Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK.
Nat Genet. 2019 Jan;51(1):180-186. doi: 10.1038/s41588-018-0271-0. Epub 2018 Nov 26.
Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
不同的暴露因素，包括饮食、身体活动或外部条件，都可能导致基因型-环境相互作用（G×E）。尽管高维环境数据越来越多，并且多个暴露因素已被牵连到同一基因座的 G×E 中，但 G×E 的多环境测试尚未建立。在这里，我们提出了结构线性混合模型（StructLMM），这是一种计算效率高的方法，可以识别和描述与一个或多个环境相互作用的基因座。在使用模拟验证了我们的模型之后，我们将 StructLMM 应用于英国生物库中的体重指数，我们的模型产生了先前已知和新的 G×E 信号。最后，在对大型血液 eQTL 数据集的应用中，我们证明了 StructLMM 可用于研究与数百个环境变量的相互作用。
