sEst: Accurate sex-estimation and abnormality detection in methylation microarray data

Chol Hee Jung, Daniel J. Park, Peter Georgeson, Khalid Mahmood, Roger L. Milne, Melissa C. Southey, Bernard J. Pope

Research output: Contribution to journalArticleResearchpeer-review

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.

Original languageEnglish
Article number3172
Number of pages11
JournalInternational Journal of Molecular Sciences
Volume19
Issue number10
DOIs
Publication statusPublished - 15 Oct 2018

Keywords

  • DNA methylation
  • Epigenetics
  • Sex information
  • Sex-chromosome abnormalities

Cite this

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title = "sEst: Accurate sex-estimation and abnormality detection in methylation microarray data",
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.",
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sEst : Accurate sex-estimation and abnormality detection in methylation microarray data. / Jung, Chol Hee; Park, Daniel J.; Georgeson, Peter; Mahmood, Khalid; Milne, Roger L.; Southey, Melissa C.; Pope, Bernard J.

In: International Journal of Molecular Sciences, Vol. 19, No. 10, 3172, 15.10.2018.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Park, Daniel J.

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AU - Southey, Melissa C.

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