bioRxivGeneticsPreprint
Age and mortality associated DNA methylation patterns on the X-chromosome in male and female samples
Shuxia LiiD, Jesper LundiD, Jan BaumbachiD, Kaare ChristenseniD, Jonas Mengel‐FromiD, Weilong LiiD, Afsaneh MohammadnejadiD, Alison Pattie, Riccardo Emilio MarioniiD, Ian J. DearyiD, Qihua TaniD
Abstract
BackgroundMultiple epigenetic association studies on human aging have been performed reporting large numbers of sites differentially methylated across ages on the autosomal chromosomes. The X-chromosome has been studied little, due to analytical difficulties in dealing with sex differences in X-chromosome content and X-inactivation in females. Based on large collections of genome-wide DNA methylation data on two Danish cohorts of identical twins (mean ages, 66 and 79 years) and the Lothian Birth Cohort 1921 (mean age 79 years), we conducted a chromosome-wide association analysis on male and female samples separately with equal sample sizes to discover age-dependent X-linked DNA methylation patterns using chromosome 20 with about same number of CpGs analysed as an autosomal reference, and compare the age-related changes in DNA methylation between the two sexes. In addition, age-related methylation sites were assessed for their associations with mortality.\n\nResultsWe identified more age-related DNA methylation sites (FDR<0.05) in females than in males. Among them, predominantly more sites were hypermethylated in the older as compared with the younger cohorts, a pattern similar to that observed on chromosome 20. Among the age-related sites, 13 CpGs in males and 24 CpGs in females were found significant (FDR<0.05) in all cohorts. Survival analysis showed that there are more age-methylated CpGs that contribute to reduce mortality than those that increase mortality in male but not in female samples.\n\nConclusionThe X-chromosome displays significant age-and sex-dependent methylation patterns which might be differentially associated with mortality in the two sexes.
- organism: human
- assay: DNA methylation
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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