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Background <p>Genetic 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.</p> Methods <p>We 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 <i>cis</i>-methylation quantitative trait loci (meQTLs), estimated <i>cis</i>-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 <i>trans</i>-meQTL mapping.</p> Results <p>We 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. <i>Cis</i>-meQTL effects replicated robustly in external datasets: effect sizes correlated strongly with prior studies (EA Pearson’s <i>r</i> = 0.76; 90.8% concordant directions; AA Pearson’s <i>r</i> = 0.71; 86.6% concordant directions). We built DNAm prediction models with <i>cis</i>-h<sup>2</sup> &gt; 0.01 for 2,677,714 CpG sites in AA and 1,976,046 CpG sites in EA, achieving mean cross-validated prediction R<sup>2</sup> 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&#xa0;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 <i>trans</i>-meQTL mapping detected widespread long-range associations, with limited cross-study overlap but high directional concordance among shared signals.</p> Conclusions <p>This 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.</p>

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An atlas of genetic effects on the monocyte methylome across European and African populations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月28日

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  • Wanheng Zhang,
  • Chuan Qiu,
  • Xiao Zhang,
  • Zichen Zhang,
  • Kuan-Jui Su,
  • Zhe Luo,
  • Minghui Liu,
  • Bingxin Zhao,
  • Lang Wu,
  • Qing Tian,
  • Hui Shen,
  • Chong Wu,
  • Hong-Wen Deng

摘要

Background

Genetic 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.

Methods

We 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.

Results

We 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.

Conclusions

This 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.