Genome-Wide Significance Levels and Weighted Hypothesis Testing.
Genome-Wide Significance Levels and Weighted Hypothesis Testing.
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DOI：
10.1214/09-sts289
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发表时间：
2009-11
期刊：
影响因子：
--
通讯作者：
Wasserman L
中科院分区：
文献类型：
--
作者：
Roeder K;Wasserman L
遗传研究通常涉及同时测试大量相关假设。为了控制总体错误率，需要大量的惩罚，使得检测中等强度的信号变得困难。为了提高这种情况下的功效，许多作者考虑使用加权 p 值，其动机通常基于假设的科学合理性。我们回顾了这些文献，得出最佳权重，并表明该功效对于这些权重的错误指定非常稳健。我们在实践中考虑两种选择权重的方法。第一个是外部加权，基于先验信息。第二种是估计权重，使用数据来选择权重。
Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered using weighted p-values, with the motivation often based upon the scientific plausibility of the hypotheses. We review this literature, derive optimal weights and show that the power is remarkably robust to misspecification of these weights. We consider two methods for choosing weights in practice. The first, external weighting, is based on prior information. The second, estimated weighting, uses the data to choose weights.