Genome-wide association studies (GWAS) are used for attempting to find genes/genetic loci associated with a phenotype (trait). The trait chosen to be studied might be height, weight or body mass index (BMI) –– or a complex disease such as schizophrenia, Alzheimer disease, or type-2 diabetes. The trait might also be efficacy or toxicity of a particular drug. These multifactorial traits are generally caused by hundreds if not thousands of genes, plus epigenetic effects, plus environmental factors. The present study focuses on gene-environment interactions of a multifactorial trait studied by GWAS.
BMI is a standardized measure of human body size that is calculated from weight and height. Twin studies have demonstrated a heritable component of BMI, and GWAS have shown that BMI is influenced by hundreds of common genetic variants. Recently, a GWAS for BMI on almost 340,000 individuals, reported 97 genetic loci associated with variation in BMI. However, only a few studies have investigated the effect of gene-environment interactions on BMI. Identification of gene-environment interactions for complex human traits poses several challenges. For instance, most GWAS of complex traits have been performed by large-scale meta-analyses of multiple cohorts, which complicate a harmonized collection of lifestyle and environmental data. Also, the contributing effects of genetic variants identified through GWAS are generally small, and differences in the effects of genetic variants between groups exposed to different lifestyle factors may be difficult to detect in smaller cohorts –– due to lack of statistical power.
In the attached paper, authors examine gene-environment interactions in almost 362,500 unrelated participants of Caucasian ancestry from the UK Biobank resource. A total of 94 BMI-associated single-nucleotide polymorphisms (SNPs) or variants –– 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, and 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 (measure of socioeconomic status) were all highly significant (P = 1.5 x 10–29, P = 3.8 x 10–26 and P = 4.7 x 10–11, respectively). Interestingly, the frequency of alcohol consumption, rather than the total weekly amount, resulted in the significant interaction.
Not surprisingly, the FTO locus was the strongest single locus interacting with any of the lifestyle factors. However, 13 significant interactions were also observed after removing the FTO locus from the genetic score. The authors’ 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 characterize the causal factors.
PloS Genet Sept 2o17; 13: e1006977