Ritchie Marylyn D, Davis Joe R, Aschard Hugues, Battle Alexis, Conti David, Du Mengmeng, Eskin Eleazar, Fallin M Daniele, Hsu Li, Kraft Peter, Moore Jason H, Pierce Brandon L, Bien Stephanie A, Thomas Duncan C, Wei Peng, Montgomery Stephen B
Am J Epidemiol. 2017 Oct 1;186(7):771-777. doi: 10.1093/aje/kwx229.
A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.
不断增长的遗传和环境信息知识库极大地推动了疾病风险因素的研究。然而，对所有环境下的所有变异进行检测的计算复杂性和统计负担，需要新颖的研究设计和假设驱动的方法。我们讨论了如何整合来自模式生物、功能基因组学和综合方法的生物学知识，以助力发现新的基因-环境相互作用，并讨论了每种方法的具体方法学考量。我们考虑了这些方法的应用揭示与药物反应和免疫相关的基因-环境相互作用效应的具体例子，并强调了这些改进如何有助于更深入地理解疾病的发病机制以及实现精准医学。
