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Correcting for Sample Heterogeneity in Methylome-Wide Association Studies

  • James Y. ZouEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1589)

Abstract

Epigenome-wide association studies (EWAS) face many of the same challenges as genome-wide association studies (GWAS), but have an added challenge in that the epigenome can vary dramatically across cell types. When cell-type composition differs between cases and controls, this leads to spurious associations that may obscure true associations. We have developed a computational method, FaST-LMM-EWASher, which automatically corrects for cell-type composition without needing explicit knowledge of it. In this chapter, we provide a tutorial on using FaST-LMM-EWASher for DNA methylation data and discuss data analysis strategies.

Keywords:

DNA methylation Epigenome-wide association study Computational method Sample heterogeneity 

Notes

Acknowledgements

FaST-LMM-EWASher was developed in collaboration with Jennifer Listgarten, Martin Aryee, and the Microsoft Research Los Angeles group. We would also like to thank Yvonne Yamanaka for helpful feedback.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA

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