Multi-omics Insights Into the Biological Mechanisms Underlying Statistical Gene-by-Lifestyle Interactions With Smoking and Alcohol Consumption

Authors

Timothy D Majarian
Amy R Bentley
Vincent Laville
Michael R Brown
Daniel I Chasman
Paul S de Vries
Mary F Feitosa
Nora Franceschini
W James Gauderman
Casey Marchek
Daniel Levy
Alanna C Morrison
Michael Province
Dabeeru C Rao
Karen Schwander
Yun Ju Sung
Charles N Rotimi
Hugues Aschard
C Charles Gu
Alisa K Manning
CHARGE Gene-Lifestyle Interactions Working Group

Language

English

Publication Date

1-1-2022

Journal

Frontiers in Genetics

DOI

10.3389/fgene.2022.95471

PMID

36544485

PMCID

PMC9760722

PubMedCentral® Posted Date

12-5-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Though both genetic and lifestyle factors are known to influence cardiometabolic outcomes, less attention has been given to whether lifestyle exposures can alter the association between a genetic variant and these outcomes. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium's Gene-Lifestyle Interactions Working Group has recently published investigations of genome-wide gene-environment interactions in large multi-ancestry meta-analyses with a focus on cigarette smoking and alcohol consumption as lifestyle factors and blood pressure and serum lipids as outcomes. Further description of the biological mechanisms underlying these statistical interactions would represent a significant advance in our understanding of gene-environment interactions, yet accessing and harmonizing individual-level genetic and 'omics data is challenging. Here, we demonstrate the coordinated use of summary-level data for gene-lifestyle interaction associations on up to 600,000 individuals, differential methylation data, and gene expression data for the characterization and prioritization of loci for future follow-up analyses. Using this approach, we identify 48 genes for which there are multiple sources of functional support for the identified gene-lifestyle interaction. We also identified five genes for which differential expression was observed by the same lifestyle factor for which a gene-lifestyle interaction was found. For instance, in gene-lifestyle interaction analysis, the T allele of rs6490056 (

Keywords

multi-omics, gene-lifestyle interactions, smoking, alcohol, serum lipids, blood pressure, summary data

Published Open-Access

yes

Recommended Citation

Majarian, Timothy D; Bentley, Amy R; Laville, Vincent; et al., "Multi-omics Insights Into the Biological Mechanisms Underlying Statistical Gene-by-Lifestyle Interactions With Smoking and Alcohol Consumption" (2022). Faculty, Staff and Student Publications. 822.
https://digitalcommons.library.tmc.edu/uthsph_docs/822