MIMOSA: A resource consisting of improved methylome imputation models increases power to identify DNA methylation-phenotype associations

Abstract

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.