Complex diseases and traits arise from the interplay of genetic and environmental factors. Understanding these interactions is crucial, yet current statistical methods for genotype-by-environment (GxE) interaction analysis often fail to accurately determine causal directions. This project proposes the development of a novel framework, the Genetic Causality Inference Model (GCIM), which infers causal relationships without assuming a predefined direction. Many existing methods rely on prior knowledge of the causal direction in GxE interactions, and if these assumptions are incorrect, they can lead to misleading conclusions.
The primary objective of this project is to develop GCIM to accurately determine the causal directions of GxE interactions. The model will be validated through simulations with varying scenarios, and its performance will be compared to that of existing GxE methods. The model will also be applied to UK Biobank data, which offers comprehensive phenotypic, genetic, and environmental data essential for robust GxE analyses. This dataset will allow us to analyze the causal directions of complex traits and diseases with rigorous quality control measures and confounding adjustments to ensure the reliability of our findings.
This project seeks to address a common limitation of current GxE methods: the potential for misinterpretation when the true causal direction is unknown. GCIM overcomes this by inferring causal directions without predefined assumptions. Through simulations and application to UK Biobank data, the proposed method will be thoroughly validated and verified. The validated method will enhance GxE analysis in complex diseases. The developed framework will be integrated into user-friendly software and made publicly available. The findings will be disseminated through high-impact publications and scientific platforms, promoting collaboration and contributing to personalized medicine by incorporating individual environmental exposures.
