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PLoS One. 2010 Apr 6;5(4):e10028. doi: 10.1371/journal.pone.0010028.

A study of the influence of sex on genome wide methylation.

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  • 1The Mind Research Network, Albuquerque, New Mexico, United States of America. jliu@mrn.org


Sex differences in methylation status have been observed in specific gene-disease studies and healthy methylation variation studies, but little work has been done to study the impact of sex on methylation at the genome wide locus-to-locus level or to determine methods for accounting for sex in genomic association studies. In this study we investigate the genomic sex effect on saliva DNA methylation of 197 subjects (54 females) using 20,493 CpG sites. Three methods, two-sample T-test, principle component analysis and independent component analysis, all successfully identify sex influences. The results show that sex not only influences the methylation of genes in the X chromosome but also in autosomes. 580 autosomal sites show strong differences between males and females. They are found to be highly involved in eight functional groups, including DNA transcription, RNA splicing, membrane, etc. Equally important is that we identify some methylation sites associated with not only sex, but also other phenotypes (age, smoking and drinking level, and cancer). Verification was done through an independent blood cell DNA methylation data (1298 CpG sites from a cancer panel array). The same genomic site-specific influence pattern and potential confounding effects with cancer were observed. The overlapping rate of identified sex affected genes between saliva and blood cell is 81% for X chromosome, and 8% for autosomes. Therefore, correction for sex is necessary. We propose a simple correction method based on independent component analysis, which is a data driven method and accommodates sample differences. Comparison before and after the correction suggests that the method is able to effectively remove the potentially confounding effects of sex, and leave other phenotypes untouched. As such, our method is able to disentangle the sex influence on a genome wide level, and paves the way to achieve more accurate association analyses in genome wide methylation studies.

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