title: Correcting for Sample Heterogeneity in Methylome-Wide Association Studies.
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.
authors.affiliation: School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. jzou@fas.harvard.edu.
authors.forename: James Y
authors.initials: JY
authors.lastname: Zou
mesh_terms: D001172:Arthritis, Rheumatoid / Q000235:genetics*; D016022:Case-Control Studies; D019295:Computational Biology / Q000379:methods*; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods*; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D012984:Software
keywords: Computational method; DNA methylation; Epigenome-wide association study; Sample heterogeneity