Interest in the study of gene–environment interaction has recently grown due to the sudden availability of molecular genetic data—in particular, polygenic scores—in many long-running longitudinal studies. Identifying and estimating statistical interactions comes with several analytic and inferential challenges; these challenges are heightened when used to integrate observational genomic and social science data. We articulate some of these key challenges, provide new perspectives on the study of gene–environment interactions, and end by offering some practical guidance for conducting research in this area. Given the sudden availability of well-powered polygenic scores, we anticipate a substantial increase in research testing for interaction between such scores and environments. The issues we discuss, if not properly addressed, may impact the enduring scientific value of gene–environment interaction studies.

中文翻译：

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广义马尔可夫量分配系统：社会科学应用

最近，由于许多长期开展的纵向研究中，分子遗传数据（尤其是多基因得分）的突然获得，对基因与环境相互作用的研究兴趣日益浓厚。识别和估计统计相互作用会带来一些分析和推论上的挑战。当用于整合观察基因组和社会科学数据时，这些挑战变得更加严峻。我们阐明了其中的一些关键挑战，为研究基因与环境之间的相互作用提供了新的视角，最后为进行该领域的研究提供了一些实用的指导。鉴于功能强大的多基因评分的突然出现，我们预计有关此类评分与环境之间相互作用的研究测试将大幅增加。我们讨论的问题，如果未正确解决，