Rask-Andersen Mathias, Karlsson Torgny, Ek Weronica E, Johansson Åsa
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
PLoS Genet. 2017 Sep 5;13(9):e1006977. doi: 10.1371/journal.pgen.1006977. eCollection 2017 Sep.
Previous genome-wide association studies (GWAS) have identified hundreds of genetic loci to be associated with body mass index (BMI) and risk of obesity. Genetic effects can differ between individuals depending on lifestyle or environmental factors due to gene-environment interactions. In this study, we examine gene-environment interactions in 362,496 unrelated participants with Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated SNPs, selected from a previous GWAS on BMI, were used to construct weighted genetic scores for BMI (GSBMI). Linear regression modeling was used to estimate the effect of gene-environment interactions on BMI for 131 lifestyle factors related to: dietary habits, smoking and alcohol consumption, physical activity, socioeconomic status, mental health, sleeping patterns, as well as female-specific factors such as menopause and childbirth. In total, 15 lifestyle factors were observed to interact with GSBMI, of which alcohol intake frequency, usual walking pace, and Townsend deprivation index, a measure of socioeconomic status, were all highly significant (p = 1.4510-29, p = 3.8310-26, p = 4.66*10-11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount resulted in a significant interaction. The FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after omitting the FTO locus from the genetic score. Our analyses indicate that many lifestyle factors modify the genetic effects on BMI with some groups of individuals having more than double the effect of the genetic score. However, the underlying causal mechanisms of gene-environmental interactions are difficult to deduce from cross-sectional data alone and controlled experiments are required to fully characterise the causal factors.
以往的全基因组关联研究（GWAS）已经确定了数百个与体重指数（BMI）和肥胖风险相关的基因位点。由于基因 - 环境相互作用，个体之间的遗传效应可能因生活方式或环境因素而有所不同。在本研究中，我们在来自英国生物银行资源的362,496名无亲缘关系的白种人参与者中研究了基因 - 环境相互作用。从先前关于BMI的GWAS中选择的总共94个与BMI相关的单核苷酸多态性（SNP）用于构建BMI的加权遗传评分（GSBMI）。线性回归模型用于估计基因 - 环境相互作用对与以下方面相关的131种生活方式因素的BMI影响：饮食习惯、吸烟和饮酒、身体活动、社会经济地位、心理健康、睡眠模式，以及女性特定因素，如更年期和分娩。总共观察到15种生活方式因素与GSBMI相互作用，其中饮酒频率、平常步行速度和衡量社会经济地位的汤森贫困指数均具有高度显著性（分别为p = 1.45×10 - 29，p = 3.83×10 - 26，p = 4.66×10 - 11）。有趣的是，饮酒频率而非每周总量导致了显著的相互作用。FTO基因座是与任何生活方式因素相互作用的最强单一位点。然而，从遗传评分中剔除FTO基因座后，也观察到了13种显著的相互作用。我们的分析表明，许多生活方式因素会改变对BMI的遗传效应，某些个体组的效应超过遗传评分的两倍。然而，仅从横断面数据很难推断基因 - 环境相互作用的潜在因果机制，需要进行对照实验来全面表征因果因素。
