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.

The authors have declared no competing interest.

This study was partially funded by National Institutes of Health (R03 AG070669).

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study is a secondary analysis. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

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The mQTL summary data are available at http://mqtldb.godmc.org.uk/downloads. The datasets of FHS Offspring Cohort are publicly available via dbGaP (www.ncbi.nlm.nih.gov/gap): dbGaP Study Accession: phs000342 and phs000724. The UK Biobank is an open-access resource but requires registration, available at https://www.ukbiobank.ac.uk/researchers/. The Baselmans models can be downloaded from http://bbmri.researchlumc.nl/atlas/#data. The 1000 Genomes Project data can be downloaded from https://www.internationalgenome.org/data. The genetic distance data for 1000 Genomes Project can be downloaded from https://github.com/joepickrell/1000-genomes-genetic-maps. The MIMOSA models are available at OSF.IO https://osf.io/4swdq/?view_only=1e824972813a40e78d5af25f4b2c7154. Real data results are available at https://chongwulab.shinyapps.io/SUMMIT-app/, where they can be easily downloaded.

https://osf.io/4swdq/?view_only=1e824972813a40e78d5af25f4b2c7154

https://chongwulab.shinyapps.io/SUMMIT-app/