Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
Am J Epidemiol. 2012 Feb 1;175(3):203-7; discussion 208-9. doi: 10.1093/aje/kwr365. Epub 2011 Dec 22.
One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.
在后全基因组关联研究时代,一个目标是描述基因-环境相互作用,包括扫描所有可用的多态性,而不仅仅是那些表现出显著主效应的多态性。近年来,已经提出了几种此类“基因-环境广泛相互作用研究”的方法。本期《美国流行病学杂志》中的两篇文章对这些不同方法的性能进行了系统比较,一篇基于模拟,另一篇基于对 2 型糖尿病的全基因组关联研究扫描的应用。作者讨论了这些贡献提出的一些更广泛的问题,包括这些方法所依赖的基因-环境独立性假设的合理性、复制的必要性以及这些方法的各种推广。