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.
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Data availability
This study made use of publicly available datasets. This research has been conducted using the UK Biobank Resource under application number 42148. Data from the UK Biobank are available by application to all bona fide researchers in the public interest at https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access. Data from the Lung Health studies are available by application at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000291.v2.p1. The SNPxE summary statistics used in the Article are available at http://qlu-lab.org/data.html.
Code availability
PIGEON software package is publicly available via GitHub at https://github.com/qlu-lab/PIGEON.
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Acknowledgements
We acknowledge research support from National Institutes of Health (NIH) grant U01 HG012039, the National Institute on Aging (NIA) (R00 AG056599) and the University of Wisconsin-Madison Office of the Chancellor and the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation (WARF). We also acknowledge use of the facilities of the Center for Demography of Health and Aging at the University of Wisconsin-Madison, funded by NIA Center Grant P30 AG017266. We thank members of the Social Genomics Working Group at University of Wisconsin for helpful comments. This research has been conducted using the UK Biobank Resource under application 42148.
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J.M. and Q.L. conceived and designed the study. J.M. developed the statistical framework, performed the simulations and data analysis, and implemented the software. G.S. assisted in GxSex analysis in UK Biobank. Yixuan Wu assisted in implementing the software. J.H. assisted in developing the statistical framework. Yuchang Wu assisted in UKB data preparation. S.B. assisted in Lung Health Study data preparation. J.S.A., K.S., L.L.S. and J.M.F. advised on result interpretation. Q.L. advised on statistical and genetic issues. J.M. and Q.L. wrote the paper. All authors contributed to paper editing and approved the paper.
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Miao, J., Song, G., Wu, Y. et al. PIGEON: a statistical framework for estimating gene–environment interaction for polygenic traits. Nat Hum Behav (2025). https://doi.org/10.1038/s41562-025-02202-9
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DOI: https://doi.org/10.1038/s41562-025-02202-9
