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Title: Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity.
Authors: Hägg S,  Ganna A,  Van Der Laan SW,  Esko T,  Pers TH,  Locke AE,  Berndt SI,  Justice AE,  Kahali B,  Siemelink MA,  Pasterkamp G,  GIANT Consortium,  Strachan DP,  Speliotes EK,  North KE,  Loos RJ,  Hirschhorn JN,  Pawitan Y,  Ingelsson E
Journal: Hum Mol Genet
Date: 2015 Dec 1
Branches: OEEB
PubMed ID: 26376864
PMC ID: PMC4643645
Abstract: 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.