Biology subjects

Cade, B. E.

Publications and source records attributed to Cade, B. E..

At least 19 recordsLinked to original sources

bioRxiv2025

cellSTAAR: Incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of non-coding regions

Whole genome sequencing (WGS) studies have identified hundreds of millions of rare variants (RVs) and have enabled RV association tests (RVATs) of these variants with complex traits and diseases. Analysis of non-coding variants is challenged by the considerable variability in regulatory function which candidate Cis-Regulatory Elements (cCREs) exhibit across cell types. We propose cellSTAAR, which integrates WGS data with single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and variant sets. To reflect the uncertainty in cCRE-gene linking, cellSTAAR also links cCREs to their target genes using an omnibus framework which aggregates results from a variety of popular linking approaches. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data to four lipids phenotypes: LDL cholesterol, a binary variable corresponding to high LDL cholesterol, HDL cholesterol, and triglycerides. We also provide replication results for all four phenotypes using UK Biobank (N = 190,000). Evidence from simulation studies and our real data analysis demonstrates that cellSTAAR boosts power and improves interpretation of RVATs of cCREs.

genetics↗

bioRxiv2023

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

genetics↗

bioRxiv2023

Validation of human telomere length trans-ancestry meta-analysis association signals identifies POP5 and KBTBD6 as novel human telomere length regulation genes

Telomere length genome-wide association studies (GWAS) have become well-powered to detect novel genes in telomere length regulation. However, no prior work has validated these putative novel genes to confirm the contribution of GWAS loci to telomere length regulation. We conducted a trans-ancestry meta-analysis of 211,369 individuals. Through enrichment analyses of chromatin state and cell-type heritability we identified blood and immune cells as the most relevant cell type to examine telomere length association signals. We validated specific GWAS associations by overexpressing KBTBD6, a component of an E3 ubiquitin ligase complex, and POP5, a component of the Ribonuclease P/MRP complex, and demonstrating that both lengthened telomeres as predicted by our statistical analyses. CRISPR/Cas9 deletion of the predicted causal regions of these association peaks in K562 immortalized blood cells reduced expression of these genes, demonstrating that these loci are related to transcriptional regulation of KBTBD6 and POP5, respectively. Together our results demonstrate the utility of telomere length GWAS in the identification of novel telomere length regulation mechanisms and highlight the importance of the proteasome-ubiquitin pathway in telomere length regulation.

genetics↗

bioRxiv2023

Structural variation across 138,134 samples in the TOPMed consortium

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

genomics↗

bioRxiv2021

Clonal hematopoiesis is driven by aberrant activation of TCL1A

A diverse set of driver genes, such as regulators of DNA methylation, RNA splicing, and chromatin remodeling, have been associated with pre-malignant clonal expansion of hematopoietic stem cells (HSCs). The factors mediating expansion of these mutant clones remain largely unknown, partially due to a paucity of large cohorts with longitudinal blood sampling. To circumvent this limitation, we developed and validated a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate). Applying PACER to 5,071 persons with clonal hematopoiesis accurately recapitulated the known fitness effects due to different driver mutations. A genome-wide association study of PACER revealed that a common inherited polymorphism in the TCL1A promoter was associated with slower clonal expansion. Those carrying two copies of this protective allele had up to 80% reduced odds of having driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 by CRISPR editing led to aberrant expression of TCL1A and expansion of HSCs in vitro. These effects were abrogated in HSCs from donors carrying the protective TCL1A allele. Our results indicate that the fitness advantage of multiple common driver genes in clonal hematopoiesis is mediated through TCL1A activation. PACER is an approach that can be widely applied to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues.

genetics↗

bioRxiv2021

A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare variants (RVs) associations with complex human traits. Variant set analysis is a powerful approach to study RV association, and a key component of it is constructing RV sets for analysis. However, existing methods have limited ability to define analysis units in the noncoding genome. Furthermore, there is a lack of robust pipelines for comprehensive and scalable noncoding RV association analysis. Here we propose a computationally-efficient noncoding RV association-detection framework that uses STAAR (variant-set test for association using annotation information) to group noncoding variants in gene-centric analysis based on functional categories. We also propose SCANG (scan the genome)-STAAR, which uses dynamic window sizes and incorporates multiple functional annotations, in a non-gene-centric analysis. We furthermore develop STAARpipeline to perform flexible noncoding RV association analysis, including gene-centric analysis as well as fixed-window-based and dynamic-window-based non-gene-centric analysis. We apply STAARpipeline to identify noncoding RV sets associated with four quantitative lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several noncoding RV associations in an additional 9,123 TOPMed samples.

genetics↗

bioRxiv2021

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

genetics↗

bioRxiv2020

Rare coding variants in 35 genes associate with circulating lipid levels: a multi-ancestry analysis of 170,000 exomes

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency<1%) predicted damaging coding variation using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

genetics↗

bioRxiv2020

Presence and Transmission of Mitochondrial Heteroplasmic Mutations in Human Populations of European and African Ancestry

We investigated the concordance of mitochondrial DNA heteroplasmic mutations (heteroplasmies) in different types of maternal pairs (n=6,745 pairs) of European (EA, n=4,718 pairs) and African (AA, n=2,027 pairs) Americans with whole genome sequences (WGSs). The average concordance rate of heteroplasmies was highest between mother-offspring pairs, followed by sibling-sibling pairs and more distantly related maternal pairs in both EA and AA participants. The allele fractions of concordant heteroplasmies exhibited high correlation (R2=0.8) between paired individuals. Compared to concordant heteroplasmies, discordant ones were more likely to locate in coding regions, be nonsynonymous or nonsynonymous-deleterious (p<0.001). The average number of heteroplasmies per individual (i.e. heteroplasmic burden) was at a similar level until older age (70-80 years old) and increased significantly thereafter (p<0.01). The burden of deleterious heteroplasmies (combined annotation-dependent depletion score[≥]15), however, was significantly correlated with advancing age (20-44, 45-64, [≥]65 years, p-trend=0.01). A genome-wide association analysis of the heteroplasmic burden identified many significant (P<5e-8) common variants (minor allele frequency>0.05) at 11p11.12. Many of the top SNPs act as strong long-range cis regulators of protein tyrosine phosphatase receptor type J. This study provides further evidence that mtDNA heteroplasmies may be inherited or somatic. Somatic heteroplasmic variants increase with advancing age and are more likely to have an adverse impact on mitochondrial function. Further studies are warranted for functional characterization of the deleterious heteroplasmies occurring with advancing age and the association of the 11p11.12 region of the nuclear genome with mtDNA heteroplasmy.

genetics↗

bioRxiv2020

Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries

Traditional Hardy-Weinberg equilibrium (HWE) tests (the {chi}2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

bioinformatics↗

bioRxiv2020

A system for phenotype harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institutes Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 TOPMed studies per phenotype. We discuss the challenges faced in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

genetics↗

bioRxiv2020

Multi-ancestry genome-wide gene-sleep interactions identify novel loci for blood pressure

Long and short sleep duration are associated with elevated blood pressure (BP), possibly through effects on molecular pathways that influence neuroendocrine and vascular systems. To gain new insights into the genetic basis of sleep-related BP variation, we performed genome-wide gene by short or long sleep duration interaction analyses on four BP traits (systolic BP, diastolic BP, mean arterial pressure, and pulse pressure) across five ancestry groups using 1 degree of freedom (1df) interaction and 2df joint tests. Primary multi-ancestry analyses in 62,969 individuals in stage 1 identified 3 novel loci that were replicated in an additional 59,296 individuals in stage 2, including rs7955964 (FIGNL2/ANKRD33) showing significant 1df interactions with long sleep duration and rs73493041 (SNORA26/C9orf170) and rs10406644 (KCTD15/LSM14A) showing significant 1df interactions with short sleep duration (Pint < 5x10-8). Secondary ancestry-specific two-stage analyses and combined stage 1 and 2 analyses additionally identified 23 novel loci that need external replication, including 3 and 5 loci showing significant 1df interactions with long and short sleep duration, respectively (Pint < 5x10-8). Multiple genes mapped to our 26 novel loci have known functions in sleep-wake regulation, nervous and cardiometabolic systems. We also identified new gene by long sleep interactions near five known BP loci ([≤]1Mb) including NME7, FAM208A, MKLN1, CEP164, and RGL3/ELAVL3 (Pint < 5x10-8). This study indicates that sleep and primary mechanisms regulating BP may interact to elevate BP level, suggesting novel insights into sleep-related BP regulation.

genomics↗

bioRxiv2019

Leveraging pleiotropy to discover and interpret GWAS results for sleep-associated traits

Genetic association studies of many heritable traits resulting from physiological testing often have modest sample sizes due to the cost and burden of the required phenotyping. This reduces statistical power and limits discovery of multiple genetic associations. We present a strategy to leverage pleiotropy between traits to both discover new loci and to provide mechanistic hypotheses of the underlying pathophysiology. Specifically, we combine a colocalization test with a locus-level test of pleiotropy. In simulations, we show that this approach is highly selective for identifying true pleiotropy driven by the same causative variant, thereby improves the chance to replicate the associations in underpowered validation cohorts and leads to higher interpretability. Here, as an exemplar, we use Obstructive Sleep Apnea (OSA), a common disorder diagnosed using overnight multi-channel physiological testing. We leverage pleiotropy with relevant cellular and cardio-metabolic phenotypes and gene expression traits to map new risk loci in an underpowered OSA GWAS. We identify several pleiotropic loci harboring suggestive associations to OSA and genome-wide significant associations to other traits, and show that their OSA association replicates in independent cohorts of diverse ancestries. By investigating pleiotropic loci, our strategy allows proposing new hypotheses about OSA pathobiology across many physiological layers. For example, we identify and replicate the pleiotropy across the plateletcrit, OSA and an eQTL of DNA primase subunit 1 (PRIM1) in immune cells. We find suggestive links between OSA, a measure of lung function (FEV1/FVC), and an eQTL of matrix metallopeptidase 15 (MMP15) in lung tissue. We also link a previously known genome-wide significant peak for OSA in the hexokinase 1 (HK1) locus to hematocrit and other red blood cell related traits. Thus, the analysis of pleiotropic associations has the potential to assemble diverse phenotypes into a chain of mechanistic hypotheses that provide insight into the pathogenesis of complex human diseases. Author SummaryLarge genetic studies with hundreds of thousands of patients have been successful at finding genetic variants that associate with disease traits in humans. However, smaller-scale studies can often have inadequate power to discover new genetic associations. Here, we use a small genetic study of Obstructive Sleep Apnea (OSA), to introduce a strategy that both helps find genetic associations and proposes biological hypotheses for the mechanisms behind those associations. To achieve this, we use large genetic studies carried out on traits that are related to OSA, and look for genetic variants that affect both OSA in our small study and the trait in question in the large study. By linking two or more traits at select loci, we were able to, among other results, find a locus that affects the expression of a gene in immune cells (DNA primase subunit 1), a marker of thrombotic and inflammatory processes (plateletcrit) and OSA. This results in a novel genetic association to OSA and a corresponding biological hypothesis behind its effect on OSA.

genetics↗

bioRxiv2019

Whole-Genome Association Analyses of Sleep-disordered Breathing Phenotypes in the NHLBI TOPMed Program

Sleep-disordered breathing (SDB) is a common disorder associated with significant morbidity. Through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program we report the first whole-genome sequence analysis of SDB. We identified 4 rare gene-based associations with SDB traits in 7,988 individuals of diverse ancestry and 4 replicated common variant associations with inclusion of additional samples (n=13,257). We identified a multi-ethnic set-based rare-variant association (p = 3.48 x 10-8) on chromosome X with ARMCX3. Transcription factor binding site enrichment identified associations with genes implicated with respiratory and craniofacial traits. Results highlighted associations in genes that modulate lung development, inflammation, respiratory rhythmogenesis and HIF1A-mediated hypoxic response.

genetics↗

bioRxiv2019

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency <1% and 46% are singletons. These rare variants provide insights into mutational processes and recent human evolutionary history. The nearly complete catalog of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and non-coding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and extends the reach of nearly all genome-wide association studies to include variants down to ~0.01% in frequency.

genomics↗

bioRxiv2019

De novo mutations across 1,465 diverse genomes reveal novel mutational insights and reductions in the Amish founder population.

de novo Mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) program, we directly estimate and analyze DNM counts, rates, and spectra from 1,465 trios across an array of diverse human populations. Using the resulting call set of 86,865 single nucleotide DNMs, we find a significant positive correlation between local recombination rate and local DNM rate, which together can explain up to 35.5% of the genome-wide variation in population level rare genetic variation from 41K unrelated TOPMed samples. While genome-wide heterozygosity does correlate weakly with DNM count, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, interestingly, we do find significantly fewer DNMs in Amish individuals compared with other Europeans, even after accounting for parental age and sequencing center. Specifically, we find significant reductions in the number of T[->]C mutations in the Amish, which seems to underpin their overall reduction in DNMs. Finally, we calculate near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by non-additive genetic effects and/or the environment, and that a less mutagenic environment may be responsible for the reduced DNM rate in the Amish. SignificanceHere we provide one of the largest and most diverse human de novo mutation (DNM) call sets to date, and use it to quantify the genome-wide relationship between local mutation rate and population-level rare genetic variation. While we demonstrate that the human single nucleotide mutation rate is similar across numerous human ancestries and populations, we also discover a reduced mutation rate in the Amish founder population, which shows that mutation rates can shift rapidly. Finally, we find that variation in mutation rates is not heritable, which suggests that the environment may influence mutation rates more significantly than previously realized.

genetics↗

bioRxiv2018

Genome-wide association analysis of excessive daytime sleepiness identifies 42 loci that suggest phenotypic subgroups

Excessive daytime sleepiness (EDS) affects 10-20% of the population and is associated with substantial functional deficits. We identified 42 loci for self-reported EDS in GWAS of 452,071 individuals from the UK Biobank, with enrichment for genes expressed in brain tissues and in neuronal transmission pathways. We confirmed the aggregate effect of a genetic risk score of 42 SNPs on EDS in independent Scandinavian cohorts and on other sleep disorders (restless leg syndrome, insomnia) and sleep traits (duration, chronotype, accelerometer-derived sleep efficiency and daytime naps or inactivity). Strong genetic correlations were also seen with obesity, coronary heart disease, psychiatric diseases, cognitive traits and reproductive ageing. EDS variants clustered into two predominant composite phenotypes - sleep propensity and sleep fragmentation - with the former showing stronger evidence for enriched expression in central nervous system tissues, suggesting two unique mechanistic pathways. Mendelian randomization analysis indicated that higher BMI is causally associated with EDS risk, but EDS does not appear to causally influence BMI.

genomics↗

bioRxiv2018

Epigenome-wide association analysis of daytime sleepiness in the Multi-Ethnic Study of Atherosclerosis reveals African-American specific associations

Study ObjectivesExcessive daytime sleepiness (EDS) is a consequence of inadequate sleep, or of a primary disorder of sleep-wake control. Population variability in prevalence of EDS and susceptibility to EDS are likely due to genetic and biological factors as well as social and environmental influences. Epigenetic modifications (such as DNA methylation-DNAm) are potential influences on a range of health outcomes. Here, we explored the association between DNAm and daytime sleepiness quantified by the Epworth Sleepiness Scale (ESS).\n\nMethodsWe performed multi-ethnic and ethnic-specific epigenome-wide association studies for DNAm and ESS in 619 individuals from the Multi-Ethnic Study of Atherosclerosis. Replication was assessed in the Cardiovascular Health Study (CHS). Genetic variants in genes proximal to ESS-associated DNAm were analyzed to identify methylation quantitative trait loci and followed with replication of genotype-sleepiness associations in the UK Biobank.\n\nResults61 methylation sites were associated with ESS (FDR [≤] 0.1) in African Americans only, including an association in KCTD5, a gene strongly implicated in sleep. One association (cg26130090) replicated in CHS African Americans (p-value 0.0004). We identified a sleepiness-associated methylation site in the gene RAI1, a gene associated with sleep and circadian phenotypes. In a follow-up analysis, a genetic variant within RAI1 associated with both DNAm and sleepiness score. The variants association with sleepiness was replicated in the UK Biobank.\n\nConclusionsOur analysis identified methylation sites in multiple genes that may be implicated in EDS. These sleepiness-methylation associations were specific to African Americans. Future work is needed to identify mechanisms driving ancestry-specific methylation effects.\n\nStatement of SignificanceExcessive daytime sleepiness is associated with negative health outcomes such as reduction in quality of life, increased workplace accidents, and cardiovascular mortality. There are race/ethnic disparities in excessive daytime sleepiness, however, the environmental and biological mechanisms for these differences are not yet understood. We performed an association analysis of DNA methylation, measured in monocytes, and daytime sleepiness within a racially diverse study population. We detected numerous DNA methylation markers associated with daytime sleepiness in African Americans, but not in European and Hispanic Americans. Future work is required to elucidate the pathways between DNA methylation, sleepiness, and related behavioral/environmental exposures.

genomics↗