Genome-wide methylation data improves dissection of the effect of smoking on body mass index

• Published in: PLoS genetics Vol. 17; no. 9; p. e1009750
• Main Authors: Amador, Carmen, Zeng, Yanni, Barber, Michael, Walker, Rosie M, Campbell, Archie, McIntosh, Andrew M, Evans, Kathryn L, Porteous, David J, Hayward, Caroline, Wilson, James F, Navarro, Pau, Haley, Chris S
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09-09-2021; Public Library of Science (PLoS)
• Subjects: Biobanks; Biology and Life Sciences; Body Mass Index; Cholesterol; DNA Methylation; Environmental changes; Estimates; Gene expression; Genetic aspects; Genetic diversity; Genetics; Genome, Human; Genome-wide association studies; Genomes; Health aspects; High density lipoprotein; Humans; Methylation; Obesity; Pandemics; Physical Sciences; Sexes; Smoking; Smoking - genetics; Social Sciences; United Kingdom; United Kingdom > UK; Scotland
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1009750

Variation in obesity-related traits has a genetic basis with heritabilities between 40 and 70%. While the global obesity pandemic is usually associated with environmental changes related to lifestyle and socioeconomic changes, most genetic studies do not include all relevant environmental covariates, so the genetic contribution to variation in obesity-related traits cannot be accurately assessed. Some studies have described interactions between a few individual genes linked to obesity and environmental variables but there is no agreement on their total contribution to differences between individuals. Here we compared self-reported smoking data and a methylation-based proxy to explore the effect of smoking and genome-by-smoking interactions on obesity related traits from a genome-wide perspective to estimate the amount of variance they explain. Our results indicate that exploiting omic measures can improve models for complex traits such as obesity and can be used as a substitute for, or jointly with, environmental records to better understand causes of disease.