Department of Medicine, Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA; Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA.
Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
Am J Hum Genet. 2024 Apr 4;111(4):626-635. doi: 10.1016/j.ajhg.2024.03.002.
Despite the importance of gene-environment interactions (GxEs) in improving and operationalizing genetic discovery, interpretation of any GxEs that are discovered can be surprisingly difficult. There are many potential biological and statistical explanations for a statistically significant finding and, likewise, it is not always clear what can be claimed based on a null result. A better understanding of the possible underlying mechanisms leading to a detected GxE can help investigators decide which are and which are not relevant to their hypothesis. Here, we provide a detailed explanation of five "phenomena," or data-generating mechanisms, that can lead to nonzero interaction estimates, as well as a discussion of specific instances in which they might be relevant. We hope that, given this framework, investigators can design more targeted experiments and provide cleaner interpretations of the associated results.
尽管基因-环境相互作用(GxEs)对于改善和实现遗传发现非常重要,但对任何发现的 GxEs 的解释都可能非常困难。对于具有统计学意义的发现,有许多潜在的生物学和统计学解释,同样,基于零结果也不一定清楚可以声称什么。更好地了解导致检测到的 GxE 的可能潜在机制可以帮助研究人员确定哪些与他们的假设相关,哪些不相关。在这里,我们详细解释了可能导致非零交互估计的五个“现象”或数据生成机制,并讨论了它们在哪些情况下可能相关的具体实例。我们希望,有了这个框架,研究人员可以设计更有针对性的实验,并对相关结果提供更清晰的解释。