Efficiency and power in genetic association studies
Efficiency and power in genetic association studies
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DOI：
10.1038/ng1669
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发表时间：
2005-11-01
期刊：
影响因子：
30.8
通讯作者：
Altshuler, D
中科院分区：
文献类型：
--
作者：
de Bakker, PIW;Yelensky, R;Altshuler, D
我们通过专门研究基因分型投资和统计功效之间的关系，研究了全基因组关联研究中标签SNP的选择和分析。成对或多标记方法是否能最大限度地提高效率和功效？当从不完整的资源（例如HapMap）中选择标签时，功率会受到多大程度的影响？我们使用来自HapMap ENCODE项目的基因型数据、在现实疾病模型下模拟的关联研究以及多假设检验的经验校正来解决这些问题。我们展示了一种基于单倍型的标记方法，其性能一致优于单标记测试和显着提高标记效率的优先排序方法。检查所有观察到的单倍型的关联性，而不仅仅是那些已知SNP的代理，可以提高检测罕见因果等位基因的能力，但代价是降低检测常见因果等位基因的能力。功效对于从其中选择标签的参考面板的完整性是稳健的。这些发现对优先考虑标签SNP和解释关联研究具有重要意义。
We investigated selection and analysis of tag SNPs for genome-wide association studies by specifically examining the relationship between investment in genotyping and statistical power. Do pairwise or multimarker methods maximize efficiency and power? To what extent is power compromised when tags are selected from an incomplete resource such as HapMap? We addressed these questions using genotype data from the HapMap ENCODE project, association studies simulated under a realistic disease model, and empirical correction for multiple hypothesis testing. We demonstrate a haplotype-based tagging method that uniformly outperforms single-marker tests and methods for prioritization that markedly increase tagging efficiency. Examining all observed haplotypes for association, rather than just those that are proxies for known SNPs, increases power to detect rare causal alleles, at the cost of reduced power to detect common causal alleles. Power is robust to the completeness of the reference panel from which tags are selected. These findings have implications for prioritizing tag SNPs and interpreting association studies.