Abstract

The X chromosome is often excluded from studies analyzing associations between traits and DNA methylation. In females, one copy of most genes on the X is inactivated (X chromosome inactivation; XCI) through DNA methylation of the gene promoter on the inactive X. This leads to challenges in analyzing and interpreting DNA methylation data patterns. Particularly for sex-biased diseases and traits, there may be many loci of interest on the X chromosome, which contains about 5% of the genome. To address the need for appropriate analysis of DNA methylation data on the X chromosome, we develop a statistical approach to infer locus-specific escape from XCI sensitive to phenotype or covariate values. Performance of this method is illustrated by analysis of data from two sex biased traits: rheumatoid arthritis which is 3-fold more common in females, and recurrent venous thromboembolism which occurs 2.5 times more often in males. Analyses of these two datasets identify new trait-associated loci on the X chromosome, demonstrate the capabilities of the new method for both bisulfite sequencing data and Illumina EPIC data, suggest at least one locus where variable escape may explain a sex-specific disease association, and rule out variable escape as a potential explanation at other loci.