Brandon L. Pierce

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 .

Telomere length is a biomarker of aging and disease risk. Most human studies have assessed average telomere length, limiting our understanding of variability across chromosome arms. Using long-read sequencing data from >2500 All of Us participants, we estimate chromosome-specific telomere lengths and characterize sources of biological and technical variation. Telomere length varies by chromosome arm, accounting for 9.1% of total variance. Substantial variance (8.9%) in chromosome-specific telomere lengths is attributable to individual, independent of age, suggesting that inter-individual differences in length are established at birth and maintained through life. Age is inversely associated with length for all arms, but longer arms show stronger association. We demonstrate that chromosome-specific telomere estimates enable analysis of disease associations for individual telomeres and for individuals’ shortest telomere. Overall, this work highlights the utility of long-read sequencing for population-scale analysis of chromosome-specific telomere lengths and provides a framework to guide future studies.

Background: In Bangladesh, > 50 million individuals are chronically exposed to inorganic arsenic (iAs) through drinking water, increasing risk for cancer and other iAs-related diseases. Previous studies show that individuals’ ability to metabolize and eliminate iAs, and their risk of toxicity, is influenced by genetic variation in the AS3MT and FTCD gene regions. Methods: To identify additional loci influencing arsenic metabolism, we used data from Bangladeshi individuals to conduct genome-wide association analyses of the relative abundances of arsenic species measured in both urine (n = 6,540) and blood (n = 976). These species include iAs, monomethylated arsenic (MMA) and dimethylated arsenic (DMA) species. Results: In analyses of urine arsenic species, we identified a novel association signal in the FMO gene cluster (1q24.3), with the lead SNP residing in FMO3 (MMA% P = 4.2x10^-16). In analyses of blood arsenic species, we identified an additional signal in the FMO cluster, with the lead SNP residing in FMO4 (DMA% P = 2.3x10^-22) and a novel signal at 10q25.1, with the lead SNP in GSTO1 (DMA% P = 5.3x10^-13). Lead SNPs at FMO3 and GSTO1 are associated with the splicing of FMO3 and GSTO1, respectively, in multiple tissue types, but also contain missense variants. The lead SNPs at FMO4 are associated with FMO4 expression level in multiple tissue types. These newly identified SNPs did not show a clear association with risk for arsenic-induced skin lesions (P > 0.05), based on 3,448 cases and 5,207 controls. Conclusion: We identified novel loci influencing arsenic metabolites measured in both urine and blood. FMOs are involved in the oxidation of xenobiotics but have no known direct role in arsenic metabolism, while GSTO1 has a well-established role in catalyzing the reduction of arsenic species. The novel associations we report appear specific to blood or urine, with no detectable impact on skin toxicity risk, pointing to complexities in arsenic metabolism and its genetic contributors that require further study.

Supplementary Table S8 shows the gene-ontology enrichment results for differentially methylated CpGs identified in all genomic regions in African American and European American men

Supplementary Figure S4 shows the distribution of the post-QC CpGs across EPIC Infinium I and Infinium II assays, chromosomes, and genomic regions including CpG islands and transcription start sites.

Supplementary Table S5 shows genes identified in both AA and EA, based on differentially methylated CpGs that were previously identified as differentially methylated in Prostate Cancer.

Supplementary Figure S1 shows the results of a Principal Component Analysis of participant SNP genotypes, demonstrating separate clusters of those with African American and European American ancestry.

Supplementary Figure S2 shows a Principal Component Analysis of DNA methylation in the benign and tumor tissue of African American and European American individuals, demonstrating tissue type as the largest source of variation (A&B). Additionally, it shows that within tumor samples PC1 is associated with Gleason Score (C).

Supplementary Figure S6 shows the distribution of differentially methylated CpGs across the genome in African Americans and European Americans compared with the overall probe distribution of the tested EPIC array CpGs.

Supplementary Figure S5 shows a dendogram demonstrating the initial clustering analysis based on all post-QC probes in which there is sample clustering by tissue type.