Evaluation of 41 Candidate Gene Variants for Obesity in the EPIC-Potsdam Cohort by Multi-Locus Stepwise Regression

et al. (2013) Evaluation of 41 Candidate Gene Variants for Obesity in the EPIC-Potsdam Cohort by Multi-Locus Stepwise Regression. PLoS ONE 8(7): e68941. doi:10.1371/journal.pone.0068941 Evaluation of 41 Candidate Gene Variants for Obesity in the EPIC-Potsdam Cohort by Multi-Locus Stepwise Regression Sven Knu ppel 0 Klaus Rohde 0 Karina Meidtner 0 Dagmar Drogan 0 Hermann-Georg Holzhu tter 0 Heiner Boeing 0 Eva Fisher 0 Balraj Mittal, Sanjay Gandhi Medical Institute, India 0 1 Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke , Nuthetal, Germany, 2 Exp. Genetics of Cardiovascular Diseases , Max Delbr u ck Center for Molecular Medicine Berlin-Buch , Berlin, Germany , 3 Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbru cke , Nuthetal, Germany , 4 Institute of Biochemistry, Charite -Universita tsmedizin Berlin , Berlin, Germany , 5 Administrative Office of the Commission on Genetic Testing, Robert Koch-Institute , Berlin , Germany Objective: 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. Methods: 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/m2, 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. Results: The strongest single SNP effects on BMI were found for TBC1D1 rs637797 (b = 20.33, SE = 0.13), FTO rs9939609 (b = 0.28, SE = 0.13), MC4R rs17700144 (b = 0.41, SE = 0.15), and MC4R rs10871777 (b = 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?106) showed effects ranging from 21.70 (SE = 0.34) to 0.74 kg/m2 (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 22.96 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. Conclusion: 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. - Funding: The recruitment phase of the EPIC-Potsdam Study was supported by the Federal Ministry of Science, Germany (01 EA 9401), and the European Union (SOC 95201408 05F02). This study is supported by grants from the Federal Ministry of Education and Science (NGFNplus: 01GS0821 and Competence Network Obesity: 01GI1121B). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors declare that they have no competing interests. Obesity is an increasing health problem worldwide that is associated with an increased risk of several common diseases including cardiovascular diseases, type 2 diabetes mellitus and certain cancers. The World Health Organization estimated that by 2008, 1.4 billion adults, 20 years and older, were overweight and from those more than 200 million men and nearly 300 million women were obese [1]. Although it is well known, that environmental and genetic factors contribute to the development of obesity, the genetic factors predisposing to obesity are still poorly understood [2]. Several studies identified a large number of single nucleotide polymorphisms (SNPs) as determinants of body mass index (BMI, kg/m2), waist circumference, and body fat mass as reviewed in Rankinen et al. [3]. More recently, large scale genome-wide association studies have led to additional discoveries of common obesity-related SNPs [4,5]. However, one of the strongest common genetic predictor of body mass index, the genetic variants of the FTO gene (fat mass and obesity associated gene), explain only 1% of the total heritability of obesity [6]. So far, there is limited data about the extent to which nonadditive effects of genes, mostly described in terms of genegene interaction, will add to the inherited risk for obesity development. It is generally assumed that several loci could interactively contribute to common diseases or traits with higher magnitude of effects than the single variants. Resolving such combined effects is imperative to enable the identification of persons at high risk based on their genetic profile. In order to design a multi-locus based statistical tool to identify SNP combinations we extended the classical haplotype-based approach [7,8] by combining it with stepwise regression [9] and applied this approach before to SNPs related to atopic dermatitis in a chromosomal region [10]. The aim of this study was to introduce an adapted version of the multi-locus stepwise regression (MSR) to combine SNP alleles from various chromosomes, i.e. unphased genotypes, in the way haplotypes are constructed [10,11] and use those allele combinations as units for association analysis with a continuous outcome to identify particular allele combinations related to quantitative disease phenotypes. As an empirical example, we assessed the impact of allelic combinations derived from 41 candidate gene SNPs for obesity-related phenotypes (BMI and waist circumference) in a German population-based sample of healthy middleaged men and women [12]. Materials and Methods Ethics Statement Written informed consent was obtained from all study participants, and approval was given by the Ethical Committee of the Medical Association of the State of Brandenburg, Germany. Subjects and Study Design The European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort study is part of a large multicenter European-wide cohort study [13]. Recruitment of 27,548 participants, aged mainly 35 to 65 years, from the general population living in the area of Potsdam in northern Germany was conducted between 1994 and 1998. The baseline examination included anthropometric and blood pressure measurements, blood sampling, a self-administered validated food-frequency questionnaire, and a personal interview on l (...truncated)