Accurate estimation of SNP genotypes and genetic relatedness from DNA methylation data

Abstract

Epigenome-wide association studies (EWAS) are susceptible to widespread confounding caused by population structure and genetic relatedness. Nevertheless, kinship estimation is challenging in EWAS without genotyping data. We propose MethylGenotyper, a method that for the first time enables accurate genotyping at thousands of SNPs directly from commercial DNA methylation microarrays. We model the intensities of methylation probes near SNPs with a mixture of three beta distributions corresponding to different genotypes and estimate parameters with an expectation-maximization algorithm. We conduct extensive simulations to demonstrate the performance of the method. When applying MethylGenotyper to Infinium EPIC array data of 4,662 Chinese, we obtain genotypes at 4,319 SNPs with a concordance rate of 98.26%, enabling the identification of 255 pairs of close relatedness. Furthermore, we show that MethylGenotyper allows for the estimation of both population structure and cryptic relatedness among 702 Australians of diverse ancestry. We have implemented MethylGenotyper in a publicly available R package to facilitate future large-scale EWAS.