Medicine, University of Southern California, 1540 Alcazar Street, CHP‑220, Los Angeles, California 90089‑9011, USA.
Nat Rev Genet. 2010 Apr;11(4):259-72. doi: 10.1038/nrg2764.
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.
尽管最近的全基因组关联 (GWA) 研究取得了一定的成果,但已鉴定的变异仅能解释大多数复杂疾病遗传率的一小部分。这种无法解释的遗传率可能部分归因于基因-环境 (G×E) 相互作用,或涉及多个基因和暴露因素的更复杂途径。本文综述了用于研究特定 G×E 相互作用和选择最合适方法的现有流行病学设计和统计分析方法。我讨论了正在开发用于研究整个途径的方法以及挖掘 GWA 数据中相互作用的可用技术。我还探讨了将基于假设的途径方法与“非特异性”GWA 研究相结合的方法。