Gene-lifestyle interactions in the genomics of human complex traits

Abstract

The role and biological significance of gene-environment interactions in human traits and diseases remain poorly understood. To address these questions, the CHARGE Gene-Lifestyle Interactions Working Group conducted series of genome-wide interaction studies (GWIS) involving up to 610,475 individuals across four ancestries 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 G×E interactions across phenotype-exposure-ancestry combinations, and to derive insights on the potential mechanistic underlying G×E through in-silico functional analyses. Our analyses show 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 modest correlation between marginal 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 potential 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-analyze genetic and interaction effects. We performed simulations to characterize these limitations and to provide the community with guidelines for future G×E studies.

Gene-vegetarianism interactions in calcium, testosterone, and eGFR identified in genome-wide analysis across 30 biomarkers

“…These gene-vegetarianism interactions can also help explain inconsistencies observed in previous observational studies, especially across ancestral groups [73]. We proposed three novel genevegetarianism interactions in this study and used available functional analyses to put these interactions into plausible biological context.…”

“…In this multi-trait GWIS, the multiple testing burden was high, on top of the already strict genome-wide significance threshold. GWIS have sample size requirements which require approximately four times more participants to achieve the same power as in a GWAS with comparable effect sizes [72, 73]. We suspect that future studies with larger sample sizes would produce a higher number of significant loci.…”

“…The emerging paradigms of precision medicine and precision nutrition (also called nutrigenetics) suggest that genetic makeup should help inform optimal disease treatment strategies [78]. Gene-environment interactions are able to indicate potential differences in molecular mechanisms and pathways utilized among individuals in different exposure groups; these pathways may be different even in cases where small phenotypic variance is observed [73]. These gene-vegetarianism interactions can also help explain inconsistencies observed in previous observational studies, especially across ancestral groups [73].…”

“…Gene-environment interactions are able to indicate potential differences in molecular mechanisms and pathways utilized among individuals in different exposure groups; these pathways may be different even in cases where small phenotypic variance is observed [73]. These gene-vegetarianism interactions can also help explain inconsistencies observed in previous observational studies, especially across ancestral groups [73]. We proposed three novel gene-vegetarianism interactions in this study and used available functional analyses to put these interactions into plausible biological context.…”

Multi-omics insights into the biological mechanisms underlying statistical gene-by-lifestyle interactions with smoking and alcohol consumption

“…Given the minor differences in filtering strategies for each of these projects, all meta-analysis results were re-processed using a common pipeline, as is described in more detail in ( Laville et al, 2022 ). Briefly, SNPs were excluded for low MAF (<1%) or significant heterogeneity across included cohorts ( p < 10–6).…”