• Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, Molecular Epidemiology and Science for Life Laboratory, Department of Medical Sciences, Uppsala University, SE-751 41 Uppsala, Sweden.
• Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA, Analytical and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
• Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands.
• Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Estonian Genome Center, University of Tartu, Tartu 51010, Estonia.
• Divisions of Endocrinology and Genetics and Center for Basic and Translational Obesity Research, Boston Children's Hospital, Boston, MA 02115, USA, Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA, Department of Genetics, Harvard Medical School, Boston, MA 02115, USA, Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark, Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
• Center for Statistical Genetics, Department of Biostatistics.
• Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA.
• Department of Epidemiology.
• Department of Internal Medicine, Division of Gastroenterology, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
• Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands, Laboratory of Clinical Chemistry and Hematology, Division Laboratories & Pharmacy, UMC Utrecht, Utrecht, The Netherlands.

To date, genome-wide association studies (GWASs) have identified >100 loci with single variants associated with body mass index (BMI). This approach may miss loci with high allelic heterogeneity; therefore, the aim of the present study was to use gene-based meta-analysis to identify regions with high allelic heterogeneity to discover additional obesity susceptibility loci. We included GWAS data from 123 865 individuals of European descent from 46 cohorts in Stage 1 and Metabochip data from additional 103 046 individuals from 43 cohorts in Stage 2, all within the Genetic Investigation of ANthropometric Traits (GIANT) consortium. Each cohort was tested for association between ∼2.4 million (Stage 1) or ∼200 000 (Stage 2) imputed or genotyped single variants and BMI, and summary statistics were subsequently meta-analyzed in 17 941 genes. We used the 'VErsatile Gene-based Association Study' (VEGAS) approach to assign variants to genes and to calculate gene-based P-values based on simulations. The VEGAS method was applied to each cohort separately before a gene-based meta-analysis was performed. In Stage 1, two known (FTO and TMEM18) and six novel (PEX2, MTFR2, SSFA2, IARS2, CEP295 and TXNDC12) loci were associated with BMI (P < 2.8 × 10(-6) for 17 941 gene tests). We confirmed all loci, and six of them were gene-wide significant in Stage 2 alone. We provide biological support for the loci by pathway, expression and methylation analyses. Our results indicate that gene-based meta-analysis of GWAS provides a useful strategy to find loci of interest that were not identified in standard single-marker analyses due to high allelic heterogeneity.