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

Carmen Amador,Pau Navarro,Chris S Haley,Archie Campbell,David J Porteous,Michael Barber,Rosie M Walker,Yanni Zeng,Caroline Hayward,Andrew M Mcintosh,James F Wilson,Kathryn L Evans,Heather J Cordell

doi:10.1371/journal.pgen.1009750

Abstract

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.

Highlights

Variation in obesity-related traits such as body mass index (BMI) has a complex basis with heritabilities ranging from 40 to 70%, with the genetic variants detected to date explaining up to 5% of BMI variation [1]
Hundreds of genetic variants associated with obesity-related traits, like body mass index (BMI), have been previously identified, as well as lifestyles contributing to obesity risk
Certain combinations of genetic variants and lifestyles may change the risk of obesity more than expected from their individual effects

Summary

Introduction

Variation in obesity-related traits such as body mass index (BMI) has a complex basis with heritabilities ranging from 40 to 70%, with the genetic variants detected to date explaining up to 5% of BMI variation [1]. When performing GWAS on traits like BMI, lipids, and blood pressure, several studies have stratified their samples on the basis of smoking status or have explicitly modelled interactions leading to identification of new genetic variants associated with those traits [13,14,15]. In UK Biobank, using a new approach that only requires summary statistics, Shin & Lee [16] estimated the contributions of the interactions to be much smaller: 0.6% of BMI variation

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