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Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Nicotine from cigarette smoke is a biologically active molecule that has pleiotropic effects in the airway, which could play a role in smoking-induced lung disease. However, whether nicotine and its metabolites reach sustained, physiologically relevant concentrations on airway surfaces of smokers is not well defined. To address these issues, concentrations of nicotine, cotinine, and hydroxycotinine were measured by mass spectrometry (MS) in supernatants of induced sputum obtained from participants in the subpopulations and intermediate outcome measures in COPD study (SPIROMICS), an ongoing observational study that included never smokers, former smokers, and current smokers with and without chronic obstructive pulmonary disease (COPD). A total of 980 sputum supernatants were analyzed from 77 healthy never smokers, 494 former smokers (233 with COPD), and 396 active smokers (151 with COPD). Sputum nicotine, cotinine, and hydroxycotinine concentrations corresponded to self-reported smoking status and were strongly correlated to urine measures. A cutoff of ∼8–10 ng/mL of sputum cotinine distinguished never smokers from active smokers. Accounting for sample dilution during processing, active smokers had airway nicotine concentrations in the 70–850 ng/mL (∼0.5–5 µM) range, and concentrations remained elevated even in current smokers who had not smoked within 24 h. This study demonstrates that airway nicotine and its metabolites are readily measured in sputum supernatants and can serve as biological markers of smoke exposure. In current smokers, nicotine is present at physiologically relevant concentrations for prolonged periods, supporting a contribution to cigarette-induced airway disease.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R

Most gene expression and alternative splicing quantitative trait loci (eQTL/sQTL) studies have been biased toward European ancestry individuals. Here, we performed eQTL and sQTL analysis using TOPMed whole genome sequencing-derived genotype data and RNA sequencing data from stored peripheral blood mononuclear cells in 1,012 African American participants from the Jackson Heart Study (JHS). At a false discovery rate (FDR) of 5%, we identified 4,798,604 significant eQTL-gene pairs, covering 16,538 unique genes; and 5,921,368 sQTL-gene-cluster pairs, covering 9,605 unique genes. About 31% of detected eQTL and sQTL variants with a minor allele frequency (MAF) > 1% in JHS were rare (MAF < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. We also generated 17,630 eQTL credible sets and 24,525 sQTL credible sets for genes (gene-clusters) with lead QTL p < 5e-8. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.

Background and Purpose: Stroke is the leading cause of death and long-term disability worldwide. Previous genome-wide association studies identified 51 loci associated with stroke (mostly ischemic) and its subtypes among predominantly European populations. Using whole-genome sequencing in ancestrally diverse populations from the Trans-Omics for Precision Medicine (TOPMed) Program, we aimed to identify novel variants, especially low-frequency or ancestry-specific variants, associated with all stroke, ischemic stroke and its subtypes (large artery, cardioembolic, and small vessel), and hemorrhagic stroke and its subtypes (intracerebral and subarachnoid). Methods: Whole-genome sequencing data were available for 6833 stroke cases and 27 116 controls, including 22 315 European, 7877 Black, 2616 Hispanic/Latino, 850 Asian, 54 Native American, and 237 other ancestry participants. In TOPMed, we performed single variant association analysis examining 40 million common variants and aggregated association analysis focusing on rare variants. We also combined TOPMed European populations with over 28 000 additional European participants from the UK BioBank genome-wide array data through meta-analysis. Results: In the single variant association analysis in TOPMed, we identified one novel locus 13q33 for large artery at whole-genome-wide significance ( P −9 ) and 4 novel loci at genome-wide significance ( P −8 ), all of which need confirmation in independent studies. Lead variants in all 5 loci are low-frequency but are more common in non-European populations. An aggregation of synonymous rare variants within the gene C6orf26 demonstrated suggestive evidence of association for hemorrhagic stroke ( P −6 ). By meta-analyzing European ancestry samples in TOPMed and UK BioBank, we replicated several previously reported stroke loci including PITX2 , HDAC9 , ZFHX3 , and LRCH1 . Conclusions: We represent the first association analysis for stroke and its subtypes using whole-genome sequencing data from ancestrally diverse populations. While our findings suggest the potential benefits of combining whole-genome sequencing data with populations of diverse genetic backgrounds to identify possible low-frequency or ancestry-specific variants, they also highlight the need to increase genome coverage and sample sizes.

Background and Purpose: Clonal hematopoiesis of indeterminate potential (CHIP) is a novel age-related risk factor for cardiovascular disease–related morbidity and mortality. The association of CHIP with risk of incident ischemic stroke was reported previously in an exploratory analysis including a small number of incident stroke cases without replication and lack of stroke subphenotyping. The purpose of this study was to discover whether CHIP is a risk factor for ischemic or hemorrhagic stroke. Methods: We utilized plasma genome sequence data of blood DNA to identify CHIP in 78 752 individuals from 8 prospective cohorts and biobanks. We then assessed the association of CHIP and commonly mutated individual CHIP driver genes ( DNMT3A , TET2 , and ASXL1 ) with any stroke, ischemic stroke, and hemorrhagic stroke. Results: CHIP was associated with an increased risk of total stroke (hazard ratio, 1.14 [95% CI, 1.03–1.27]; P =0.01) after adjustment for age, sex, and race. We observed associations with CHIP with risk of hemorrhagic stroke (hazard ratio, 1.24 [95% CI, 1.01–1.51]; P =0.04) and with small vessel ischemic stroke subtypes. In gene-specific association results, TET2 showed the strongest association with total stroke and ischemic stroke, whereas DMNT3A and TET2 were each associated with increased risk of hemorrhagic stroke. Conclusions: CHIP is associated with an increased risk of stroke, particularly with hemorrhagic and small vessel ischemic stroke. Future studies clarifying the relationship between CHIP and subtypes of stroke are needed.

Introduction: Analyzing plasma proteins is a novel approach to uncover molecular mechanisms by which psychosocial stress may lead to chronic illness, including cardiometabolic disease. Proteomic analyses of cardiometabolic disease have been reported; however, proteomic studies of stress and depressive symptoms are limited. Hypothesis: We hypothesized that plasma protein concentrations are both positively and negatively associated with greater depressive symptoms and perceived global stress, respectively, in African American adults from the Jackson Heart Study (JHS). Methods: Participants from exam 1 (2000-2004) with SomaScan 1.3k platform proteomics data were utilized (n=2143, mean age=55.3). Depressive symptoms and global stress scores were measured via the 20-item Centers for Epidemiological Studies (CES-D) Scale and an 8-item global perceived stress scale adapted for the JHS, respectively. Multivariable linear models were used to test the association between depressive symptoms and global stress scores and proteins, controlling for age, sex, and proteomics batch. Bonferroni correction was used to adjust the p-value threshold for multiple testing (3.78e-5, for 1,322 tested proteins). Results: Six (6) proteins were found to be significantly associated with either depressive symptoms or perceived global stress. Angiopoietin-2 (β=0.014, SE=0.003, p =1.33e-5), growth/differentiation factor 15 or MIC-1 (β=0.014, SE=0.003, p=7.08e-8), and kynureninase (β=0.014, SE=0.003, p =1.55e-5) were positively associated with depressive symptoms. Contactin-5 (β=-0.017, SE=0.003, p =1.40e-7) and neural cell adhesion molecule 1 (120 kDa isoform) (β=-0.016, SE=0.003, p =6.74e-7) were negatively associated with depressive symptoms. Leukotriene A-4 hydrolase or LKHA4 (β=0.024, SE=0.005, p =2.72e-6) was positively associated with perceived stress. We note that several of these proteins which are associated with higher levels of stress or depression symptoms have also been associated with cardiovascular disease and related phenotypes; for example, LKHA4 was the lead association with left ventricular mass in previous JHS analyses. Conclusions: JHS proteomic profiles revealed differences in protein concentrations by psychosocial measures. We will next explore the extent to which identified proteins may mediate relationships between stress and depressive symptoms and cardiovascular endpoints. Future investigations may identify potentially targetable proteomic mechanisms by which psychosocial stressors manifest into disease.

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in elevated risks for coronary heart disease (CHD) and death, but its association with incident type 2 diabetes (T2D) is unknown. Hypothesis: We hypothesized that CHIP is associated with elevated risk of incident T2D. Methods: CHIP was derived from whole genome sequencing of blood DNA in NHLBI Trans-omics for Precision Medicine (TOPMed) cohorts. We analyzed 17,637 participants without prior T2D, cardiovascular disease, or cancer at blood draw, with prospective follow-up for incident T2D. We evaluated baseline prevalence of CHIP vs. no CHIP with incident T2D risk using Cox regression. We also investigated CHIP variants previously related to CHD: DNMT3A , TET2 , ASXL1 , JAK2 , and TP53 . We estimated multivariable-adjusted hazard ratios and 95% confidence intervals (HR [CI]) adjusted for age, sex, body mass index, smoking, alcohol, and education. We combined cohort estimates via fixed effects meta-analysis. Results: On average, participants were age 63.4 years (SD=11.5) and 76% female. Prevalence of CHIP was 6.0% (1,055) at baseline. There were 2,467 incident T2D cases over mean=9.8 years follow-up. Compared to those without a mutation, having CHIP was associated with a 23% higher T2D risk, both overall (combined HR=1.23; 95% CI=1.04, 1.45), and among those with CHD-related CHIP mutations (87% of total CHIP): HR=1.23 (1.03, 1.46). Although those with CHIP mutations of TET2 (HR=1.48; 1.05, 2.08) and ASXL1 (HR=1.76; 1.03, 2.99) had larger elevations in T2D risk, and DNMT3A was suggestive of increased T2D risk (HR=1.15; 0.93, 1.43), statistical power was limited for JAK2 and TP53 mutation analyses. Conclusions: CHIP was associated with higher incidence of T2D. CHIP mutations located on loci previously implicated in aging and CHD were also related to T2D, suggesting shared pathology of atherosclerosis and T2D.

Introduction: The aromatic amino acids tyrosine and phenylalanine may increase type 2 diabetes risk, but few studies have examined whether these critical molecular precursors exert additional phenotypic effects and whether these effects are independent of insulin resistance. We evaluated associations between two aromatic amino acids - tyrosine and phenylalanine - with a broad range of phenotypic categories in the UK Biobank using polygenic risk score (PRS) instrumental variables that are robust to confounding and reverse causation and tested for mediation by glycated hemoglobin (HbA1c) as a measure of insulin resistance. Hypothesis: We hypothesized that tyrosine and phenylalanine would show broad phenotypic effects and that many of these effects would be mediated by HbA1c. Methods: We constructed PRS (Crosspred) using all nominally significant and common (minor allele frequency >5%) variants from genome-wide association studies (SAIGE) of unrelated European ancestry participants in the UK Biobank. We evaluated associations with 273 curated phenotypes spanning 20 categories, including endocrine, circulatory, and cancer related traits. We corrected for multiple comparisons using false discovery rate Results: A total of 108,554 participants had tyrosine or phenylalanine measured at baseline (mean age=57 years; 54% (58,880/108,554) female). PRS were predictive of tyrosine (R 2 =0.28) and phenylalanine (R 2 =0.26). Tyrosine and phenylalanine PRS showed significant associations with 121/273 (44%) and 124/273 (45%) of phenotypes, respectively, including chronic kidney disease, serum testosterone, lymphocyte count, and myocardial infarction (MI; n=3,349 cases, mean years of follow-up=11). For incident MI, every one standard deviation increase in the tyrosine PRS increased the odds of MI by 4.4% (total effect odds ratio (OR) = 1.044 (95% confidence interval (CI): 1.002, 1.089). The direct effect of the tyrosine PRS on incident MI was slightly decreased (OR= 1.040 (95% CI: 0.997, 1.084) when extending the statistical model to examine mediation by HbA1c, with an estimated indirect effect of 1.004 (95% CI: 1.003, 1.006) and an estimated percent mediated by HbA1c of 9.95% (95% CI: -0.04%, 19.90%). Findings were consistent for phenylalanine, where the percent of the total effect mediated by HbA1c was 8.94% (95% CI: -1.93, 19.80%). Conclusions: The effect of tyrosine and phenylalanine on incident MI may primarily operate through pathways other than glucose dysregulation.

Physical fitness is a well-known correlate of health and the aging process and DNA methylation (DNAm) data can capture aging via epigenetic clocks. However, current epigenetic clocks did not yet use measures of mobility, strength, lung, or endurance fitness in their construction. We develop blood-based DNAm biomarkers for fitness parameters gait speed (walking speed), maximum handgrip strength, forced expiratory volume in one second (FEV1), and maximal oxygen uptake (VO2max) which have modest correlation with fitness parameters in five large-scale validation datasets (average r between 0.16-0.48). We then use these DNAm fitness parameter biomarkers with DNAmGrimAge, a DNAm mortality risk estimate, to construct DNAmFitAge, a new biological age indicator that incorporates physical fitness. DNAmFitAge is associated with low-intermediate physical activity levels across validation datasets (p = 6.4E-13), and younger/fitter DNAmFitAge corresponds to stronger DNAm fitness parameters in both males and females. DNAmFitAge is lower (p = 0.046) and DNAmVO2max is higher (p = 0.023) in male body builders compared to controls. Physically fit people have a younger DNAmFitAge and experience better age-related outcomes: lower mortality risk (p = 7.2E-51), coronary heart disease risk (p = 2.6E-8), and increased disease-free status (p = 1.1E-7). These new DNAm biomarkers provide researchers a new method to incorporate physical fitness into epigenetic clocks.

Aims/hypothesis: Type 2 diabetes is a growing global public health challenge. Investigating quantitative traits, including fasting glucose, fasting insulin and HbA1c, that serve as early markers of type 2 diabetes progression may lead to a deeper understanding of the genetic aetiology of type 2 diabetes development. Previous genome-wide association studies (GWAS) have identified over 500 loci associated with type 2 diabetes, glycaemic traits and insulin-related traits. However, most of these findings were based only on populations of European ancestry. To address this research gap, we examined the genetic basis of fasting glucose, fasting insulin and HbA1c in participants of the diverse Population Architecture using Genomics and Epidemiology (PAGE) Study. Methods: We conducted a GWAS of fasting glucose (n = 52,267), fasting insulin (n = 48,395) and HbA1c (n = 23,357) in participants without diabetes from the diverse PAGE Study (23% self-reported African American, 46% Hispanic/Latino, 40% European, 4% Asian, 3% Native Hawaiian, 0.8% Native American), performing transethnic and population-specific GWAS meta-analyses, followed by fine-mapping to identify and characterise novel loci and independent secondary signals in known loci. Results: Four novel associations were identified (p < 5 × 10−9), including three loci associated with fasting insulin, and a novel, low-frequency African American-specific locus associated with fasting glucose. Additionally, seven secondary signals were identified, including novel independent secondary signals for fasting glucose at the known GCK locus and for fasting insulin at the known PPP1R3B locus in transethnic meta-analysis. Conclusions/interpretation: Our findings provide new insights into the genetic architecture of glycaemic traits and highlight the continued importance of conducting genetic studies in diverse populations. Data availability: Full summary statistics from each of the population-specific and transethnic results are available at NHGRI-EBI GWAS catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics).

Many common and rare variants associated with hematologic traits have been discovered through imputation on large-scale reference panels. However, the majority of genome-wide association studies (GWASs) have been conducted in Europeans, and determining causal variants has proved challenging. We performed a GWAS of total leukocyte, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts generated from 109,563,748 variants in the autosomes and the X chromosome in the Trans-Omics for Precision Medicine (TOPMed) program, which included data from 61,802 individuals of diverse ancestry. We discovered and replicated 7 leukocyte trait associations, including (1) the association between a chromosome X, pseudo-autosomal region (PAR), noncoding variant located between cytokine receptor genes (CSF2RA and CLRF2) and lower eosinophil count; and (2) associations between single variants found predominantly among African Americans at the S1PR3 (9q22.1) and HBB (11p15.4) loci and monocyte and lymphocyte counts, respectively. We further provide evidence indicating that the newly discovered eosinophil-lowering chromosome X PAR variant might be associated with reduced susceptibility to common allergic diseases such as atopic dermatitis and asthma. Additionally, we found a burden of very rare FLT3 (13q12.2) variants associated with monocyte counts. Together, these results emphasize the utility of whole-genome sequencing in diverse samples in identifying associations missed by European-ancestry-driven GWASs.

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI’s Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Mosaic mutations in blood are common with increasing age and are prognostic markers for cancer, cardiovascular dysfunction and other diseases. This group of acquired mutations include megabase-scale mosaic chromosomal alterations (mCAs). These large mutations have mainly been surveyed using SNP array data from individuals of European (EA) or Japanese genetic ancestry. To gain a better understanding of mCA rates and associated risk factors in genetically diverse populations, we surveyed whole genome sequencing data from 67,390 individuals, including 20,132 individuals of African ancestry (AA), and 7,608 of Hispanic ancestry (HA) with deep (30X) whole genome sequencing data from the NHLBI Trans Omics for Precision Medicine (TOPMed) program. We adapted an existing mCA calling algorithm for application to WGS data, and observed higher sensitivity with WGS data, compared with array-based data, in uncovering mCAs at low mutant cell fractions. As in previous reports, we observed a strong association with age and a non-uniform distribution of mCAs across the genome. The presence of autosomal (but not chromosome X) mCAs was associated with an increased risk of both lymphoid and myeloid malignancies. After adjusting for age, we found that individuals of European ancestry have the highest rates of autosomal mCAs, mirroring the higher rate of leukemia in this group. Our analysis also uncovered higher rates of chromosome X mCAs in AA and HA compared to EA, again after adjusting for age. Germline variants inATMandMPLshowed strong associations with mCAs incis, including ancestry specific variants. And rare variant gene-burden analysis confirmed the association of putatively protein altering variants inATMandMPLwith mCAs incis. Individual rare variants inDCPS, ADM17, PPP1R16B, andTET2were all associated with autosomal mCAs and rare variants inOR4C16were associated with chromosome X mCAs in females. There was significant enrichment of co-occurrence of CHIP mutations and mCAs both altering cancer associated genesTET2, DNMT3A, JAK2, CUX1, andTP53. Overall, our study demonstrates that rates of mCAs differ across populations and that rare inherited germline variants are strongly associated with mCAs across genetically diverse populations. These results strongly motivate further studies of mCAs in under-represented populations to better understand the causes and consequences of this class of somatic variation.

Background Monocytes/macrophages participate in cardiovascular disease. CD163 (cluster of differentiation 163) is a monocyte/macrophage receptor, and the shed sCD163 (soluble CD163) reflects monocyte/macrophage activation. We examined the association of sCD163 with incident cardiovascular disease events and performed a genome‐wide association study to identify sCD163‐associated variants. Methods and Results We measured plasma sCD163 in 5214 adults (aged ≥65 years, 58.7% women, 16.2% Black) of the CHS (Cardiovascular Health Study). We used Cox regression models (associations of sCD163 with incident events and mortality); median follow‐up was 26 years. Genome‐wide association study analyses were stratified on race. Adjusted for age, sex, and race and ethnicity, sCD163 levels were associated with all‐cause mortality (hazard ratio [HR], 1.08 [95% CI, 1.04–1.12] per SD increase), cardiovascular disease mortality (HR, 1.15 [95% CI, 1.09–1.21]), incident coronary heart disease (HR, 1.10 [95% CI, 1.04–1.16]), and incident heart failure (HR, 1.18 [95% CI, 1.12–1.25]). When further adjusted (eg, cardiovascular disease risk factors), only incident coronary heart disease lost significance. In European American individuals, genome‐wide association studies identified 38 variants on chromosome 2 near MGAT5 (top result rs62165726, P =3.3×10 −18 ),19 variants near chromosome 17 gene ASGR1 (rs55714927, P =1.5×10 −14 ), and 18 variants near chromosome 11 gene ST3GAL4 . These regions replicated in the European ancestry ADDITION‐PRO cohort, a longitudinal cohort study nested in the Danish arm of the Anglo‐Danish‐Dutch study of Intensive Treatment Intensive Treatment In peOple with screeNdetcted Diabetes in Primary Care. In Black individuals, we identified 9 variants on chromosome 6 (rs3129781 P =7.1×10 −9 ) in the HLA region, and 3 variants (rs115391969 P =4.3×10 −8 ) near the chromosome 16 gene MYLK3 . Conclusions Monocyte function, as measured by sCD163, may be predictive of overall and cardiovascular‐specific mortality and incident heart failure.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods have focused only on individuals with one primary continental ancestry, thus poorly accommodating recently-admixed individuals. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals by explicitly modeling ancestry-specific effects and jointly estimating ancestry-shared effects. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses.

HypothesisThe prevalence of type 2 diabetes is higher in Latino populations compared with other major ancestry groups. Not only has the Latino population been systematically underrepresented in large-scale genetic analyses, but previous studies relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation reference panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The NHLBI Trans-Omics for Precision Medicine (TOPMed) reference panel represents a unique opportunity to analyze rare genetic variations in the Latino population.MethodsWe evaluate the TOPMed imputation performance using genotyping array and whole-exome sequence data in 6 Latino cohorts. To evaluate the ability of TOPMed imputation of increasing the identified loci, we performed a Latino type 2 diabetes GWAS meta-analysis in 8,150 type 2 diabetes cases and 10,735 controls and replicated the results in 6 additional cohorts including whole-genome sequence data from the All of Us cohort.ResultsWe show that, compared to imputation with 1000G, the TOPMed panel improves the identification of rare and low-frequency variants. We identified 26 distinct signals including a novel genome-wide significant variant (minor allele frequency 1.6%, OR=2.0, P=3.4×10−9) near ORC5. A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improves the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.ConclusionsOur results demonstrate the utility of TOPMed imputation for identifying low-frequency variation in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.

BackgroundHeterogeneity in the long-term metabolic response to dietary macronutrient composition can be partially explained by genetic factors. However, few studies have demonstrated reproducible gene-diet interactions (GDIs), likely due in part to measurement error in dietary intake estimation as well as insufficient capture of rare genetic variation. Discovery analyses in ancestry-diverse cohorts that include rare genetic variants from whole-genome sequencing (WGS) could help identify genetic variants modifying the effects of dietary macronutrient composition on glycemic phenotypes.ObjectiveWe aimed to identify macronutrient GDIs across the genetic frequency spectrum associated with continuous glycemic traits in genetically and culturally diverse cohorts.MethodsWe analyzed N=33,187 diabetes-free participants from 10 cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program with WGS, self-reported diet, and glycemic traits (fasting glucose [FG], insulin [FI], and hemoglobin A1c [HbA1c]). We fit multivariable-adjusted linear mixed models for the main effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and for its interactions with genetic variants genome-wide. Tests were performed for both common variants and gene-based rare variant sets in each cohort followed by a combined cohort meta-analysis.ResultsIn main effect models, participants consuming more calories from carbohydrate at the expense of fat had modestly lower glycemic trait values (β per 250 kcal substitution for FG: −0.030 mmol/L, p=2.7×10−6; lnFI: −0.008 log(pmol/L), p=0.17; HbA1c: −0.013 %, p=0.025). In GDI analyses, a common African ancestry-enriched variant (rs79762542; 78 kb upstream of the FRAS1 gene) reached study-wide significance (p = 1.14×10−8) indicating a higher HbA1c with greater proportion of calories from carbohydrate vs. fat among minor allele carriers only. This interaction was replicated in the UK Biobank cohort. Simulations revealed that there is (1) a substantial impact of measurement error on statistical power for GDI discovery at these sample sizes, especially for rare genetic variants, and (2) over 150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error.ConclusionsOur analysis identified a potential genetic interaction modifying the dietary macronutrient-HbA1c association while highlighting the importance of precise exposure measurement and significantly increased sample size to identify additional similar effects.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome arrays, resulting in over 100 associated variants. We extended this work with high-coverage whole genome sequencing analyses from fifteen cohorts in NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program. Over 23,000 non-diabetic individuals from five race-ethnicities/populations (African, Asian, European, Hispanic and Samoan) were included. Eight variants were significantly associated with FG or FI across previously identified regions MTNR1B, G6PC2, GCK, GCKR and FOXA2. We additionally characterize suggestive associations with FG or FI near previously identified SLC30A8, TCF7L2, and ADCY5 regions as well as APOB, PTPRT, and ROBO1. Functional annotation resources including the Diabetes Epigenome Atlas were compiled for each signal (chromatin states, annotation principal components, and others) to elucidate variant-to-function hypotheses. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions creating a foundation for future sequencing-based investigations of glycemic traits.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease (CKD) and diabetes. Our two-stage whole-exome sequencing study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort (CRIC) and Atherosclerosis Risk in Communities (ARIC) studies (stage-1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine (TOPMed) participants (stage-2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex, and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test (SKAT-O) implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds (95% confidence interval: 33.6, 1105) of DKD compared with non-carriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% confidence interval: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

Background: Multi-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes. Objectives: To discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events. Methods: Summary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10−9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27). Results: Across the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes. Conclusions: The discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Summary Whole-genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci, which have not been reported previously. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, and G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3 bp indel p.Lys2169del (g.88717175_88717177TCT[4]) (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis (MIM: 194380 ), associated with higher mean corpuscular hemoglobin concentration (MCHC). In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

ImportanceGout disparities among Black individuals in the US have recently been explained by socioclinical factors; however, no information is available among Asian individuals living in Western countries, despite their disproportionately worsening metabolic health.ObjectiveTo determine the prevalence of gout and serum urate concentrations according to race and ethnicity and to explore the association of social determinants of health and clinical factors.Design, Setting, and ParticipantsThis is a population-based, cross-sectional analysis. Data from a nationally representative sample of US adults were obtained from the National Health and Nutrition Examination Survey (NHANES) (2011-2018) in which Asian race data were collected (primary). Data from the UK Biobank (2006-2021) were used for replication of the Asian vs White differences. Data analysis was performed from December 2021 to September 2022.Main Outcomes and MeasuresRace-specific gout prevalence and serum urate levels.ResultsA total of 22 621 participants from NHANES (2011-2018) were included in the analysis (mean [SD] age, 49.8 [17.8] years; 10 948 male participants [48.4%]). In 2017 to 2018, gout affected 12.1 million US individuals, with its crude prevalence increasing from 3.6% (95% CI, 2.8%-4.5%) in 2011 to 2012 to 5.1% (95% CI, 4.2%-5.9%) in 2017 to 2018 (P for trend = .03); this trend was no longer significant after age adjustment (P for trend = .06) or excluding Asian individuals (P for trend = .11). During the same period, age- and sex-adjusted prevalence among Asian Americans doubled from 3.3% (95% CI, 2.1%-4.5%) to 6.6% (95% CI, 4.4%-8.8%) (P for trend = .007) to numerically exceed all other racial and ethnic groups in 2017 to 2018, with age- and sex-adjusted odds ratio (ORs) of 1.61 (95% CI, 1.03-2.51) and a socioclinical factor–adjusted multivariable OR of 2.62 (95% CI, 1.59-4.33) for Asian vs White individuals. The latest age- and sex-adjusted gout prevalence among US individuals aged 65 years and older was 10.0% among White individuals and 14.8% among Asian individuals (including 23.6% of Asian men). Serum urate concentrations also increased between 2011 and 2018 among US Asian individuals (P for trend = .009). The Asian vs White disparity was also present in the UK Biobank.Conclusions and RelevanceThe findings of this study suggest that the prevalence of gout among Asian individuals numerically surpassed that for all other racial and ethnic groups in 2017 to 2018. This Asian vs White disparity did not appear to be associated with socioclinical factors.

Malaria and iron deficiency (ID) are common and interrelated public health problems in African children. Observational data suggest that interrupting malaria transmission reduces the prevalence of ID1. To test the hypothesis that malaria might cause ID, we used sickle cell trait (HbAS, rs334 ), a genetic variant that confers specific protection against malaria2, as an instrumental variable in Mendelian randomization analyses. HbAS was associated with a 30% reduction in ID among children living in malaria-endemic countries in Africa (n = 7,453), but not among individuals living in malaria-free areas (n = 3,818). Genetically predicted malaria risk was associated with an odds ratio of 2.65 for ID per unit increase in the log incidence rate of malaria. This suggests that an intervention that halves the risk of malaria episodes would reduce the prevalence of ID in African children by 49%. A genetic link suggests that interventions that halve the risk of malaria episodes could reduce the prevalence of iron deficiency in African children by nearly 50%.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

BackgroundPeople hospitalized with COVID-19 often exhibit hematological alterations, such as lower lymphocyte and platelet counts, which have been reported to associate with disease prognosis. It is unclear whether inter-individual variability in baseline hematological parameters prior to acute infection influences risk of SARS-CoV-2 infection and progression to severe COVID-19.MethodsWe assessed the association of blood cell counts and indices with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we used summary statistics from genome-wide association studies to assess the shared genetic architecture of baseline blood cell counts and indices on COVID-19 outcomes.ResultsWe observed inconsistent associations between measured blood cell indices and both SARS-CoV-2 infection and COVID-19 hospitalization in UK Biobank and VUMC SD. In Mendelian randomization analyses using genetic summary statistics, no putative causal relationships were identified between COVID-19 related outcomes and hematological indices after adjusting for multiple testing. We observed overlapping genetic association signals between hematological parameters and COVID-19 traits. For example, we observed overlap between infection susceptibility-associated variants at PPP1R15A and red blood cell parameters, and between disease severity-associated variants at TYK2 and lymphocyte and platelet phenotypes.ConclusionsWe did not find convincing evidence of a relationship between baseline hematological parameters and susceptibility to SARS-CoV-2 infection or COVID-19 severity, though this relationship should be re-examined as larger and better-powered genetic analyses of SARS-CoV-2 infection and severe COVID-19 become available.

Introduction: Although thiazide diuretics are common therapies for the treatment of hypertension, evidence suggests that they are also associated with elevated blood lipid concentrations after their initiation. Pharmacogenomics studies could help identify potential biological pathways underlying this phenomenon, but few well-powered studies have been conducted. Methods: In participants of European ancestry from the UK Biobank, we conducted a genome-wide association study (GWAS) to examine whether common variants (minor allele frequency [MAF] >1%) modified the effect of thiazide diuretic use on three lipid measures: low density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) concentrations (mmol/L). TG concentrations were natural log transformed, and LDL-C concentrations were adjusted for statin use. Given the conflicting evidence about the impact of loop diuretics on blood lipid concentrations, unexposed participants were defined as those taking neither thiazides nor loop diuretics. GWAS of UK10K plus 1000-Genomes Phase 3 imputed data were performed using SAIGE, adjusting for age, sex, fasting status, body mass index, study center, and the first 10 ancestral principal components. Results: Approximately 7.1% (n=27,971) of participants (n=395,058; mean age=57 years; 54% female [n=214,334]) used thiazides at study baseline. The thiazide-LDL-C GWAS (n=9,680,686 variants) identified one chromosome 19 locus harboring several genome-wide significant variants that modified the effect of thiazide diuretic use on LDL-C. The lead variant rs2199576 (MAF = 0.18) had an effect estimate (standard error) of 0.159 (0.013) among participants exposed to thiazides, and an effect estimate of 0.083 (0.003) among participants unexposed to any diuretic, yielding a significant variant-by-thiazide interaction effect estimate of 0.076 (0.013) ( p = 1.62 x 10 -9 ). These effect estimates are comparable to previously identified main effects of blood lipids loci. The rs2199576 variant resides near the PVRL2 locus, a cholesterol-responsive gene that is located within 100 kb of TOMM40 and the APO cluster, which have both been associated with blood lipid concentrations and Alzheimer’s disease. Conclusion: Our results may provide insights into pathways that influence the adverse effects of thiazide diuretic use and highlight the large sample sizes needed to detect modest pharmacogenetic effects.

Introduction: Despite decades of research linking obesity with cardiovascular disease (CVD) and its risk factors, major research gaps remain, with few studies evaluating the often-described but poorly understood heterogeneity in CVD risk observed within obese populations. One plausible, but largely unexplored source of heterogeneity in obesity-associated CVD risk is the impact of body fat distribution (central versus overall obesity), obesity duration and severity. Longitudinally assessed anthropometric measures and GWAS derived from these exposures may improve understanding of functional properties of loci associated with obesity and downstream influences on CVD. Methods: We respond to these research gaps by using 25 years of Atherosclerosis Risk in Communities (N=14,514, mean baseline age=54 years; 55% Female; 25.73% African-American) data and latent class mixed models to derive four longitudinal measures of obesity distribution, duration, and severity, operationalized as obesity subtypes. Genome wide association studies on the subclasses were conducted using logistic multi-variate regression models to test for associations between variants and BMI. Meta-analyses across race and sex were conducted in METAL. Functional properties of single nucleotide polymorphisms (SNP) were assessed in FUMA. Results: The four obesity subclasses were: decline (4.1%), stable/slow decline (67.8%) [referent], moderate increase (24.6%), and rapid increase (3.6%). The lead SNP from the decline group was known, on chromosome 7 (rs1196508, beta (SE) = -0.44 (0.089), p = 6.84x10 -7 , minor allele frequency (MAF) = 0.36), and nearest to a tyrosine phosphatase receptor, PTPRZ1 , in the central nervous system. We combined moderate and rapid increase for analyses; they shared a common top, unknown, SNP on chromosome 5 (rs76956550, beta (SE) = -0.37 (0.074), MAF=0.12), nearest to PGAM5P1 . The top SNP within the rapid increase group was unknown, on chromosome 14 (rs580888, beta(SE) = -0.57 (0.11), MAF=0.35), and nearest to the OTX2 gene, a transcription factor involved in production of dopaminergic neurons. Mutations of this gene are associated with pituitary hormone deficiency. Conclusion: Our GWAS of obesity subclasses demonstrated unique genetic and functional properties between groups, supporting the contention that CVD risk may differ across obese individuals. We intend to replicate our novel association of OTX2 with BMI, waist-to-hip ratio, and waist circumference in people who rapidly gained weight in other large longitudinal cohorts.

Introduction: Experimental and observational evidence suggests that aberrant branched chain amino acid (BCAA; leucine, isoleucine, and valine) metabolism promotes insulin resistance and downstream disease, prompting interest in BCAA catabolism as a treatment target. Most published research has evaluated the three BCAAs in combination, although each BCAA may harbor distinct phenotypic effects, as valine is glucogenic, leucine is ketogenic, and isoleucine is glucogenic and ketogenic. Methods: Because studies examining individual phenotypic effects of BCAAs that account for the effects of other BCAAs have been limited by high correlation between BCAAs and strong confounding by dietary patterns and shared genetic loci, we applied a novel causal inference method developed to address these limitations: multivariable Mendelian randomization (MVMR). In a study of UK Biobank European ancestry participants, we examined 1,468 phenotypes that included metabolic, cardiovascular, cancer, cognitive, hematopoietic, and musculoskeletal domains. Polygenic risk score (PRS) instrumental variables were constructed using genotypic and circulating BCAA phenotypic data in European ancestry UK Biobank participants and the Crosspred method was used to correct for overfitting. Significant independent causal effects were defined as estimates with P- values< 1.1x10 -5 [0.05/(1,468 phenotypes*3 BCAAs)]. Results: The n=97,469 participants with measured BCAAs were on average middle aged (mean age=57), female (54%), and overweight [mean body mass index (BMI)=27]. Each BCAA PRS strongly predicted its circulating concentrations ( P- value-100 ). MVMR methods did not identify any phenotypes for which all three BCAAs showed evidence of a directionally consistent and significant independent causal effect. However, there were numerous instances where BCAAs harbored independent and directionally inconsistent statistically significant effects. For example, significant positive causal effects for valine independent of isoleucine and leucine and significant negative causal effects for leucine independent of valine and isoleucine were identified for BMI, glycated hemoglobin, and type 2 diabetes; no significant independent causal effects for isoleucine with BMI, glycated hemoglobin, and type 2 diabetes were identified. In contrast, models without control for the effects of other BCAAs suggested that isoleucine, leucine and valine each had significant positive causal effects on BMI, glycated hemoglobin, and type 2 diabetes. When examining other phenotypes using MVMR, heterogeneity in the effects of individual BCAAs was detected for cardiovascular and cognitive phenotypes, among others. Conclusion: BCAAs may harbor distinct and opposing effects on metabolism, health, and disease, motivating further studies that examine the independent effects of these essential amino acids.

DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analysed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n = 127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n = 144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root-mean-square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbours, logistic regression, penalized functional regression, random forest, and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder. Large genetically informative cohorts of individuals with ASD have led to the identification of a limited number of common genome-wide significant (GWS) risk loci to date. However, many more common genetic variants are expected to contribute to ASD risk given the high heritability. Here, we performed a genome-wide association study (GWAS) on 6222 case-pseudocontrol pairs from the Simons Foundation Powering Autism Research for Knowledge (SPARK) dataset to identify additional common genetic risk factors and molecular mechanisms underlying risk for ASD. We identified one novel GWS locus from the SPARK GWAS and four significant loci, including an additional novel locus from meta-analysis with a previous GWAS. We replicated the previous observation of significant enrichment of ASD heritability within regulatory regions of the developing cortex, indicating that disruption of gene regulation during neurodevelopment is critical for ASD risk. We further employed a massively parallel reporter assay (MPRA) and identified a putative causal variant at the novel locus from SPARK GWAS with strong impacts on gene regulation (rs7001340). Expression quantitative trait loci data demonstrated an association between the risk allele and decreased expression of DDHD2 (DDHD domain containing 2) in both adult and prenatal brains. In conclusion, by integrating genetic association data with multi-omic gene regulatory annotations and experimental validation, we fine-mapped a causal risk variant and demonstrated that DDHD2 is a novel gene associated with ASD risk.

Background Prospective studies have suggested higher factor VIII (FVIII) levels are an independent risk factor for coronary heart disease (CHD) and stroke. However, limited information, including on genetic and epigenetic contributors to FVIII variation, is available specifically among African Americans (AAs), who have higher FVIII levels than Europeans. Objectives We measured FVIII levels in ~3400 AAs from the community-based Jackson Heart Study and assessed genetic, epigenetic, and epidemiological correlates of FVIII, as well as incident cardiovascular disease (CVD) associations. Methods We assessed cross-sectional associations of FVIII with CVD risk factors as well as incident CHD, stroke, heart failure, and mortality associations. We additionally assessed associations with TOPMed whole genome sequencing data and an epigenome-wide methylation array. Results Our results confirmed associations between FVIII and risk of incident CHD events and total mortality in AAs; mortality associations were largely independent of traditional risk factors. We also demonstrate an association of FVIII with incident heart failure, independent of B-type natriuretic peptide. Two genomic regions were strongly associated with FVIII (ABO and VWF). The index variant at VWF is specific to individuals of African descent and is distinct from the previously reported European VWF association signal. Epigenome-wide association analysis showed significant FVIII associations with several CpG sites in the ABO region. However, after adjusting for ABO genetic variants, ABO CpG sites were not significant. Conclusions Larger sample sizes of AAs will be required to discover additional genetic and epigenetic contributors to FVIII phenotypic variation, which may have consequences for CVD health disparities.

Background Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Results We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. Conclusion This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10-9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Genetic studies on telomere length are important for understanding age-related diseases. Prior GWAS for leukocyte TL have been limited to European and Asian populations. Here, we report the first sequencing-based association study for TL across ancestrally-diverse individuals (European, African, Asian and Hispanic/Latino) from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. We used whole genome sequencing (WGS) of whole blood for variant genotype calling and the bioinformatic estimation of telomere length in n=109,122 individuals. We identified 59 sentinel variants (p-value

Mitochondrial DNA copy number (mtDNA-CN) measured from blood specimens is a minimally invasive marker of mitochondrial function that exhibits both inter-individual and intercellular variation. To identify genes involved in regulating mitochondrial function, we performed a genome-wide association study (GWAS) in 465,809 White individuals from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank (UKB). We identified 133 SNPs with statistically significant, independent effects associated with mtDNA-CN across 100 loci. A combination of fine-mapping, variant annotation, and co-localization analyses was used to prioritize genes within each of the 133 independent sites. Putative causal genes were enriched for known mitochondrial DNA depletion syndromes (p = 3.09 × 10–15) and the gene ontology (GO) terms for mtDNA metabolism (p = 1.43 × 10–8) and mtDNA replication (p = 1.2 × 10–7). A clustering approach leveraged pleiotropy between mtDNA-CN associated SNPs and 41 mtDNA-CN associated phenotypes to identify functional domains, revealing three distinct groups, including platelet activation, megakaryocyte proliferation, and mtDNA metabolism. Finally, using mitochondrial SNPs, we establish causal relationships between mitochondrial function and a variety of blood cell-related traits, kidney function, liver function and overall (p = 0.044) and non-cancer mortality (p = 6.56 × 10–4).

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Moreover, PIRs of cell-type-specific SIPs show enriched heritability of relevant blood cell trait (s), and are more enriched with GWAS variants associated with blood cell traits compared to PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type. Importantly, SIP subnetworks incorporating cell-type-specific SIPs and ATAC-seq peaks help interpret GWAS variants. Examples include GWAS variants associated with platelet count near the megakaryocyte SIP gene EPHB3 and variants associated lymphocyte count near the native CD4 T-Cell SIP gene ETS1. Interestingly, around 25.7% ~ 39.6% blood cell traits GWAS variants residing in SIP PIR regions disrupt transcription factor binding motifs. Importantly, our analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.