Evaluation of 41 candidate gene variants for obesity in the EPIC-Potsdam cohort by multi-locus stepwise regression

Obesity has become a leading preventable cause of morbidity and mortality in many parts of the world. It is thought to originate from multiple genetic and environmental determinants. The aim of the current study was to introduce haplotype-based multi-locus stepwise regression (MSR) as a method to investigate combinations of unlinked single nucleotide polymorphisms (SNPs) for obesity phenotypes.

In 2,122 healthy randomly selected men and women of the EPIC-Potsdam cohort, the association between 41 SNPs from 18 obesity-candidate genes and either body mass index (BMI, mean=25.9 kg/m(2), SD=4.1) or waist circumference (WC, mean=85.2 cm, SD=12.6) was assessed. Single SNP analyses were done by using linear regression adjusted for age, sex, and other covariates. Subsequently, MSR was applied to search for the 'best' SNP combinations. Combinations were selected according to specific AICc and p-value criteria. Model uncertainty was accounted for by a permutation test.

The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (β = -0.33, SE=0.13), FTO rs9939609 (β=0.28, SE=0.13), MC4R rs17700144 (β=0.41, SE=0.15), and MC4R rs10871777 (β=0.34, SE=0.14). All these SNPs showed similar effects on waist circumference. The two 'best' six-SNP combinations for BMI (global p-value= 3.45⋅10(-6) and 6.82⋅10(-6)) showed effects ranging from -1.70 (SE=0.34) to 0.74 kg/m(2) (SE=0.21) per allele combination. We selected two six-SNP combinations on waist circumference (global p-value = 7.80⋅10(-6) and 9.76⋅10(-6)) with an allele combination effect of -2.96 cm (SE=0.76) at maximum. Additional adjustment for BMI revealed 15 three-SNP combinations (global p-values ranged from 3.09⋅10(-4) to 1.02⋅10(-2)). However, after carrying out the permutation test all SNP combinations lost significance indicating that the statistical associations might have occurred by chance.

MSR provides a tool to search for risk-related SNP combinations of common traits or diseases. However, the search process does not always find meaningful SNP combinations in a dataset.