  2025-11-14

  articleOpen access

  &lt;p dir="ltr"&gt;Polygenic scores strongly predict type 1 diabetes risk, but most scores were developed in European-ancestry populations. In this study, we leveraged recent multi-ancestry genome-wide association studies to create a type 1 diabetes multi-ancestry polygenic score (T1D MAPS). We trained the score in the Mass General Brigham (MGB) Biobank (372 individuals with type 1 diabetes) and tested the score in the All of Us program (86 individuals with type 1 diabetes). We evaluated the area under the receiver operating characteristic curve (AUC), and we compared the AUC to two published single-ancestry scores: T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; and T1D GRS&lt;sub&gt;AFR&lt;/sub&gt;. We also developed an updated score (T1D MAPS2) that combines T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; and T1D MAPS. Among individuals with non-European ancestry, the AUC of T1D MAPS was 0.90, significantly higher than T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; (0.82) and T1D GRS&lt;sub&gt;AFR&lt;/sub&gt; (0.82). Among individuals with European ancestry, the AUC of T1D MAPS was slightly lower than T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; (0.89 vs. 0.91). However, T1D MAPS2 performed equivalently to T1D GRS2&lt;sub&gt;EUR&lt;/sub&gt; in European ancestry (0.91 vs. 0.91) and performed better in non-European ancestry (0.90 vs. 0.82). Overall, these findings advance the accuracy of type 1 diabetes genetic risk prediction across diverse populations.&lt;/p&gt;

  PublisherDOI

• Type 2 Diabetes Genetic Risk and Type 1 Diabetes Heterogeneity and Progression

  Diabetes · 2025-12-10 · 1 citations

  articleOpen access

  Insulin secretion varies widely in preclinical type 1 diabetes. To understand the pathogenesis of this metabolic heterogeneity, we asked whether genetic predisposition to type 2 diabetes, quantified by a type 2 diabetes genetic risk score (T2D-GRS), modulates β-cell function and disease progression in individuals at risk of type 1 diabetes. We analyzed 4,324 islet autoantibody–positive TrialNet Pathway to Prevention participants with genome-wide genotyping and oral glucose tolerance testing. Both T2D-GRS and the type 1 diabetes genetic risk score 2 (T1D-GRS2) differed significantly across five previously described groups defined by C-peptide area under the curve (AUC; a measure of insulin secretion). The highest C-peptide AUC group, compared with the lowest, had significantly higher T2D-GRS, lower T1D-GRS2, higher BMI z-score, greater insulin resistance, older age, and lower prevalence of male participants; multiple islet autoantibody positivity; and IA-2 or insulin autoantibody positivity. Progression to clinical (stage 3) type 1 diabetes was significantly associated with T1D-GRS2 across all groups and with T2D-GRS in all but the lowest C-peptide AUC group. In conclusion, type 2 diabetes genetic burden shapes metabolic heterogeneity and accelerates progression in preclinical type 1 diabetes. These results support the evaluation of type 2 diabetes–related mechanisms as targets to improve the prediction and prevention of type 1 diabetes. ARTICLE HIGHLIGHTS: Heterogeneity in β-cell function is a barrier to precision medicine in type 1 diabetes. We asked whether type 2 diabetes-associated genes influence insulin secretion and progression to clinical type 1 diabetes in autoantibody-positive individuals. A type 2 diabetes genetic risk score was associated with higher C-peptide area under the curve (AUC) and increased clinical type 1 diabetes risk in all but the lowest C-peptide AUC subgroup. Addressing type 2 diabetes mechanisms could improve type 1 diabetes prediction and prevention.

  PublisherOA PDFDOI

• Dissecting Genetic and Environmental Determinants of Plasma Molecular Signatures and Their Link to Type 2 Diabetes Risk

  medRxiv · 2025-11-28

  preprintOpen access

  Background: Type 2 diabetes (T2D) is a heterogeneous disease shaped by both genetic, environmental, cultural, and socioeconomic factors, with well-documented disparities in incidence across populations. The molecular pathways underlying these disparities, however, remain poorly understood. Plasma metabolites and proteins integrate both genetic and environmental influences on type 2 diabetes (T2D) risk, providing insight into disease mechanisms. We aimed to quantify the variance in these molecular profiles explained by environmental and genetic ancestry domains and to apply causal inference approaches to identify environmentally and genetic ancestry influenced pathways contributing to T2D risk. Methods: We analyzed plasma proteomic and metabolomic profiles from 3,360 MESA participants (51.6% female), and in 1,333 participants from the Women's Health Initiative. To characterize the sources of variance in plasma proteomic and metabolomic profiles, we performed variance decomposition partitioning into four domains: biological (age, sex, BMI), genetic ancestry (principal components), lifestyle (smoking, alcohol intake, diet), and social determinants (self-reported race and ethnicity, income, education). To assess causal pathways towards T2D risk, we applied two-sample Mendelian Randomization to disentangle environmental and genetic contributors to T2D risk. Results: The largest share of variance in proteomic and metabolomic profiles was explained by biological and lifestyle factors, while race and ethnicity and genetic ancestry accounted for smaller but non-redundant contributions. Genetic ancestry was primarily associated with lipid and apolipoprotein variation, whereas race and ethnicity and socioeconomic factors were associated with immune and inflammatory signatures. Environmentally influenced metabolites (e.g., diacylglycerols, phosphatidylethanolamines, lysophosphatidylcholines) and vascular-inflammatory proteins were consistently linked to higher T2D risk, while genetic ancestry influenced triglycerides and IGFBP3 reflected inherited risk pathways. Mediation analyses showed that selected lipids and proteins (e.g., IGFBP2, HGF, SSC4D) explained 10-25% of racial/ethnic disparities in T2D. Mendelian randomization identified causal roles for seven lipid species and IGFBP3 in T2D risk. Conclusions: Our results reveal both genetic and non-genetic sources of variation in proteomic and metabolomic profiles, uncovering environmental and genetic pathways contributing to T2D risk. These findings advance precision medicine by identifying modifiable molecular mediators of disparities and potential causal targets for prevention.

  PublisherOA PDFDOI

Recent grants

• NIH Grant RC1HL099911

  NIH · $1.0M · 2012

• NIH Grant UC2HL103010

  NIH · $1.5M · 2013

• NIH Grant RC2HL103010

  NIH · $817k · 2011

• NIH Grant P50AR045231

  NIH · $7.2M · 2004

• NIH Grant U01HL120393

  NIH · $5.6M · 2021

Frequent coauthors

• Bradford B. Worrall

  1924 shared

• Donna K. Arnett

  University of Charleston

  1714 shared

• James F. Meschia

  Mayo Clinic in Florida

  1706 shared

• Myriam Fornage

  The University of Texas Health Science Center at Houston

  1669 shared

• Braxton D. Mitchell

  1543 shared

• Cecilia M. Lindgren

  1443 shared

• Cathy C. Laurie

  University of Washington

  1393 shared

• Vincent Thijs

  University of Melbourne

  1390 shared

Education

• Postdoctoral Research Associate, Animal Sciences

  Purdue University

  1981

• Ph.D., Animal Sciences

  Purdue University

  1979

• M.S., Animal Sciences

  Purdue University

  1975

• B.S., Mathematics

  North Carolina State University

  1973