MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations.
MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations.
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MIMOSA：一种由改进的甲基化插补模型组成的资源，增强了识别 DNA 甲基化-表型关联的能力。
DOI：
10.1101/2023.03.20.23287418
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Wu,Chong
中科院分区：
文献类型：
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作者：
Melton,HunterJ;Zhang,Zichen;Deng,Hong-Wen;Wu,Lang;Wu,Chong
虽然DNA甲基化与许多复杂疾病的发病机制有关，但在这些过程中发挥关键作用的确切甲基化位点仍然不清楚。识别可能的致病CpG位点和提高疾病病因学理解的一种策略是进行甲基化全关联研究(MWASs)，在MWASs中可以识别与复杂疾病相关的预测DNA甲基化。然而，目前的MWAS模型主要是使用单个研究的数据来训练的，从而限制了甲基化预测的准确性和后续关联研究的能力。在这里，我们介绍了一种新的资源，MWAS归因于遵守摘要水平的mQTL和相关的LD矩阵(Mimosa)，这是一组模型，通过使用DNA甲基化遗传学联合会(GoDMC)提供的大型摘要水平的mQTL数据集，大大提高了DNA甲基化和后续的MWAS能力的预测精度。通过对28个复杂性状和疾病的GWAS(基因组范围关联研究)汇总统计分析，我们发现，Mimosa显著提高了全血DNA甲基化预测的准确性，为低遗传率的CpG位点建立了卓有成效的预测模型，并比以前的方法确定了更多的CpG位点-表型关联。最后，我们使用Mimosa进行了一个高胆固醇的案例研究，准确地定位了146个推测导致CpG的位置。
Although DNA methylation has been implicated in the pathogenesis of numerous complex diseases, 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 by 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). With 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 in high cholesterol, pinpointing 146 putatively causal CpG sites.