Reimagining Gene-Environment Interaction Analysis for Human Complex Traits
Miao, J.; Song, G.; Wu, Y.; Hu, J.; Wu, Y.; Basu, S.; Andrews, J. S.; Schaumberg, K.; Fletcher, J. M.; Schmitz, L. L.; Lu, Q.
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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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