Interaction between Social/Psychosocial Factors and Genetic Variants on Body Mass Index: A Gene-Environment Interaction Analysis in a Longitudinal Setting

Abstract

Obesity, which develops over time, is one of the leading causes of chronic diseases such as cardiovascular disease. However, hundreds of BMI (body mass index)-associated genetic loci identified through large-scale genome-wide association studies (GWAS) only explain about 2.7% of BMI variation. Most common human traits are believed to be influenced by both genetic and environmental factors. Past studies suggest a variety of environmental features that are associated with obesity, including socioeconomic status and psychosocial factors. This study combines both gene/regions and environmental factors to explore whether social/psychosocial factors (childhood and adult socioeconomic status, social support, anger, chronic burden, stressful life events, and depressive symptoms) modify the effect of sets of genetic variants on BMI in European American and African American participants in the Health and Retirement Study (HRS). In order to incorporate longitudinal phenotype data collected in the HRS and investigate entire sets of single nucleotide polymorphisms (SNPs) within gene/region simultaneously, we applied a novel set-based test for gene-environment interaction in longitudinal studies (LGEWIS). Childhood socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI in European Americans in the HRS. The most significant SNP (rs9540488) by childhood socioeconomic status interaction within the rs9540493 gene/region was suggestively replicated in the Multi-Ethnic Study of Atherosclerosis (MESA) (p = 0.07).

Human genes influence the interaction between Streptococcus mutans and host caries susceptibility: a genome-wide association study in children with primary dentition

“…The interactions between SNPs and S. mutans were assessed by including a product term () in the regression models. 50,51 To maximize sample size and power, stratified analyses were not conducted at the genome-wide association analysis stage. 52 …”

Change and Stability in Sibling Resemblance in Obesity Markers: The Portuguese Sibling Study on Growth, Fitness, Lifestyle, and Health

“…In any case, what these results tend to show is the importance of both genetic and environmental factors and probably their interactions on changes in obesity markers within the family orbit. We also contend that given the evident lack of data on genome-wide association (GWA) and gene-environment interaction studies in all markers (except for BMI), we are not yet able to present a clear picture of what is happening [42–44]. Indeed, using BMI data Felix et al [43] GWA study signaled that the direction of the effect size for all 15 single nucleotide polymorphisms (SNPs) was identical in children and in adults.…”

“…However, Graff et al [44] showed that obesity susceptibility loci may have a comparatively stronger role during adolescence than adulthood, with variations across race/ethnic subpopulations. Additionally, using European and African American samples, Zhao et al [42] examined social/psychosocial factors that may modify the effect of sets of genetic variants on BMI and reported that socioeconomic status (parental education) was found to modify the genetic effect in the gene/region around SNP rs9540493 on BMI only in European Americans.…”

Causal effects from tobacco smoking on obesity-related traits: a Mendelian randomization study

“…16 The previous GWAS meta-analysis for body mass index using the Genetic Epidemiology Research on Adult Health and Aging (GERA) and Genetic Investigation of ANthropometric Traits (GIANT) consortium was used as the replication outcome data, which was available in the GWAS catalog (URL: https://www.ebi.ac.uk/gwas/studies/GCST006368) (Figure 1). 13,20 The meta-analysis included 364,487 samples mostly of European ancestry towards body mass index phenotype (Supplementary Table 2). The GERA data was not included in the previous meta-analysis which provided the genetic instrumental variable, as GERA data was only used for GWAS towards drinking behavior in the meta-analysis.…”