title: How to make DNA methylome wide association studies more powerful.
abstract: Genome-wide association studies had a troublesome adolescence, while researchers increased statistical power, in part by increasing subject numbers. Interrogating the interaction of genetic and environmental influences raised new challenges of statistical power, which were not easily bested by the addition of subjects. Screening the DNA methylome offers an attractive alternative as methylation can be thought of as a proxy for the combined influences of genetics and environment. There are statistical challenges unique to DNA methylome data and also multiple features, which can be exploited to increase power. We anticipate the development of DNA methylome association study designs and new analytical methods, together with integration of data from other molecular species and other studies, which will boost statistical power and tackle causality. In this way, the molecular trajectories that underlie disease development will be uncovered.
authors.affiliation: Singapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore.
authors.forename: Xinyi
authors.initials: X
authors.lastname: Lin
authors.affiliation: MRC Lifecourse Epidemiology Unit, Faculty of Medicine, University of Southampton, Southampton, SO16 6YD, UK.
authors.forename: Sheila
authors.initials: S
authors.lastname: Barton
authors.affiliation: Singapore Institute for Clinical Sciences (SICS), Agency for Science & Technology Research (A*STAR), Brenner Centre for Molecular Medicine, 30 Medical Drive, 117609, Singapore.
authors.forename: Joanna D
authors.initials: JD
authors.lastname: Holbrook
mesh_terms: D000818:Animals; D019175:DNA Methylation; D057890:Epigenomics / Q000379:methods* / Q000592:standards; D059647:Gene-Environment Interaction; D055106:Genome-Wide Association Study / Q000379:methods* / Q000592:standards; D006801:Humans
keywords: DNA methylation; DNA methylation association studies (methWAS); developmental trajectories; epigenetic epidemiology; epigenome-wide association study (EWAS); gene × environment (GxE)