PDF(Pubmed)
Abstract：
Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.
摘要：
尽管DNA甲基化（DNAm）与许多复杂疾病的发病机理有关，从癌症到心血管疾病到自身免疫性疾病，在这些过程中起关键作用的确切甲基化位点仍然难以捉摸。确定推定的CpG位点和增强疾病病因理解的一种策略是进行全甲基化关联研究(MWAS)。其中可以确定与复杂疾病相关的预测DNA甲基化。然而,当前的MWAS模型主要使用来自单个研究的数据进行训练，从而限制了甲基化预测的准确性和后续关联研究的能力。这里,我们引入了一种新的资源，MWAS估算甲基化组支持摘要级mQTL和相关LD矩阵(MIMOSA)，一组模型，通过使用DNA甲基化遗传学联盟（GoDMC）提供的大型汇总级mQTL数据集，大大提高了DNA甲基化的预测准确性和随后的MWAS能力。通过对28种复杂性状和疾病的GWAS（全基因组关联研究）汇总统计的分析，我们证明MIMOSA大大提高了全血中DNA甲基化预测的准确性，为低遗传力CpG位点制作了卓有成效的预测模型，并确定比先前方法明显更多的CpG位点-表型关联。最后,我们使用MIMOSA进行高胆固醇的案例研究，精确定位146个推定因果CpG位点。
