PIGEON: a statistical framework for estimating gene–environment interaction for polygenic traits

Jiacheng Miao, Gefei Song, Yixuan Wu, Jiaxin Hu, Yuchang Wu, Shubhashrita Basu, James S. Andrews, Katherine Schaumberg, Jason M. Fletcher, Lauren L. Schmitz and Qiongshi Lu ()
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Jiacheng Miao: University of Wisconsin–Madison
Gefei Song: University of Wisconsin–Madison
Yixuan Wu: University of Wisconsin–Madison
Jiaxin Hu: University of Wisconsin–Madison
Yuchang Wu: University of Wisconsin–Madison
Shubhashrita Basu: Southern Utah University
James S. Andrews: University of Alabama
Katherine Schaumberg: University of Wisconsin–Madison
Jason M. Fletcher: University of Wisconsin–Madison
Lauren L. Schmitz: University of Wisconsin–Madison
Qiongshi Lu: University of Wisconsin–Madison

Nature Human Behaviour, 2025, vol. 9, issue 8, 1654-1668

Abstract: Abstract Understanding gene–environment interaction (GxE) is crucial for deciphering the genetic architecture of human complex traits. However, current statistical methods for GxE inference face challenges in both scalability and interpretability. Here we introduce PIGEON—a unified statistical framework for quantifying polygenic GxE using a variance component analytical approach. Based on this framework, we outline the main objectives in GxE studies and introduce an estimation procedure that requires only summary statistics data as input. We demonstrate the effectiveness of PIGEON through theoretical and empirical analyses, including a quasi-experimental gene-by-education study of health outcomes and gene-by-sex interaction for 530 traits using UK Biobank. We also identify genetic interactors that explain the treatment effect heterogeneity in a clinical trial on smoking cessation. PIGEON suggests a path towards polygenic, summary statistics-based inference in future GxE studies.

Date: 2025
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DOI: 10.1038/s41562-025-02202-9

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