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.
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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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Zou, J.Y. (2015). Correcting for Sample Heterogeneity in Methylome-Wide Association Studies. In: Haggarty, P., Harrison, K. (eds) Population Epigenetics. Methods in Molecular Biology, vol 1589. Humana Press, New York, NY. https://doi.org/10.1007/7651_2015_266
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DOI: https://doi.org/10.1007/7651_2015_266
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Publisher Name: Humana Press, New York, NY
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