统计推断
推论
方差分量
计算机科学
统计
差异(会计)
计量经济学
基因-环境相互作用
统计假设检验
人类健康
估计
计算生物学
生物
基因
数学
人工智能
遗传学
医学
工程类
环境卫生
会计
基因型
业务
系统工程
作者
Jiacheng Miao,Gefei Song,Yixuan Wu,Jiaxin Hu,Yuchang Wu,Shubhashrita Basu,James S. Andrews,Katherine Schaumberg,Jason M. Fletcher,Lauren Schmitz,Qiongshi Lu
标识
DOI:10.1101/2022.12.11.519973
摘要
Abstract In this study, we introduce PIGEON—a novel statistical framework for quantifying and estimating polygenic gene-environment interaction (GxE) using a variance component analytical approach. Based on PIGEON, we outline the main objectives in GxE studies, demonstrate the flaws in existing GxE approaches, and introduce an innovative estimation procedure which only requires summary statistics as input. We demonstrate the statistical superiority of PIGEON through extensive theoretical and empirical analyses and showcase its performance in multiple analytic settings, including a quasi-experimental GxE study of health outcomes, gene-by-sex interaction for 530 traits, and gene-by-treatment interaction in a randomized clinical trial. Our results show that PIGEON provides an innovative solution to many long-standing challenges in GxE inference and may fundamentally reshape analytical strategies in future GxE studies.
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