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Int. J. Mol. Sci. 2018, 19(10), 3172; https://doi.org/10.3390/ijms19103172

sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data

1
Melbourne Bioinformatics, The University of Melbourne, Parkville, VIC 3010, Australia
2
Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3010, Australia
3
Cancer Epidemiology & Intelligence Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia
4
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC, 3010, Australia
5
Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
6
Genetic Epidemiology Laboratory, The University of Melbourne, Parkville, VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Received: 20 September 2018 / Revised: 8 October 2018 / Accepted: 9 October 2018 / Published: 15 October 2018
(This article belongs to the Section Molecular Genetics and Genomics)
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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. View Full-Text
Keywords: DNA methylation; sex information; sex-chromosome abnormalities; epigenetics DNA methylation; sex information; sex-chromosome abnormalities; epigenetics
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Jung, C.-H.; Park, D.J.; Georgeson, P.; Mahmood, K.; Milne, R.L.; Southey, M.C.; Pope, B.J. sEst: Accurate Sex-Estimation and Abnormality Detection in Methylation Microarray Data. Int. J. Mol. Sci. 2018, 19, 3172.

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