Disentangling phenotype scale, amplification, and effect heterogeneity underlying polygenic GxE architecture of complex traits
Abstract
While polygenic gene-by-environment (GxE) signals are increasingly detected in biobank data, it is often unclear whether they reflect the phenotypes measurement scale or one of two biologically distinct mechanisms: heterogeneous genetic effects across environmental strata (effect heterogeneity) or systematically larger but correlated effects in one stratum (amplification). We introduce REGENA (Robust Estimator of Gene-ENvironment Architectures), a variance-component method that disentangles these contributions at biobank scale by learning a phenotype transformation that minimizes scale-induced GxE, thereby assessing scale-dependence while jointly estimating the cross-environment genetic correlation and environment-specific genetic variances. In simulations, REGENA is well-calibrated and powerful while recovering the transformation underlying scale-dependent GxE. Applied to 43 quantitative traits and seven environmental exposures in 291,273 UK Biobank individuals, REGENA finds that roughly half of trait-environment pairs with significant GxE are scale-dependent. Among the remaining scale-independent trait-environment pairs, GxE heritability accounted for an average of 15% of additive heritability and was driven primarily by effect heterogeneity (accounting for 71% of GxE heritability on average) although there was substantial variation in its relative contribution across trait-environment pairs. Environment-specific effects tend to make a larger contribution at lower allele frequencies, accounting for 6% of genetic variance in low-frequency variants versus 3.9% at common variants (signed test p = 1.7 x 10-24). Modeling these distinctions improves environment-specific polygenic prediction (14% on average), with the largest gains for pairs whose genetic effects are least correlated across environments (p = 4.0 x 10-3; 3.8-fold for testosterone stratified by sex and 2.32-fold for waist-hip ratio stratified by alcohol consumption). Further, environment-stratified GWAS detects 174 loci missed by pooled GWAS with 67 having opposite-signed effects across strata.
Related
- Phenotypic heterogeneity in pediatric pituitary stalk interruption syndrome: greater disease burden in the metabolic-onset phenotype
- Disentangling global atrophy burden from local structural patterns reveals clinically relevant heterogeneity in mild cognitive impairment.
- Case Report: Right frontal periventricular heterotopia underlying secondary network epilepsy with a Lennox–Gastaut phenotype: multimodal SEEG, CCEP, and rs-fMRI evidence
- Integrated Transcriptomic Analysis Identifies a Ferroptosis-Related Gene Signature Underlying Macrophage Heterogeneity and Immune Dysregulation in Rheumatoid Arthritis Synovium
- Disorder-specific and shared genetic architecture underlying schizophrenia and bipolar disorder
BibTeX
@misc{liu2026disentangling,
title = {{Disentangling phenotype scale, amplification, and effect heterogeneity underlying polygenic GxE architecture of complex traits}},
author = {Zhengtong Liu and Aakarsh Anand and Xinyue Qie and Manuela Costantino and Moonseong Jeong and Aditya Gorla and Noah Zaitlen and Andy Dahl and Sriram Sankararaman},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.15.26365681},
url = {https://www.medrxiv.org/content/10.64898/2026.10.15.26365681v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Disentangling phenotype scale, amplification, and effect heterogeneity underlying polygenic GxE architecture of complex traits
AU - Zhengtong Liu
AU - Aakarsh Anand
AU - Xinyue Qie
AU - Manuela Costantino
AU - Moonseong Jeong
AU - Aditya Gorla
AU - Noah Zaitlen
AU - Andy Dahl
AU - Sriram Sankararaman
PY - 2026
DA - 2026/10/15
PB - medRxiv
DO - 10.64898/2026.10.15.26365681
UR - https://www.medrxiv.org/content/10.64898/2026.10.15.26365681v1
AB - While polygenic gene-by-environment (GxE) signals are increasingly detected in biobank data, it is often unclear whether they reflect the phenotypes measurement scale or one of two biologically distinct mechanisms: heterogeneous genetic effects across environmental strata (effect heterogeneity) or systematically larger but correlated effects in one stratum (amplification). We introduce REGENA (Robust Estimator of Gene-ENvironment Architectures), a variance-component method that disentangles these contributions at biobank scale by learning a phenotype transformation that minimizes scale-induced GxE, thereby assessing scale-dependence while jointly estimating the cross-environment genetic correlation and environment-specific genetic variances. In simulations, REGENA is well-calibrated and powerful while recovering the transformation underlying scale-dependent GxE. Applied to 43 quantitative traits and seven environmental exposures in 291,273 UK Biobank individuals, REGENA finds that roughly half of trait-environment pairs with significant GxE are scale-dependent. Among the remaining scale-independent trait-environment pairs, GxE heritability accounted for an average of 15% of additive heritability and was driven primarily by effect heterogeneity (accounting for 71% of GxE heritability on average) although there was substantial variation in its relative contribution across trait-environment pairs. Environment-specific effects tend to make a larger contribution at lower allele frequencies, accounting for 6% of genetic variance in low-frequency variants versus 3.9% at common variants (signed test p = 1.7 x 10-24). Modeling these distinctions improves environment-specific polygenic prediction (14% on average), with the largest gains for pairs whose genetic effects are least correlated across environments (p = 4.0 x 10-3; 3.8-fold for testosterone stratified by sex and 2.32-fold for waist-hip ratio stratified by alcohol consumption). Further, environment-stratified GWAS detects 174 loci missed by pooled GWAS with 67 having opposite-signed effects across strata.
ER -