Genome-Wide Interaction and Pathway Association Studies for Body Mass Index.

Hongxiao Jiao,Miaomiao Zhang,R Arlen Price,Yaogang Wang,Yuan Zhang,Yong Zang,Kai Wang,Kai Wang,Wei-Dong Li

doi:10.3389/fgene.2019.00404

Abstract

Objective: We investigated gene interactions (epistasis) for body mass index (BMI) in a European-American adult female cohort via genome-wide interaction analyses (GWIA) and pathway association analyses.Methods: Genome-wide pairwise interaction analyses were carried out for BMI in 493 extremely obese cases (BMI > 35 kg/m2) and 537 never-overweight controls (BMI < 25 kg/m2). To further validate the results, specific SNPs were selected based on the GWIA results for haplotype-based association studies. Pathway-based association analyses were performed using a modified Gene Set Enrichment Algorithm (GSEA) (GenGen program) to further explore BMI-related pathways using our genome wide association study (GWAS) data set, GIANT, ENGAGE, and DIAGRAM Consortia.Results: The EXOC4-1q23.1 interaction was associated with BMI, with the most significant epistasis between rs7800006 and rs10797020 (P = 2.63 × 10-11). In the pathway-based association analysis, Tob1 pathway showed the most significant association with BMI (empirical P < 0.001, FDR = 0.044, FWER = 0.040). These findings were further validated in different populations.Conclusion: Genome-wide pairwise SNP-SNP interaction and pathway analyses suggest that EXOC4 and TOB1-related pathways may contribute to the development of obesity.

Highlights

Obesity is a worldwide epidemic associated with increased morbidity of chronic diseases, including diabetes, cardiovascular diseases, metabolic syndrome, and cancer
These genetic variability can explain only a minor fraction of obesity cases (Li et al, 2010; Speliotes et al, 2010). This is partly due to the existence of other mechanisms such as epigenetics, gene-environment, and gene-gene interactions, that influence the heritability of obesity (Gibson, 2010; Wang X. et al, 2010)
To avoid errors caused by chance and rare genotypes, some interactions were excluded according to the exclusion criteria, which has been described in method. rs7800006(EXOC4)-rs10797020(1q23.1) interaction yielded the lowest P-value (P = 2.63 × 10−11) after screening by exclusion criteria, but did not pass the threshold for multiple testing (P < 4.05 × 10−13)

Summary

Introduction

Obesity is a worldwide epidemic associated with increased morbidity of chronic diseases, including diabetes, cardiovascular diseases, metabolic syndrome, and cancer. In 2015, 603.7 million adults and 107.7 million children were obese; in many countries the incidence of obesity continues to rise, doubling since 1980 (Afshin et al, 2017). Large-scale genomewide association studies (GWASs) and meta-analyses have successfully identified in excess of 75 loci associated with obesity (Fall and Ingelsson, 2014). These genetic variability can explain only a minor fraction of obesity cases (Li et al, 2010; Speliotes et al, 2010). Almost one-third of the genetic variance in the etiology of obesity were due to nonadditive factors, according to the family, twin and adoption studies (Stunkard et al, 1986; Price, 1987; Sorensen et al, 1989; Stunkard et al, 1990)

Methods

Results

Conclusion