Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies
Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies
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DOI：
10.1093/hmg/ddt064
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发表时间：
2013-06-01
影响因子：
3.5
通讯作者：
Teo, Yik-Ying
中科院分区：
文献类型：
--
作者：
Wang, Xu;Chua, Hui-Xiang;Teo, Yik-Ying
全基因组关联研究（GWAS）已经发现了数千种与人类健康和疾病相关的变异。虽然早期的GWAS主要集中在欧洲，东亚和南亚血统的遗传同质人群，但下一代全基因组调查开始在单一的荟萃分析中汇集来自种族多样性人群的研究。然而，传统的流行病学策略荟萃分析，假设固定或随机效应可能不是最合适的方法，联合收割机GWAS的结果，因为这些要么赋予低统计功率或确定大多数位点的变异携带同质效应大小，在大多数研究中存在。在跨种族荟萃分析中，一些遗传位点可能会在人群中表现出异质性效应量。这可能是由于研究设计的差异，与其他遗传变异相互作用产生的差异，或归因于调节基因影响的环境，饮食或生活方式因素的真正生物学差异。在这里，我们比较了在遗传多样性人群中荟萃分析GWAS的不同策略，我们有意改变了不同人群中存在的效应量。随后，我们将产生最高统计功效的方法应用于对2型糖尿病中7个GWAS的跨种族荟萃分析，并表明这些方法识别了经典策略可能会错过的真正关联。
Genome-wide association studies (GWASs) have discovered thousands of variants that are associated with human health and disease. Whilst early GWASs have primarily focused on genetically homogeneous populations of European, East Asian and South Asian ancestries, the next-generation genome-wide surveys are starting to pool studies from ethnically diverse populations within a single meta-analysis. However, classical epidemiological strategies for meta-analyses that assume fixed- or random-effects may not be the most suitable approaches to combine GWAS findings as these either confer low statistical power or identify mostly loci where the variants carry homogeneous effect sizes that are present in most of the studies. In a trans-ethnic meta-analysis, it is likely that some genetic loci will exhibit heterogeneous effect sizes across the populations. This may be due to differences in study designs, differences arising from the interactions with other genetic variants, or genuine biological differences attributed to environmental, dietary or lifestyle factors that modulate the influence of the genes. Here we compare different strategies for meta-analyzing GWAS across genetically diverse populations, where we intentionally vary the effect sizes present across the different populations. We subsequently applied the methods that yielded the highest statistical power to a trans-ethnic meta-analysis of seven GWAS in type 2 diabetes, and showed that these methods identified bona fide associations that would otherwise have been missed by the classical strategies.