Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Qc, Canada.
Department of Human Ecology, University of California, Davis, USA.
Dev Psychopathol. 2020 Feb;32(1):73-83. doi: 10.1017/S0954579418001438.
Currently, two main approaches exist to distinguish differential susceptibility from diathesis-stress and vantage sensitivity in Genotype × Environment interaction (G × E) research: regions of significance (RoS) and competitive-confirmatory approaches. Each is limited by its single-gene/single-environment foci given that most phenotypes are the product of multiple interacting genetic and environmental factors. We thus addressed these two concerns in a recently developed R package (LEGIT) for constructing G × E interaction models with latent genetic and environmental scores using alternating optimization. Herein we test, by means of computer simulation, diverse G × E models in the context of both single and multiple genes and environments. Results indicate that the RoS and competitive-confirmatory approaches were highly accurate when the sample size was large, whereas the latter performed better in small samples and for small effect sizes. The competitive-confirmatory approach generally had good accuracy (a) when effect size was moderate and N ≥ 500 and (b) when effect size was large and N ≥ 250, whereas RoS performed poorly. Computational tools to determine the type of G × E of multiple genes and environments are provided as extensions in our LEGIT R package.
目前，区分基因-环境交互作用（G×E）研究中的差异易感性、素质-压力和优势敏感性有两种主要方法：显著区域（RoS）和竞争确认方法。由于大多数表型是多个相互作用的遗传和环境因素的产物，因此这两种方法都受到其单基因/单环境焦点的限制。我们最近使用交替优化开发了一个 R 包（LEGIT），用于构建具有潜在遗传和环境分数的 G×E 交互模型，从而解决了这两个问题。在此，我们通过计算机模拟测试了单基因和多基因、多环境条件下的多种 G×E 模型。结果表明，在样本量较大时，RoS 和竞争确认方法具有较高的准确性，而后者在小样本和小效应量下表现更好。竞争确认方法在效应量适中且 N≥500 时，或在效应量较大且 N≥250 时，通常具有较好的准确性，而 RoS 表现不佳。我们提供了 LEGIT R 包的扩展，用于确定多个基因和环境的 G×E 类型的计算工具。
