Gene-Environment Interactions in Human Evolution and Complex Traits

Project Summary Genetic variations, environmental exposures, and their interactions underlie the etiology of all human diseases. While genome-wide association studies have revealed many trait-associated genetic variants and epidemiological studies have pinpointed myriad disease-associated environmental factors, the role

of their interactions is much less explored, mainly due to the lack of very large population cohorts, high- quality environmental measures, and efficient tools. This proposal aims to characterize gene- environment interactions (GEI) in both human evolution and complex traits. Genetic and polygenic adaptations to local environments during human evolution have shaped the gene-environment

relationship and the genetic architecture of complex traits. Leveraging the growing number of ancient DNA, we will first develop and apply statistical tests to identify genetic and polygenic responses to the Agricultural Revolution. The findings of adaptive genetic variants and polygenic traits will inform our

understanding and study of the current epidemics of complex diseases, which are likely results of present-day gene-lifestyle mismatches. Second, to directly identify and quantify GEI in complex traits, we will develop an efficient computational pipeline and perform large-scale interaction analysis across

the genome, phenome, and selected high-quality environmental factors in UK Biobank. All summary statistics will be released publicly as a database on a dedicated website to fuel further explorations, such as meta-analysis and testing for replicability across cohorts and ancestries. Third, to assist and

guide future GEI studies, we will develop the first bioinformatics tool for phenome-wide interaction study (PheWIS) of target genetic variants and environmental exposures, enabling efficient and unbiased search for environment-modifiable phenotypic effects. Moreover, to alleviate the multiple testing burden

in GEI studies with a large number of exposures and clinical outcomes, we will examine if Mendelian randomization analysis coupled with phenome-wide association study (PheWAS-MR) could be an effective way to prioritize potentially causal exposure-outcome relationships, which may increase the statistical power of detecting GEI and assist the downstream search for functional mechanisms. Lastly,

as an effort to improve the portability of polygenic score (PGS) across ancestries and subgroups with the same ancestry, we will use simulated and empirical data to test if explicit statistical modeling of GEI could mitigate the problem. Concurrently, we will examine if PGS-environment interaction analysis is

an effective approach to identify actionable environmental exposures that attenuate genetic risks. Overall, this proposed research will generate new methods, computational tools, database resources, and novel insights into the general patterns of GEI in human complex traits.