EWASex: An efficient R-package to predict sex in epigenome-wide association studies

Jesper Lund, Weilong Li, Afsaneh Mohammadnejad, Shuxia Li, Jan Baumbach, Qihua Tan*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

SUMMARY: Epigenome-Wide Association Study (EWAS) has become a powerful approach to identify epigenetic variations associated with diseases or health traits. Sex is an important variable to include in EWAS to ensure unbiased data processing and statistical analysis. We introduce the R-package EWASex, which allows for fast and highly accurate sex-estimation using DNA methylation data on a small set of CpG sites located on the X-chromosome under stable X-chromosome inactivation in females.

RESULTS: We demonstrate that EWASex outperforms the current state of the art tools by using different EWAS datasets. With EWASex, we offer an efficient way to predict and to verify sex that can be easily implemented in any EWAS using blood samples or even other tissue types. It comes with pre-trained weights to work without prior sex labels and without requiring access to RAW data, which is a necessity for all currently available methods.

AVAILABILITY AND IMPLEMENTATION: The EWASex R-package along with tutorials, documentation and source code are available at https://github.com/Silver-Hawk/EWASex.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Original languageEnglish
JournalBioinformatics
Volume37
Issue number14
Pages (from-to)2075-2076
ISSN1367-4803
DOIs
Publication statusPublished - 15. Jul 2021

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