A study of the influence of sex on genome wide methylation - PubMed (original) (raw)

A study of the influence of sex on genome wide methylation

Jingyu Liu et al. PLoS One. 2010.

Abstract

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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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1

Figure 1. Significant sex effects on 690 methylation sites with mean and standard deviation values.

a): mean and standard deviation methylation values from 12 autosomal sites. Red indicates females and blue indicates males. Bars present mean value, while lines show standard deviation. Eight sites are more methylated in females than males and four sites are more methylated in males than females. b): methylation pattern of 678 sites in X chromosome. They are sorted by female methylation level, presenting 614 sites with higher methylation in females and 64 sites with higher methylation in males. Solid squares show mean value while dash lines show standard deviation.

Figure 2

Figure 2. Autosomal sites identified as sex differentially methylated.

Human chromosomes 1-22 are arranged vertically. Methylation sites in each chromosome are plotted horizontally in blue line. Red dots present sites showing sex difference based 5% uncorrected false positive rate. The ones above blue lines are sites where females are more methylated than males. The ones below blue lines are sites where males are more methylated than females. a): result from saliva methylation data. 307 sites are methylated more in females than in males, while 273 sites are methylated more in males. b): results from blood cell verification data. 21 sites are methylated more in females than in males, while 15 sites are methylated more in males.

Figure 3

Figure 3. Weights of the sex-related factor expressed in subjects.

Weights in 143 males are plotted on the left with two subjects above zeros; weights in 54 females are plotted in the right with four subjects below zero.

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