ArticleGenome medicine2026
An atlas of genetic effects on the monocyte methylome across European and African populations.
Article in Genome medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- An atlas of genetic effects on the monocyte methylome across European and African populations.Genome medicine · 2026Article
- ProteoNexus: an integrative database to characterize genetic architecture, estimate mediation effects, and construct and evaluate prediction models of the plasma proteome.Nucleic acids research · 2026Article
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Abstract
backgroundGenetic regulation of DNA methylation in immune cells may mediate complex disease risk. However, current epigenomic studies are constrained by microarray CpG coverage, mixed-cell tissues, and limited representation of diverse ancestries. Thus, we generated a whole-genome, multi-ancestry atlas of genetic effects on the purified monocyte methylome.
methodsWe first performed whole-genome bisulfite sequencing (WGBS) of purified peripheral blood monocytes and whole-genome sequencing (WGS) from 160 African American (AA) and 298 European American (EA) participants, profiling around 25 million CpG sites. Next, we identified cis-methylation quantitative trait loci (meQTLs), estimated cis-heritability, and evaluated replication against large external meQTL resources. We further trained population-specific DNAm imputation models and applied them to methylome-wide association studies (MWAS) of 41 traits using genome-wide association study summary statistics from the Million Veteran Program. Type 2 diabetes signals were further evaluated using Mendelian randomization and Bayesian colocalization. We also conducted exploratory trans-meQTL mapping.
resultsWe identified 1,480,064 and 1,527,480 CpG sites with at least one cis-meQTL in AA and EA populations, respectively, including 543,869 shared sites and extensive population-specific regulation attributable to both allele-frequency differences and effect-size heterogeneity. Cis-meQTL effects replicated robustly in external datasets: effect sizes correlated strongly with prior studies (EA Pearson’s r = 0.76; 90.8% concordant directions; AA Pearson’s r = 0.71; 86.6% concordant directions). We built DNAm prediction models with cis-h2 > 0.01 for 2,677,714 CpG sites in AA and 1,976,046 CpG sites in EA, achieving mean cross-validated prediction R2 of 0.20 and 0.18. Across 41 traits, MWAS 23,650 significant methylation-phenotype associations (2,116 in AA and 21,534 in EA), of which ~ 98% were not interrogated by Illumina 450 K/EPIC arrays. For type 2 diabetes, MWAS identified 20 CpG sites in AA and 4,023 CpG sites in EA, with substantial support from Mendelian randomization and colocalization. Exploratory trans-meQTL mapping detected widespread long-range associations, with limited cross-study overlap but high directional concordance among shared signals.
conclusionsThis whole-genome, monocyte-resolved, multi-ancestry methylome atlas and accompanying imputation resource expand interpretable methylation variation beyond array-based studies and enable multi-ancestry integration of genetic, epigenetic, and genome-wide association study data to prioritize immune-cell regulatory mechanisms for complex disease.
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