Evaluating and improving power in whole-genome association studies using fixed marker sets
Evaluating and improving power in whole-genome association studies using fixed marker sets
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DOI：
10.1038/ng1816
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发表时间：
2006-06-01
期刊：
影响因子：
30.8
通讯作者：
Daly, Mark J.
中科院分区：
文献类型：
--
作者：
Pe'er, Itsik;de Bakker, Paul I. W.;Daly, Mark J.
新兴技术首次使同时对数十万个snp进行基因分型成为可能，从而使全基因组关联研究成为可能。利用来自国际HapMap项目的经验基因型数据，我们评估了三个全基因组基因分型阵列中包含的snp集捕获整个基因组中常见snp的程度，我们发现这些产品直接或通过连锁不平衡捕获了大多数常见snp。我们探索了使用HapMap数据的分析策略，以提高使用这些固定标记进行的关联研究的能力，并表明在关联分析中有限地包含特定的单倍型测试可以将捕获的常见变异的比例增加25-100%。最后，我们通过加权每个统计检验的可能性来反映与之相关的假定因果等位基因的数量，引入贝叶斯方法进行关联分析。
Emerging technologies make it possible for the first time to genotype hundreds of thousands of SNPs simultaneously, enabling whole-genome association studies. Using empirical genotype data from the International HapMap Project, we evaluate the extent to which the sets of SNPs contained on three whole-genome genotyping arrays capture common SNPs across the genome, and we find that the majority of common SNPs are well captured by these products either directly or through linkage disequilibrium. We explore analytical strategies that use HapMap data to improve power of association studies conducted with these fixed sets of markers and show that limited inclusion of specific haplotype tests in association analysis can increase the fraction of common variants captured by 25-100%. Finally, we introduce a Bayesian approach to association analysis by weighting the likelihood of each statistical test to reflect the number of putative causal alleles to which it is correlated.