Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood (n = 6454) and lung (n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank-derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Measures from affinity-proteomics platforms often correlate poorly, challenging interpretation of protein associations with genetic variants and phenotypes. Here, we examine 2157 proteins measured on both SomaScan 7k and Olink Explore 3072 across 1930 participants with genetic similarity to European, African, East Asian, and Admixed American ancestry references. Inter-platform correlation coefficients for these 2157 proteins follow a bimodal distribution (median r = 0.30). We evaluate protein measure associations with genetic variants, and find approximately 25-30

Introduction: The role of circulating monocytes in non-ischemic cardiac remodeling and heart failure (HF) is complex and unclear, due in part to monocyte heterogeneity and plasticity. We assessed the hypothesis that monocyte gene expression profiles reflecting activation and tissue inflammation are associated with cardiac structure and function and incident adjudicated HF in the Multi-Ethnic Study of Atherosclerosis. Methods: Monocytes were isolated from peripheral blood, and RNA was quantified using an Illumina BeadChip microarray. Cardiac magnetic resonance was performed concurrently. We used multivariable linear regression to estimate cross-sectional associations between gene expression levels and cardiac structure and function and Cox regression to estimate associations with time to incident HF. Results: We studied 12,369 transcripts mapping to 9,430 genes among 813 participants (mean age 69±9 years; 50% female; 22% Black; 29% Hispanic). Independent of traditional risk factors, expression levels of 55 transcripts were associated with left ventricular (LV) ejection fraction, 1136 with LV strain, 16 with LV geometry, 1020 with myocardial interstitial fibrosis, and 483 with left atrial size (FDR<0.05). Enrichment analysis implicated T and B cell activation, cytokine production, phagocytosis, wound healing, oxidative stress, and cell migration. Expression levels of three genes—PCCB, MTCP1, and VIM—were associated with more than one cardiac metric as well as time to clinical HF ( n =45 events over a median follow-up of 7.7 years). Conclusion: These unique data support an association between monocyte-mediated immune processes and subclinical cardiac remodeling and incident HF in the absence of ischemic injury. Agnostically identified profiles were enriched for processes related to both pro-inflammatory and pro-resolving activated monocyte function and immunometabolism, as well as tissue migration and homeostasis. These insights may help generate hypotheses toward novel therapeutic targets for HF.

BACKGROUND:Exercise unmasks limitations in multi-organ system reserve capacity characteristic of heart failure with preserved ejection fraction (HFpEF). However, the metabolic and genetic underpinnings of exercise deficits, and their cumulative contribution to HFpEF severity and prognosis, remain incompletely understood. METHODS:We used invasive cardiopulmonary exercise testing (iCPET), metabolite profiling, and genomics to simultaneously characterize seven exercise physiologic deficits in HFpEF patients: reduced exercise stroke volume and heart rate, steep pulmonary capillary wedge pressure/cardiac output (PCWP/CO) slope, elevated pulmonary vascular resistance, pulmonary mechanical limitation to exercise, impaired peripheral oxygen extraction, and obesity-related exaggerated metabolic cost of initiating exercise. We first mapped the distribution, functional, and prognostic significance of these exercise deficits. We then applied LASSO regression to identify metabolite signatures of each exercise deficit, and measured the relation of these signatures with clinical-demographic features, cardiac magnetic resonance imaging, and incident HF in 6345 individuals in the Multi-Ethnic Study of Atherosclerosis (MESA) study with ≈20-year follow-up. Finally, we mapped deficit-implicated metabolites to tissue-specific genetic variation in ≈2M individuals with HF, and in the largest genome-wide association study (GWAS) studies of HFpEF comorbidities (obesity, renal disease, diabetes) to evaluate shared metabolic mechanisms of HFpEF pathophysiology. RESULTS:Our iCPET HFpEF cohort (61.7±14.1 years, 54% female, BMI 30.6±6.7 kg/m2 ) exhibited a broad range of compound cardiac and extra-cardiac exercise deficits. Individuals with ≥5 exercise deficits had a nearly 4-fold higher hazard of incident cardiovascular event or mortality (HR 3.90, 95% CI 1.74-8.75, P<0.0001). The metabolite signature of exercise PCWP/CO slope conferred a HR of 1.43 per SD increment, 95% CI 1.20-1.71, P<0.001 for incident HF in MESA. Addition of all iCPET deficit metabolic signatures in a single model yielded ≈20% continuous net reclassification improvement over traditional HFpEF risk factors. Genes implicated by the exercise deficit metabolome were enriched in the HF GWAS (≈2M) and shared with obesity, renal dysfunction, and diabetes, highlighting a lifelong shared predisposition to HF (including HFpEF) and its comorbidities. CONCLUSIONS:Organ-specific responses to exercise and their circulating metabolite signatures are strongly linked to HFpEF development and prognosis. These results offer a paradigm for parsing HFpEF subphenotypes and prioritizing metabolic mechanisms of HFpEF.

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models’ prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait’s relevance to the PBMCs used to build our transcriptome prediction models.

Introduction:Coronary artery disease (CAD) is a leading cause of death and disability worldwide. Although genome-wide association studies (GWAS) have identified over 300 loci associated with CAD risk, the molecular mechanisms linking these variants to disease and subclinical atherosclerosis are not fully understood. Methods:We performed integration of multi-ancestry CAD GWAS with transcriptomic data from the Multi-Ethnic Study of Atherosclerosis (MESA) obtained through the Trans-Omics for Precision Medicine (TOPMed) program. For integration, we applied Bayesian colocalization analysis with and without statistical fine-mapping to identify genes whose expression levels colocalize with CAD-associated loci. We further applied causal weighted gene co-expression network analysis (cWGCNA) to identify gene co-expression modules and key driver genes associated with subclinical atherosclerosis traits in MESA. Results:We identified 108 genes showing evidence of colocalization with CAD loci, including 24 shared between the two colocalization approaches and 48 novel genes not previously reported in CAD GWAS. Follow-up replication and validation analyses prioritized 5 novel ( CCDC30, ZEB1-AS1, ZPR1, PLEKHJ1 and AC018816.3 ) and 8 previously reported genes ( DHDDS, DDX59, LNPEP, DAGLA, ZKSCAN1, LIPA, OPRL1 and EIF2B2 ) with putative roles in both CAD and subclinical atherosclerosis. cWGCNA identified five gene modules significantly associated with subclinical atherosclerosis in MESA. Additionally, three key driver genes ( ATG9B, PRAM1 and ZBTB46 ) identified by cWGCNA were also identified as CAD-colocalized genes. Discussion:Our integrative analysis highlights key genetic drivers and regulatory networks underlying CAD and subclinical atherosclerosis. These findings underscore the value of incorporating statistical fine-mapping in colocalization studies and demonstrate the utility of combining colocalization with co-expression network analysis to prioritize functional genes and pathways.

BACKGROUND AND AIMS:People with Type 2 diabetes (T2D) are twice as likely to develop cardiovascular disease (CVD), though not all excess risk has been fully elucidated. Plasma metabolomics profiles shared between these conditions may uncover molecular mechanisms linking T2D to CVD. METHODS:We conducted a cross-sectional case-control analysis, comparing T2D individuals who had prevalent CVD to those without CVD at the time of metabolite measurement. Using untargeted liquid chromatography-mass spectrometry (LC-MS), we collected 522 metabolite abundances measured in 1374 participants with T2D (224 CVD cases) from the Trans-Omics for Precision Medicine (TOPMed) program. We used a mixed effects linear model to assess the association of CVD events with each metabolite abundance, adjusting for key covariates. Metabolites meeting a suggestive significance threshold were examined using metabolite set enrichment analysis and evaluated for replication in an independent cohort Atherosclerosis Risk in Communities (ARIC) (n = 1891; 214 CVD cases). We performed meta-analysis to combine both the discovery and replication associations, and assessed overall significance using an experiment-wide Bonferroni-corrected threshold. RESULTS:Metabolites meeting a suggestive threshold were enriched in metabolite sets linked to obesity and kidney disease. Meta-analysis identified eight metabolites reaching experiment-wide significance, confirming previously established associations of asymmetric dimethylarginine, phosphatidylcholines, and gluconic acid, while additionally identifying specific phosphatidylethanolamine species, N-acetyl-L-methionine, and allantoin associated with prevalent CVD among individuals with T2D. CONCLUSIONS:Our results established and replicated metabolite associations with prevalent CVD in people with T2D. These metabolites may help characterize metabolic alterations underlying cardiovascular complications that arise in T2D.

Age is a major risk factor for many diseases, but the biological processes driving aging are heterogeneous across individuals. Efforts to untangle differences between chronological and biological age have focused on identifying age-associated markers, such as 'omics clocks. Many 'omics features, including proteins, are strongly associated with age, and genetics contribute to variance in these measures. However, few studies have identified genetic drivers of interindividual variability in 'omics changes over time. Using longitudinal proteomics data (Olink 3k) from the Multi-Ethnic Study of Atherosclerosis (MESA), we calculated a protein slope for each individual (n=2,007) and protein (n=2,737) across 3 visits spanning 14-18 years, then conducted a genome-wide analysis for each slope, both with and without adjusting for baseline protein level. Subsets in UK Biobank (UKB; n=948) and CARDIA (n=1,328) with longitudinal proteomics data were used for replication. We considered additional methods for modeling of protein change and variability, including linear mixed models, SNP-by-age interactions, and variance quantitative trait loci. Without baseline adjustment, only 19 proteins (20 credible sets) had a slope pQTL in MESA, with poor replication in UKB and CARDIA. With baseline adjustment, 607 proteins (698 credivle sets) had a slope pQTL and over 70% replicated in CARDIA and/or UKB; such baseline adjusted models may, however, be subject to collider bias. Longitudinal and cross-sectional interaction models identified fewer than 14 pQTLs, suggesting they were generally underpowered; but 73% of proteins with a variance pQTL also had a slope pQTL. By examining effect direction concordance, replication rate, directed acyclic graphs, and signal overlap with other models we demonstrate that many baseline-adjusted slope pQTLs may be arising due to model misspecification or regression to the mean. Overall, our results highlight considerations for modeling strategies of change phenotypes and build on understanding of potential genetic mechanisms influencing interindividual proteome changes over time.

Trimethylamine N-oxide (TMAO) and its related metabolites have been linked to cardiovascular disease (CVD), but their impact on DNA methylation remains unclear. Investigating these relationships may clarify the role of epigenetic mechanisms in diseases. This study analyzed data from 1,356 adults from the Cardiovascular Health Study (CHS) and the Multi-Ethnic Study of Atherosclerosis (MESA). Using stable-isotope dilution liquid chromatography with on-line electrospray ionization tandem mass spectrometry (LC–MS), we quantified TMAO and five related metabolites. DNA methylation levels were measured using Illumina BeadChip arrays. Epigenome-wide association analyses and meta-analyses were conducted across approximately 430,000 CpG sites. To explore the functional significance of the identified CpGs, we performed gene set enrichment analysis and Mendelian randomization (MR) analyses. We identified 143 metabolite-CpG pairs at FDR < 0.05, including four CpGs for TMAO (P ≤ 4.03e-7), 12 for betaine (P ≤ 1.19e-6), 53 for γ-butyrobetaine (P ≤ 6.11e-6), five for carnitine (P ≤ 5.42e-7), six for choline (P ≤ 2.81e-7), and 63 for crotonobetaine (P ≤ 7.25e-6). CpGs associated with γ-butyrobetaine showed moderate correlation with crotonobetaine-associated CpGs. In total, these metabolite-linked CpGs were mapped to 108 genes. Gene set enrichment analysis revealed 145 significantly enriched gene sets, including nine highly relevant to CVD risk. Furthermore, CpGs were enriched in 80 immunologic signature gene sets (FDR < 0.05). MR analysis identified three CpGs associated with coronary artery disease (CAD), including hypermethylation at cg18705301 (NDUFAF1), which was inversely associated with betaine levels and linked to a lower risk of CAD (P = 1.8e-5). This study identified specific DNA methylation sites associated with TMAO and related metabolites. These epigenetic changes may contribute to CVD risk through multiple pathways. Future research should validate these findings and explore their clinical implications.

Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. This study evaluated associations between blood-based ORS and COPD-related traits in both smoking-enriched and general population cohorts. ORS were developed and tested in 3,339 participants of Genetic Epidemiology of COPD (COPDGene) with blood RNA-sequencing, proteomic, and metabolomic data. Single- and multi-omic risk scores were trained on 24 cross-sectional and five longitudinal traits using 80

Adverse cardiovascular events are emerging with the use of immune checkpoint therapies in oncology. Using datasets in the Trans-Omics for Precision Medicine program (Multi-Ethnic Study of Atherosclerosis, Jackson Heart Study [JHS], and Framingham Heart Study), we examined the association of immune checkpoint plasma proteins with each other, their associated protein network with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C), and the association of HDL-C- and LDL-C-associated protein networks with all-cause mortality risk. Plasma levels of LAG3 and HAVCR2 showed statistically significant associations with mortality risk. Colocalization analysis using genome wide-association studies of HDL-C or LDL-C and protein quantitative trait loci from JHS and the Atherosclerosis Risk in Communities identified TFF3 rs60467699 and CD36 rs3211938 variants as significantly colocalized with HDL-C; in contrast, none colocalized with LDL-C. The measurement of plasma LAG3, HAVCR2, and associated proteins plus targeted genotyping may identify patients at increased mortality risk.

Most genetic variants associated with complex traits and diseases occur in non-coding genomic regions and are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterize 14,324 ancestrally diverse RNA-sequencing samples from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and integrate whole genome sequencing data to perform cis and trans expression and splicing quantitative trait locus (cis-/trans-e/sQTL) analyses in six tissues and cell types, most notably whole blood (N=6,454) and lung (N=1,291). We show this dataset enables greater detection of secondary cis-e/sQTL signals than was achieved in previous studies, and that secondary cis-eQTL and primary trans-eQTL signal discovery is not saturated even though eGene discovery is. Most TOPMed trans-eQTL signals colocalize with cis-e/sQTL signals, suggesting many trans signals are mediated by cis signals. We fine-map European UK BioBank GWAS signals from 164 traits and colocalize the resulting 34,107 fine-mapped GWAS signals with TOPMed e/sQTL signals, finding that of 10,611 GWAS signals with a colocalization, 7,096 GWAS signals colocalize with at least one secondary e/sQTL signal. These results demonstrate that larger e/sQTL analyses will continue to uncover secondary e/sQTL signals, and that these new signals will benefit GWAS interpretation.

Background Few studies have evaluated the prevalence or severity of mitral valve prolapse (MVP) and other valvular heart disease (VHD) in the rural U.S. South, where strategies for early detection are crucial for risk stratification and prevention. Objectives We assessed the prevalence of MVP and other VHD in a rural U.S. South cohort and examined associations with cardiovascular disease (CVD) risk. We also evaluated associations between MVP severity, high-sensitivity cardiac troponin T, and N-terminal pro-B-type natriuretic peptide. Methods We conducted a cross-sectional analysis from the Risk Underlying Rural Areas Longitudinal study. Logistic regression assessed associations between participant characteristics and MVP, other VHD, or either. Weighted models assessed odds for MVP and other VHD by 10-year CVD risk categories using the Predicting Risk of CVD Events (PREVENT) score. Among a subset, we evaluated associations between MVP severity and cardiac biomarkers. Results Among 2,621 participants (68.7% women), MVP and other VHD were present in 1.9% and 11.2%, respectively. Compared to the low PREVENT risk group, odds of MVP were lower and odds of VHD were higher among borderline and intermediate/high groups. High-sensitivity cardiac troponin T was lower in MVP vs non-MVP (0.64; 95% CI: 0.58-0.71), without difference by severity of MVP. N-terminal pro-B-type natriuretic peptide was higher in participants with severe MVP than non-MVP (2.03; 95% CI: 1.49-2.78). Conclusions MVP prevalence aligned with population-based epidemiologic studies. PREVENT risk category may identify individuals at higher risk for MVP and for other VHD. Future studies are needed to evaluate relationships between MVP/VHD status and clinical events.

Background:The association of overall cardiovascular health (CVH) with changes in DNA methylation (DNAm) has not been well characterized. Methods:We calculated the American Heart Association's Life's Essential 8 (LE8) score to reflect CVH in five cohorts with diverse ancestry backgrounds. Epigenome-wide association studies (EWAS) for LE8 score were conducted, followed by bioinformatic analyses. DNAm loci significantly associated with LE8 score were used to calculate a CVH DNAm score. We examined the association of the CVH DNAm score with incident CVD, CVD-specific mortality, and all-cause mortality. Results:We identified 609 CpGs associated with LE8 score at false discovery rate (FDR) < 0.05 in the discovery analysis and at Bonferroni corrected P < 0.05 in the multi-cohort replication stage. Most had low-to-moderate heterogeneity (414 CpGs [68.0%] with I2 < 0.2) in replication analysis. Pathway enrichment analyses and phenome-wide association study (PheWAS) search associated these CpGs with inflammatory or autoimmune phenotypes. We observed enrichment for phenotypes in the EWAS catalog, with 29-fold enrichment for stroke (P = 2.4e-15) and 21-fold for ischemic heart disease (P = 7.4e-38). Two-sample Mendelian randomization (MR) analysis showed significant association between 141 CpGs and ten phenotypes (261 CpG-phenotype pairs) at FDR < 0.05. For example, hypomethylation at cg20544516 (MIR33B; SREBF1) associated with lower risk of stroke (P = 8.1e-6). In multivariable prospective analyses, the CVH DNAm score was consistently associated with clinical outcomes across participating cohorts, the reduction in risk of incident CVD, CVD mortality, and all-cause mortality per standard deviation increase in the DNAm score ranged from 19% to 32%, 28% to 40%, and 27% to 45%, respectively. Conclusions:We identified new DNAm signatures for CVH across diverse cohorts. Our analyses indicate that immune response-related pathways may be the key mechanism underpinning the association between CVH and clinical outcomes.

Introduction: DNA methylation (DNAm) predictors of high sensitivity C-reactive protein (CRP) offer a stable and accurate means of assessing chronic inflammation, bypassing the CRP protein fluctuations secondary to acute illness. Poor sleep health is associated with elevated inflammation and blood CRP levels which may explain associations of sleep insufficiency with metabolic, cardiovascular and neurological diseases. Our study aims to characterize the relationships obstructive sleep apnea (OSA)-associated phenotypes and CRP markers —blood, genetic, and epigenetic indicators—within the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: Multiple polygenetic risk score (PRS)-CRP scores were evaluated for their association with circulating CRP in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort to select the best-performing PRS-CRP for association analysis in HCHS/SOL. Methylation risk scores (MRS)-CRP and PRS-CRP were constructed separately in HCHS/SOL for each individual as weighted sums of methylation beta values or allele counts, respectively. OSA-related phenotypes were measured using self-reported questionnaires and objective measurements. Survey-weighted linear and logistic regressions estimated the associations between OSA-related phenotypes (apnea-hypopnea index (AHI), minimum oxyhemoglobin saturation during sleep (min SpO2), and excessive daytime sleepiness (EDS)), diabetes and hypertension with CRP markers while adjusting for age, sex, BMI, study center, and the first five principal components of genetic ancestry. Results: We included 2221 HCHS/SOL participants (age range 37-76 yrs, 65.7% female) in the analysis. Both the MRS-CRP (95% confidence interval (CI): 0.32-0.42, p = 3.3 x 10 -38 ) and the PRS-CRP (95% CI: 0.15-0.25, p = 1 x 10 -14 ) were associated with blood CRP level. MRS-CRP was associated with AHI, min SpO2, diabetes and hypertension, while PRS-CRP markers was not. EDS was associated only with circulating CRP levels, while diabetes was associated with both circulating and MRS-CRP. Associations between OSA traits and metabolic comorbidities weakened after adjusting for MRS-CRP, with a strong impact of diabetes. Conclusions: MRS-CRP is a promising estimate for systemic and chronic inflammation, which either mediates or serves as a common cause of the association between OSA-related phenotypes and related comorbidities, especially diabetes.

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

There is insufficient understanding of the molecular basis of prostate cancer (PCa) across different populations. We perform a large-scale proteome-wide association study (PWAS) to identify proteins with genetically regulated expression in plasma to be associated with PCa risk across populations. We develop genetic prediction models for expression of 1578, 1993, 1218, and 1390 proteins for African (n = 450), European (n = 758), Asian (n = 289), and Hispanic/Latino (n = 474) males, respectively, and evaluate associations of genetically regulated protein expression with PCa risk in 19,391 PCa cases and 61,608 controls of African population, 122,188 cases and 604,640 controls of European population, 10,809 cases and 95,790 controls of Asian population, and 3931 cases and 26,405 controls of Hispanic/Latino population. We identify three, four, 15, and 73 PCa-associated proteins in African, Hispanic/Latino, Asian, and European populations, respectively, and 83 in trans-population meta-analysis. There are both pan-population and population-specific associations. Our findings provide valuable insights into etiology of PCa.

Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.

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 analyses 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 of 5%, we identified 17,630 unique eQTL credible sets covering 16,538 unique genes; and 24,525 unique sQTL credible sets covering 9,605 unique genes, with lead QTL at P < 5e-8. About 24% of independent eQTLs and independent sQTLs with a minor allele frequency > 1% in JHS were rare (minor allele frequency < 0.1%), and therefore unlikely to be detected, in European ancestry individuals. Finally, we created an open database, which is freely available online, allowing fast query and bulk download of our QTL results.