University Center for Primary Care and Public Health, University of Lausanne, Lausanne, 1010, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
Nat Commun. 2020 Mar 13;11(1):1385. doi: 10.1038/s41467-020-15107-0.
The growing sample size of genome-wide association studies has facilitated the discovery of gene-environment interactions (GxE). Here we propose a maximum likelihood method to estimate the contribution of GxE to continuous traits taking into account all interacting environmental variables, without the need to measure any. Extensive simulations demonstrate that our method provides unbiased interaction estimates and excellent coverage. We also offer strategies to distinguish specific GxE from general scale effects. Applying our method to 32 traits in the UK Biobank reveals that while the genetic risk score (GRS) of 376 variants explains 5.2% of body mass index (BMI) variance, GRSxE explains an additional 1.9%. Nevertheless, this interaction holds for any variable with identical correlation to BMI as the GRS, hence may not be GRS-specific. Still, we observe that the global contribution of specific GRSxE to complex traits is substantial for nine obesity-related measures (including leg impedance and trunk fat-free mass).
全基因组关联研究的样本量不断增加，促进了基因-环境相互作用（GxE）的发现。在这里，我们提出了一种最大似然方法，用于估计考虑到所有相互作用的环境变量但无需测量任何环境变量的连续性状的 GxE 贡献。广泛的模拟表明，我们的方法提供了无偏的交互估计值和优异的覆盖范围。我们还提供了区分特定 GxE 与一般规模效应的策略。将我们的方法应用于英国生物库中的 32 个特征，发现 376 个变体的遗传风险评分（GRS）解释了 5.2%的体重指数（BMI）变异，而 GRSxE 则额外解释了 1.9%。然而，这种相互作用适用于与 GRS 具有相同与 BMI 相关性的任何变量，因此可能不是 GRS 特异性的。尽管如此，我们观察到，特定 GRSxE 对复杂特征的总体贡献对于九个与肥胖相关的指标（包括腿部阻抗和躯干无脂肪质量）是相当大的。
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