Wanheng Zhang, Xiao Zhang, Chuan Qiu, Zichen Zhang, Kuan-Jui Su, Zhe Luo, Minghui Liu, Bingxin Zhao, Lang Wu, Qing Tian, Hui Shen, Chong Wu, Hong-Wen Deng
{"title":"欧洲和非洲人群单核细胞甲基组遗传效应图谱。","authors":"Wanheng Zhang, Xiao Zhang, Chuan Qiu, Zichen Zhang, Kuan-Jui Su, Zhe Luo, Minghui Liu, Bingxin Zhao, Lang Wu, Qing Tian, Hui Shen, Chong Wu, Hong-Wen Deng","doi":"10.1101/2024.08.12.24311885","DOIUrl":null,"url":null,"abstract":"<p><p>Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in <i>cis-</i> regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .</p>","PeriodicalId":94281,"journal":{"name":"medRxiv : the preprint server for health sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11361221/pdf/","citationCount":"0","resultStr":"{\"title\":\"An atlas of genetic effects on the monocyte methylome across European and African populations.\",\"authors\":\"Wanheng Zhang, Xiao Zhang, Chuan Qiu, Zichen Zhang, Kuan-Jui Su, Zhe Luo, Minghui Liu, Bingxin Zhao, Lang Wu, Qing Tian, Hui Shen, Chong Wu, Hong-Wen Deng\",\"doi\":\"10.1101/2024.08.12.24311885\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in <i>cis-</i> regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .</p>\",\"PeriodicalId\":94281,\"journal\":{\"name\":\"medRxiv : the preprint server for health sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11361221/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"medRxiv : the preprint server for health sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1101/2024.08.12.24311885\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv : the preprint server for health sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.08.12.24311885","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
阐明DNA甲基化(DNAm)的遗传结构对于破解复杂疾病的病因至关重要。然而,目前的表观基因组研究往往存在甲基化位点覆盖不全和使用含有异质性细胞群的组织的问题。为了应对这些挑战,我们在路易斯安那骨质疏松症研究(Louisiana Osteoporosis Study)中对来自 298 名欧洲裔美国人(EA)和 160 名非洲裔美国人(AA)的纯化单核细胞进行了深度全基因组亚硫酸氢盐测序(WGBS)和全基因组测序(WGS),在此基础上绘制了全面的人类甲基组图集。我们的图谱能够分析超过 2,500 万个 DNAm 位点。我们在EA和AA人群的顺式区域分别发现了1,383,250和1,721,167个甲基化定量性状位点(meQTLs),其中有880,108个位点在不同血统之间共享。主要由于小等位基因频率的差异,顺式甲基化定量性状位点呈现出特定人群的模式,而共享的顺式甲基化定量性状位点在不同祖先之间表现出高度的一致性。值得注意的是,顺式遗传率估计值显示 AA 群体(0.09)的平均值明显高于 EA 群体(0.04)。此外,我们还利用 Elastic Net 开发了特定人群的 DNAm 估算模型,分别对 EA 和 AA 中的 1,976,046 和 2,657,581 个甲基化位点进行了全甲基关联研究(MWAS)。通过对 "百万退伍军人计划 "中的 41 个复杂性状进行系统的多家系分析,我们的 MWAS 模型的性能得到了验证。我们的研究结果弥补了基因组学与单核细胞甲基组之间的差距,发现了新的甲基化与表型的关联及其在不同血统中的可转移性。已确定的 meQTLs、MWAS 模型和数据资源可在 www.gcbhub.org 和 https://osf.io/gct57/ 免费获取。
An atlas of genetic effects on the monocyte methylome across European and African populations.
Elucidating the genetic architecture of DNA methylation is crucial for decoding complex disease etiology. However, current epigenomic studies are often limited by incomplete methylation coverage and heterogeneous tissue samples. Here, we present the first comprehensive, multi-ancestry human methylome atlas of purified human monocytes, generated through integrated whole-genome bisulfite sequencing and whole-genome sequencing from 298 European Americans (EA) and 160 African Americans (AA). By analyzing over 25 million methylation sites, we identified 1,383,250 and 1,721,167 methylation quantitative trait loci (meQTLs) in cis- regions for EA and AA populations, respectively, revealing both shared (880,108 sites) and population-specific regulatory patterns. Furthermore, we developed population-specific DNAm imputation models, enabling methylome-wide association studies (MWAS) for 1,976,046 and 2,657,581 methylation sites in EA and AA, respectively. These models were validated through multi-ancestry analysis of 41 complex traits from the Million Veteran Program. The identified meQTLs, MWAS models, and data resources are freely available at www.gcbhub.org and https://osf.io/gct57/ .