Prioritized subset analysis: Improving power in genome-wide association studies
Prioritized subset analysis: Improving power in genome-wide association studies
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DOI：
10.1159/000109730
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发表时间：
2008-01-01
期刊：
影响因子：
1.8
通讯作者：
Watanabe, Richard M.
中科院分区：
文献类型：
--
作者：
Li, Chun;Li, Mingyao;Watanabe, Richard M.
背景资料。全基因组关联研究现在对于研究复杂疾病背后的遗传学是可行的。对于许多疾病，候选基因或区域的列表是存在的，将这些信息纳入数据分析可能会提高检测疾病变异的能力。传统的评估GWAS结果总体统计意义的方法忽视了这些信息，因为本质上对所有标记一视同仁。方法：我们提出了优先子集分析(PSA)，即从候选区域中预先选择标记的优先子集，并分别在优先子集及其互补子集上执行错误发现率(FDR)过程。结果：在一系列可供选择的模型中，PSA比全基因组单步FDR调节更强大。功率改善的程度取决于相关SNP在优先子集中的比例及其标称功率，关联SNP的比例越高，标称功率越高，导致功率改善越多。功率改进可以是显著的；对于没有包括在优先级子集中的疾病位置，功率损失几乎可以忽略不计。结论：PSA具有灵活性，允许研究人员结合来自各种来源的先前信息，这将是GWAS的有用工具。版权所有(C)2007 S.Karger AG，巴塞尔。
Background. Genome-wide association studies (GWAS) are now feasible for studying the genetics underlying complex diseases. For many diseases, a list of candidate genes or regions exists and incorporation of such information into data analyses can potentially improve the power to detect disease variants. Traditional approaches for assessing the overall statistical significance of GWAS results ignore such information by inherently treating all markers equally. Methods: We propose the prioritized subset analysis (PSA), in which a prioritized subset of markers is pre-selected from candidate regions, and the false discovery rate (FDR) procedure is carried out in the prioritized subset and its complementary subset, respectively. Results:The PSA is more powerful than the whole-genome single-step FDR adjustment for a range of alternative models. The degree of power improvement depends on the fraction of associated SNPs in the prioritized subset and their nominal power, with higher fraction of associated SNPs and higher nominal power leading to more power improvement. The power improvement can be substantial; for disease loci not included in the prioritized subset,the power loss is almost negligible. Conclusion:The PSA has the flexibility of allowing investigators to combine prior information from a variety of sources, and will be a useful tool for GWAS. Copyright (c) 2007 S. Karger AG, Basel.