Quantitative Life Sciences Program, McGill University, Montréal, Canada.
Montreal Heart Institute, Montréal, Canada.
Nat Commun. 2024 Oct 30;15(1):9374. doi: 10.1038/s41467-024-53818-w.
Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity but has a high multiple testing burden in the context of genome-wide association studies (GWAS). We adapt a colocalization method, SharePro, to account for effect heterogeneity in fine-mapping and identify candidates for GxE analysis with reduced multiple testing burden. SharePro demonstrates improved power for both fine-mapping and GxE analysis compared to existing methods as well as well-controlled false type I error in simulations. Using smoking status stratified GWAS summary statistics, we identify genetic effects on lung function modulated by smoking status that are not identified by existing methods. Additionally, using sex stratified GWAS summary statistics, we characterize sex differentiated genetic effects on fat distribution. In summary, we have developed an analytical framework to account for effect heterogeneity in fine-mapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe .
经典的基因-环境交互作用（GxE）分析可用于描述遗传效应异质性，但在全基因组关联研究（GWAS）中具有较高的多重检验负担。我们采用一种共定位方法 SharePro，在精细映射中考虑效应异质性，并确定具有降低多重检验负担的 GxE 分析候选者。与现有方法相比，SharePro 显示出在精细映射和 GxE 分析方面都具有更高的功效，并且在模拟中具有良好控制的假阳性 I 型错误。使用吸烟状态分层的 GWAS 汇总统计数据，我们确定了由吸烟状态调节的肺功能遗传效应，这些效应是现有方法无法识别的。此外，使用性别分层的 GWAS 汇总统计数据，我们描述了脂肪分布上性别差异的遗传效应。总之，我们开发了一种分析框架，用于在精细映射中考虑效应异质性，并随后提高 GxE 分析的功效。用于 GxE 分析的 SharePro 软件可在 https://github.com/zhwm/SharePro_gxe 上公开获取。
