Large-scale multivariate multi-ancestry Interaction analyses point towards different genetic mechanisms by population and exposure
Yun J Sung,
Mary F. Feitosa,
Amy R Bentley,
Charles N. Rotimi,
L. Adrienne Cupples,
Paul S. de Vries,
Michael R Brown,
Alanna C. Morrison,
Aldi T Kraja,
C. Charles Gu,
on behalf of the CHARGE Gene-Lifestyle Interactions Working Group
Posted 28 Feb 2019
bioRxiv DOI: 10.1101/562157
Posted 28 Feb 2019
The CHARGE Gene-Lifestyle Interactions Working Group is a unique initiative formed to improve our understanding of the role and biological significance of gene-environment interactions in human traits and diseases. The consortium published several multi-ancestry genome-wide interaction studies (GWIS) involving up to 610,475 individuals for three lipids and four blood pressure traits while accounting for interaction effects with drinking and smoking exposures. Here we used GWIS summary statistics from these studies to decipher potential differences in genetic associations and GxE interactions across phenotype-exposure-population trios, and to derive new insights on the potential mechanistic underlying GxE through in-silico functional analyses. Our comparative analysis shows first that interaction effects likely contribute to the commonly reported ancestry-specific genetic effect in complex traits, and second, that some phenotype-exposures pairs are more likely to benefit from a greater detection power when accounting for interactions. It also highlighted a negligible correlation between main and interaction effects, providing material for future methodological development and biological discussions. We also estimated contributions to phenotypic variance, including in particular the genetic heritability conditional on the exposure, and heritability partitioned across a range of functional annotations and cell-types. In these analyses, we found multiple instances of heterogeneity of functional partitions between exposed and unexposed individuals, providing new evidence for likely exposure-specific genetic pathways. Finally, along this work we identified potential biases in methods used to jointly meta-analyses genetic and interaction effects. We performed a series of simulations to characterize these limitations and to provide the community with guideline for future GxE studies.
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