Biomedical subjects

Daniel Levy

Publications and source records attributed to Daniel Levy.

8 recordsLinked to original sources

PubMed2026

eQTM (expression quantitative trait methylation) Atlas: a comprehensive resource of over 11 million DNA methylation-gene expression associations through across 11 tissues and 4 diseases.

MOTIVATION: Epigenome-wide association studies (EWAS) have identified numerous DNA methylation (DNAm) CpG sites associated with complex traits and diseases, but interpretation of those CpG sites remains challenging because in EWAS, CpGs are mostly linked to nearby genes based only on genomic proximity. Expression quantitative trait methylation (eQTM) analyses connect DNAm CpGs with statistically associated gene expression levels. However, a comprehensive, searchable resource integrating eQTMs across diverse tissues and disease contexts has been lacking. RESULTS: We developed the eQTM Atlas, a web-based resource that manually curates more than 11 million DNAm-gene expression associations from eight cohorts, covering 11 tissue types, four broad disease contexts, 173,886 unique CpG probes and 20,231 unique genes. The Atlas supports gene- or CpG- searches by tissue or disease type and finding associated CpG or genes, visualization of cis- and trans-eQTMs through genome browser, heatmap interfaces across various tissues, and cohort-level data downloads. By integrating eQTM results with EWAS resources, the eQTM Atlas enables users to connect disease- or trait-associated CpGs to statistically associated genes rather than relying solely on proximity-based gene annotation, supporting functional interpretation of EWAS findings and generation of disease-specific regulatory hypotheses. AVAILABILITY AND IMPLEMENTATION: The eQTM Atlas is freely available at https://shiny.crc.pitt.edu/eqtm_browser/. The web interface is implemented in R Shiny and hosted through the University of Pittsburgh Center for Research Computing (CRC). Source code is available at https://github.com/ads303/eQTM-Atlas.

DNA methylation↗

PubMed2025

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246 K individuals.

BACKGROUND: Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability. RESULTS: Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate. CONCLUSIONS: This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.

Humans↗

PubMed2025

Polygenic scores for obstructive sleep apnoea reveal pathways contributing to cardiovascular disease.

BACKGROUND: Obstructive sleep apnoea (OSA) is a common chronic condition, with obesity its strongest risk factor. Polygenic scores (PGSs) summarise the genetic liability to phenotype and can provide insights into relationships between phenotypes. Recently, large datasets that include genetic data and OSA status became available, providing an opportunity to utilise PGS approaches to study the genetic relationship between OSA and other phenotypes, while differentiating OSA-specific from obesity-specific genetic factors. METHODS: Using race/ethnic diverse samples from over 1.2 million individuals from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger's MyCode, MGB Biobank, and the Human Phenotype Project, we developed and assessed PGSs for OSA, both without (BMIunadjOSA-PGS) and with adjustment for the genetic contributions of BMI (BMIadjOSA-PGS). FINDINGS: Adjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. Only BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, while both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by females (OR = 1.1, p-value = 0.002, OR = 1.01 p-value = 0.2 in males). OSA PGSs were also associated with body fat measures with some sex-specific associations. INTERPRETATION: Distinct components of OSA genetic risk are related and independent of obesity. Sex-specific associations with body fat distribution measures may explain differing OSA risks and associations with cardiometabolic morbidities between sexes. FUNDING: R01AG080598.

Humans↗

PubMed2025

Epigenetic mechanisms underlying variation of IL-6, a well-established inflammation biomarker and risk factor for cardiovascular disease.

BACKGROUND AND AIMS: Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality worldwide, yet the underlying molecular mechanisms remain less understood. Chronic low-grade inflammation is a complex immune response contributing to the pathophysiology of cardiovascular disease. This response is signaled in part by interleukin-6 (IL-6), a pleiotropic, pro-inflammatory cytokine. Phenotypic variance in circulating IL-6 level may be explained in part by DNA methylation which is increasingly being associated with cardiovascular effects. METHODS: In this study we evaluated methylated DNA (CpG sites) associated with blood IL-6 levels across ∼4,400 ancestrally diverse individuals (81 % self-reported White; 9 % Black or African American, 8 % Hispanic or Latino/a, and 2 % Chinese American). RESULTS: We identified 178 CpG sites associated with IL-6 (p<0.05/∼395,000). Among the sites, cg04437762 is located within the transcription unit of IL6R, a current therapeutic target for inflammatory disease, and cg26692003 and cg00464927 were significant for IL6 and IL6ST trans-CpG-gene transcripts. Functional gene expression downstream of methylation identified cellular response to IL-6 and B-cell regulation and activation pathways. Four genes were linked with both a genetic component of cardiovascular disease and an IL-6 associated CpG site. Three CpG sites identified through Mendelian randomization analyses supported inference of a causal effect on IL-6 levels, including the LYN gene that regulates immune cell signaling and has been previously associated with atherosclerosis. CONCLUSIONS: Overall, we identified several novel IL-6-CpG sites and downstream pathways affected by methylation. Follow-up functional studies including the regulation of IL-6 would complement current knowledge of CVD pathophysiology and potential therapeutic targets.

Humans↗

PubMed2025

Unveiling the Genetic Landscape of Coronary Artery Disease Through Common and Rare Structural Variants.

BACKGROUND: Genome-wide association studies have identified several hundred susceptibility single nucleotide variants for coronary artery disease (CAD). Despite single nucleotide variant-based genome-wide association studies improving our understanding of the genetics of CAD, the contribution of structural variants (SVs) to the risk of CAD remains largely unclear. METHOD AND RESULTS: We leveraged SVs detected from high-coverage whole genome sequencing data in a diverse group of participants from the National Heart Lung and Blood Institute's Trans-Omics for Precision Medicine program. Single variant tests were performed on 58 706 SVs in a study sample of 11 556 CAD cases and 42 907 controls. Additionally, aggregate tests using sliding windows were performed to examine rare SVs. One genome-wide significant association was identified for a common biallelic intergenic duplication on chromosome 6q21 (P=1.54E-09, odds ratio=1.34). The sliding window-based aggregate tests found 1 region on chromosome 17q25.3, overlapping USP36, to be significantly associated with coronary artery disease (P=1.03E-10). USP36 is highly expressed in arterial and adipose tissues while broadly affecting several cardiometabolic traits. CONCLUSIONS: Our results suggest that SVs, both common and rare, may influence the risk of coronary artery disease.

Humans↗

PubMed2024

The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.

Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one's global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.

Journal Article↗

PubMed2024

Association analysis between an epigenetic alcohol risk score and blood pressure.

BACKGROUND: Epigenome-wide association studies have identified multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. This study aimed to test the hypothesis that an alcohol consumption epigenetic risk score (ERS) is associated with blood pressure (BP) traits. RESULTS: We implemented an ERS based on a previously reported epigenetic signature of 144 alcohol-associated CpGs in meta-analysis of participants of European ancestry. We found a one-unit increment of ERS was associated with eleven drinks of alcohol consumed per day, on average, across several cohorts (p < 0.0001). We examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with BP levels, i.e., a one-unit increase in ERS was associated with 0.74 mm Hg (p = 0.002) higher SBP and 0.50 mm Hg (p = 0.0006) higher DBP, but not with HTN. Longitudinal analyses in FHS (n = 3260) and five independent external cohorts (n = 4021) showed that the baseline ERS was not associated with a change in BP over time or with incident HTN. CONCLUSIONS: Our findings demonstrate that the ERS has potential clinical utility in assessing lifestyle factors related to cardiovascular risk, especially when self-reported behavioral data (e.g., alcohol consumption) are unreliable or unavailable.

Humans↗

PubMed2024

Association analysis of mitochondrial DNA heteroplasmic variants: Methods and application.

We rigorously assessed a comprehensive association testing framework for heteroplasmy, employing both simulated and real-world data. This framework employed a variant allele fraction (VAF) threshold and harnessed multiple gene-based tests for robust identification and association testing of heteroplasmy. Our simulation studies demonstrated that gene-based tests maintained an appropriate type I error rate at α = 0.001. Notably, when 5 % or more heteroplasmic variants within a target region were linked to an outcome, burden-extension tests (including the adaptive burden test, variable threshold burden test, and z-score weighting burden test) outperformed the sequence kernel association test (SKAT) and the original burden test. Applying this framework, we conducted association analyses on whole-blood derived heteroplasmy in 17,507 individuals of African and European ancestries (31 % of African Ancestry, mean age of 62, with 58 % women) with whole genome sequencing data. We performed both cohort- and ancestry-specific association analyses, followed by meta-analysis on both pooled samples and within each ancestry group. Our results suggest that mtDNA-encoded genes/regions are likely to exhibit varying rates in somatic aging, with the notably strong associations observed between heteroplasmy in the RNR1 and RNR2 genes (p < 0.001) and advance aging by the Original Burden test. In contrast, SKAT identified significant associations (p < 0.001) between diabetes and the aggregated effects of heteroplasmy in several protein-coding genes. Further research is warranted to validate these findings. In summary, our proposed statistical framework represents a valuable tool for facilitating association testing of heteroplasmy with disease traits in large human populations.

Humans↗