Leveraging polygenic risk scores to infer causal directions in genotype-by-environment interactions between complex traits
Files
(Published version)
Date
2026
Authors
Fentaw, Z.
Truong, B.
Jayasinghe, D.
Vedova, C.D.
Hemani, G.
Benyamin, B.
Hyppönen, E.
Lee, S.H.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
Human Genetics, 2026; 145(1):19-1-19-16
Statement of Responsibility
Zinabu Fentaw, Buu Truong, Dovini Jayasinghe, Chris Della Vedova, Gibran Hemani, Beben Benyamin, Elina Hyppönen, Hong Lee
Conference Name
Abstract
Most existing genotype-by-environment interaction (G×E) methods assume a known causal direction as an assumption that often does not hold and can lead to biased estimates and spurious findings. To address this, we introduce the Genetic Causality Inference Model (GCIM), a novel approach designed to infer causal directions in G×E studies. GCIM integrates polygenic risk scores (PRS) for both the exposure and the outcome to strengthen causal inference and reduce spurious interaction signals. We evaluated GCIM using simulated data across varying genetic and residual correlation settings and compared its performance to existing PRS-by-environment (PRS×E) models under both null and alternative G×E scenarios. GCIM was also applied to real-world UK Biobank data in both causal directions. GCIM consistently outperformed existing methods by accurately identifying the absence of G×E variance and avoiding false positives, even in the presence of strong phenotypic heteroscedasticity due to residual heterogeneity. Other methods often generated spurious associations, especially under reverse causality. Applying GCIM to UK Biobank data, we investigated 11 circulating biomarkers (including liver enzymes, lipids, and inflammatory markers) and three anthropometric traits (BMI, body fat, and waist-tohip ratio [WHR]). GCIM identified that bilirubin modulates genetic effects on BMI and WHR, while body fat modulates genetic effects on C-reactive protein, with associations remaining significant after multiple testing corrections. Overall, GCIM provides a more reliable framework for GxE analysis, particularly under challenging conditions such as residual heterogeneity and uncertain causal direction. However, further development is needed to improve its statistical power.
School/Discipline
Dissertation Note
Provenance
Description
Available online 07 February 2026
Access Status
Rights
© Crown 2026. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.