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Genet."],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Most existing genotype-by-environment interaction (G\u00d7E) 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\u00d7E 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\u00d7E) models under both null and alternative G\u00d7E 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\u00d7E 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-to-hip 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. 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