Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity
Gene-based meta-analysis of genome-wide association studies implicates new loci involved in obesity
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DOI：
10.1093/hmg/ddv379
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发表时间：
2015-12-01
影响因子：
3.5
通讯作者：
Ingelsson, Erik
中科院分区：
文献类型：
--
作者：
Hagg, Sara;Ganna, Andrea;Ingelsson, Erik
迄今为止，全基因组关联研究（GWAS）已经确定了> 100个与体重指数（BMI）相关的单一变异位点。这种方法可能会错过具有高等位基因异质性的位点，因此，本研究的目的是使用基于基因的荟萃分析，以确定具有高等位基因异质性的区域，以发现额外的肥胖易感性位点。我们纳入了来自第1阶段46个队列的123865名欧洲血统个体的GWAS数据和来自第2阶段43个队列的另外103046名个体的Metabochip数据，所有这些数据都在AN thropometric Traits（GIANT）财团的遗传调查中。每个队列均检测了近似240万（第1阶段）或近似20万（第2阶段）插补或基因分型的单一变异体与BMI之间的相关性，随后对17941个基因进行了汇总统计分析。我们使用“基于可变基因的关联研究”（VEGAS）方法将变异分配给基因，并基于模拟计算基于基因的P值。在进行基于基因的荟萃分析之前，将VEGAS方法分别应用于每个队列。在第一阶段，两个已知的（FTO和TMEM 18）和六个新的（PEX 2，MTFR 2，SSFA 2，IARS 2，CEP 295和TXNDC 12）基因座与BMI相关（P < 2.8x10（-6）17941基因测试）。我们确认了所有位点，其中6个仅在第2阶段具有全基因显著性。我们通过通路、表达和甲基化分析为这些位点提供生物学支持。我们的研究结果表明，基于基因的荟萃分析GWAS提供了一个有用的策略，找到感兴趣的基因座，没有确定在标准的单标记分析，由于高等位基因异质性。
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 AN thropometric Traits (GIANT) consortium. Each cohort was tested for association between similar to 2.4million (Stage 1) or similar to 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 < 2.8x10(-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.