Gene--environment-wide association studies: emerging approaches.
Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.
Thomas
Duncan
D
Medicine, University of Southern California, 1540 Alcazar Street, CHP‑220, Los Angeles, California 90089‑9011, USA. dthomas@usc.edu
D057225:Data Mining; D004777:Environment; D044127:Epigenesis, Genetic; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data / Q000639:trends; D006801:Humans; D008957:Models, Genetic; D012307:Risk Factors