Abstract
DNA methylation influences predisposition, development and prognosis for many diseases, including cancer. However, it is not uncommon to encounter samples with incorrect sex labelling or atypical sex chromosome arrangement. Sex is one of the strongest influencers of the genomic distribution of DNA methylation and, therefore, correct assignment of sex and filtering of abnormal samples are essential for the quality control of study data. Differences in sex chromosome copy numbers between sexes and X-chromosome inactivation in females result in distinctive sex-specific patterns in the distribution of DNA methylation levels. In this study, we present a software tool, sEst, which incorporates clustering analysis to infer sex and to detect sex-chromosome abnormalities from DNA methylation microarray data. Testing with two publicly available datasets demonstrated that sEst not only correctly inferred the sex of the test samples, but also identified mislabelled samples and samples with potential sex-chromosome abnormalities, such as Klinefelter syndrome and Turner syndrome, the latter being a feature not offered by existing methods. Considering that sex and the sex-chromosome abnormalities can have large effects on many phenotypes, including diseases, our method can make a significant contribution to DNA methylation studies that are based on microarray platforms.
Highlights
DNA methylation is a major mechanism in the regulation of gene expression, and, plays an important role in disease susceptibility and progression [1,2]
Illumina’s BeadChip-based platforms for DNA methylation profiling have been available for several years, and the ‘getSex’ method of sex-estimation has been available for years within the package minfi [7]
Given that the ‘rnb.execute.gender.prediction’ uses signal intensity for sex-prediction, sEst holds the same advantages for sex-prediction over this package as it does over minfi
Summary
DNA methylation is a major mechanism in the regulation of gene expression, and, plays an important role in disease susceptibility and progression [1,2]. The ‘rnb.execute.gender.prediction’ function of RnBeads uses the increase in total signal intensities on sex-chromosomes compared to autosomes to infer sex [8]. Klinefelter syndrome is an underdiagnosed condition [9], implying that it is not uncommon to inadvertently include Klinefelter syndrome individuals in studies featuring several hundred or more male samples. These abnormal sex-chromosome states should be properly accounted for in the analyses, since they confer increased risk for various diseases [11,12,13,14]
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