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DNA methylation-based sex classifier to predictsex and identify sex chromosome aneuploidy

Wang, Yucheng and Hannon, Eilis and Grant, Olivia and Gorrie-Stone, Tyler and Kumari, Meena and Mill, Jonathan and Zhai, Xiaojun and McDonald-Maier, Klaus and Schalkwyk, Leonard (2021) 'DNA methylation-based sex classifier to predictsex and identify sex chromosome aneuploidy.' BMC Genomics, 22. ISSN 1471-2164

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Abstract

Background Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. Results Here we presented a novel method to predict sex using only DNA methylation beta values, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only signal intensities) uploaded to GEO. We identified 4345 significantly (p<0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first principal components of the DNA methylation data of sex-associated probes mapped on sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. Conclusions This proposed sex classifier not only can be used for sex predictions but also applied to identify samples with sex chromosome aneuploidy, and it is freely and easily accessible by calling the ‘estimateSex’ function from the newest wateRmelon Bioconductor package (https://github.com/schalkwyk/wateRmelon).

Item Type: Article
Uncontrolled Keywords: DNA methylation, Sex prediction, Aneuploidy
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Science and Health > Life Sciences, School of
Faculty of Social Sciences > Institute for Social and Economic Research
Depositing User: Elements
Date Deposited: 01 Jul 2021 15:49
Last Modified: 01 Jul 2021 15:49
URI: http://repository.essex.ac.uk/id/eprint/30400

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