Lebrec Jeremie J, Stijnen Theo, van Houwelingen Hans C
Leiden University Medical Center, The Netherlands.
Stat Appl Genet Mol Biol. 2010;9:Article 8. doi: 10.2202/1544-6115.1503. Epub 2010 Jan 13.
In Genomewide association (GWA) studies investigating thousands of SNPs, large sample sizes are needed to obtain a reasonable power after correction for multiple testing. To obtain the necessary sample sizes, data from different populations/cohorts are combined. The problem of pooling evidence across cohorts bears some resemblance with meta-analysis of clinical trials, and in fact classical meta-analytic methodologies from that field are typically used in GWAs. However, in genetics, it can be expected that the cohorts show some amount of heterogeneity in the association measures that are used for significance testing. In this paper, we demonstrate how it is possible to exploit this heterogeneity to improve our ability to detect influential genetic variants. We also discuss how pathway analysis based on summary data can help resolve heterogeneity. The current standard method for testing SNPs across cohorts in GWAs will miss heterogeneous but important genetic variants affecting complex diseases. Our new testing strategy has the potential to detect them while maintaining sensitivity to variants with homogeneous effects.
在全基因组关联（GWA）研究中，要对数千个单核苷酸多态性（SNP）进行研究，在多重检验校正后，需要大样本量才能获得合理的检验效能。为了获得所需的样本量，需合并来自不同人群/队列的数据。跨队列合并证据的问题与临床试验的荟萃分析有一些相似之处，实际上该领域的经典荟萃分析方法通常用于全基因组关联研究。然而，在遗传学中，可以预期各队列在用于显著性检验的关联测量中会表现出一定程度的异质性。在本文中，我们展示了如何利用这种异质性来提高我们检测有影响的基因变异的能力。我们还讨论了基于汇总数据的通路分析如何有助于解决异质性问题。目前在全基因组关联研究中跨队列检测单核苷酸多态性的标准方法会遗漏影响复杂疾病的异质性但重要的基因变异。我们的新检测策略有潜力检测到这些变异，同时保持对具有同质效应的变异的敏感性。
