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

DNA methylation has been shown to be involved in the etiology of many complex diseases, yet the specific key underlying methylation sites remain largely unknown. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted or measured DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are trained with relatively small reference datasets, limiting the ability to adequately handle CpG sites with low genetic heritability. 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 summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods.