Optimizing the power of genome-wide association studies by using publicly available reference samples to expand the control group.
Optimizing the power of genome-wide association studies by using publicly available reference samples to expand the control group.
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DOI：
10.1002/gepi.20482
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发表时间：
2010-05
影响因子：
2.1
通讯作者：
Morris, Andrew P.
中科院分区：
文献类型：
--
作者：
Zhuang, Joanna J.;Zondervan, Krina;Nyberg, Fredrik;Harbron, Chris;Jawaid, Ansar;Cardon, Lon R.;Barratt, Bryan J.;Morris, Andrew P.
关键词：
事实证明，全基因组关联（GWA）研究在识别对复杂人类疾病产生影响的新基因位点方面非常成功。在此过程中，他们强调了这样一个事实，即许多具有适度影响的潜在位点仍未被发现，部分原因是需要由数千人组成的样本。大型国际倡议，例如威康信托病例控制联盟、遗传协会信息网络以及遗传和表型信息数据库，旨在通过公开全基因组数据来促进中等效应基因的发现，允许将信息组合起来进行汇总分析。原则上，这些研究中的疾病或对照样本可以通过明智地用作其他性状的“基因匹配对照”来提高任何 GWA 研究的功效。在这里，我们提出了该问题的生物学动机以及利用公开的疾病或参考样本扩大对照组的理论潜力。我们证明，在存在群体结构的情况下，这种策略的简单应用会大大增加假阳性错误率。作为一种补救措施，我们利用全基因组数据和模型选择技术来识别与疾病相关的遗传变异“轴”。然后将这些轴作为协变量纳入关联分析中，以校正群体结构，这可能会导致原始 GWA 研究中样本遗传信息标准分析的功效增加。热内特.流行病。 34: 319–326, 2010。© 2010 Wiley-Liss, Inc.
Genome-wide association (GWA) studies have proved extremely successful in identifying novel genetic loci contributing effects to complex human diseases. In doing so, they have highlighted the fact that many potential loci of modest effect remain undetected, partly due to the need for samples consisting of many thousands of individuals. Large-scale international initiatives, such as the Wellcome Trust Case Control Consortium, the Genetic Association Information Network, and the database of genetic and phenotypic information, aim to facilitate discovery of modest-effect genes by making genome-wide data publicly available, allowing information to be combined for the purpose of pooled analysis. In principle, disease or control samples from these studies could be used to increase the power of any GWA study via judicious use as “genetically matched controls” for other traits. Here, we present the biological motivation for the problem and the theoretical potential for expanding the control group with publicly available disease or reference samples. We demonstrate that a naïve application of this strategy can greatly inflate the false-positive error rate in the presence of population structure. As a remedy, we make use of genome-wide data and model selection techniques to identify “axes” of genetic variation which are associated with disease. These axes are then included as covariates in association analysis to correct for population structure, which can result in increases in power over standard analysis of genetic information from the samples in the original GWA study. Genet. Epidemiol. 34: 319–326, 2010. © 2010 Wiley-Liss, Inc.
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影响因子：
30.8
作者：
Marchini, Jonathan;Howie, Bryan;Donnelly, Peter
通讯作者：
Donnelly, Peter
DOI：
10.1056/nejmoa072366
发表时间：
2007-08-02
期刊：
The New England journal of medicine
影响因子：
--
作者：
Samani NJ;Erdmann J;Hall AS;Hengstenberg C;Mangino M;Mayer B;Dixon RJ;Meitinger T;Braund P;Wichmann HE;Barrett JH;König IR;Stevens SE;Szymczak S;Tregouet DA;Iles MM;Pahlke F;Pollard H;Lieb W;Cambien F;Fischer M;Ouwehand W;Blankenberg S;Balmforth AJ;Baessler A;Ball SG;Strom TM;Braenne I;Gieger C;Deloukas P;Tobin MD;Ziegler A;Thompson JR;Schunkert H;WTCCC and the Cardiogenics Consortium
通讯作者：
WTCCC and the Cardiogenics Consortium
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
2.1
作者：
Miclaus, Kelci;Wolfinger, Russ;Czika, Wendy
通讯作者：
Czika, Wendy
影响因子：
56.9
作者：
Frayling, Timothy M.;Timpson, Nicholas J.;McCarthy, Mark I.
通讯作者：
McCarthy, Mark I.