Department of Computational Biology, Institut Pasteur, Université de Paris, F-75015, Paris, France.
Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Eur J Hum Genet. 2022 Jun;30(6):730-739. doi: 10.1038/s41431-022-01045-6. Epub 2022 Mar 22.
The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries for three lipids and four blood pressure traits, while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted modest correlation between marginal and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell types. In these analyses, we found multiple instances of potential heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work, we identified potential biases in methods used to jointly meta-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.
基因-环境相互作用在人类特征和疾病中的作用和生物学意义仍未被充分理解。为了解决这些问题,CHARGE 基因-生活方式相互作用工作组进行了一系列全基因组相互作用研究(GWIS),涉及四个祖裔的 610,475 个人,研究了三种脂质和四种血压特征,同时考虑了与饮酒和吸烟暴露的相互作用效应。在这里,我们使用来自这些研究的 GWIS 汇总统计数据,来破译表型-暴露-祖裔组合中潜在的遗传关联和 G×E 相互作用差异,并通过计算功能分析得出关于 G×E 潜在机制的见解。我们的分析表明,首先,相互作用效应可能导致复杂特征中常见的、特定于祖裔的遗传效应,其次,一些表型-暴露对在考虑相互作用时更有可能受益于更高的检测能力。它还强调了边缘效应和相互作用效应之间的适度相关性,为未来的方法发展和生物学讨论提供了材料。我们还估计了表型方差的贡献,包括特别是在暴露条件下的遗传可遗传性,以及在一系列功能注释和细胞类型中划分的遗传可遗传性。在这些分析中,我们发现了暴露和未暴露个体之间功能分区潜在异质性的多个实例,为可能的特定于暴露的遗传途径提供了新的证据。最后,在这项工作中,我们确定了联合进行遗传和相互作用效应荟萃分析的方法中存在潜在偏差。我们进行了模拟,以描述这些局限性,并为未来的 G×E 研究提供社区指南。