Reporting and interpretation in genome-wide association studies
Reporting and interpretation in genome-wide association studies
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DOI：
10.1093/ije/dym257
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发表时间：
2008-06-01
影响因子：
7.7
通讯作者：
Wakefield, Jon
中科院分区：
文献类型：
--
作者：
Wakefield, Jon
在全基因组关联研究的背景下，我们批评了一些已被建议用于标记进一步调查的关联的方法。p值是迄今为止最常用的度量，但当关联的先验概率很小时，需要仔细校准，并且由于不考虑与每个测试相关的功率而丢弃信息。q值是一种可以控制错误发现率（FDR）的频率方法。我们提倡使用贝叶斯因子作为数据中关于零假设和备选假设比较的信息的总结，并描述了最近提出的一种易于实现的计算贝叶斯因子的方法。跨研究的数据组合使用贝叶斯因子方法很简单，功率计算也是如此。贝叶斯因子和q值提供了互补的信息，当与p值一起使用时，可以用来减少随后无法重现的报告结果的数量。
Background In the context of genome-wide association studies we critique a number of methods that have been suggested for flagging associations for further investigation.Methods The P-value is by far the most commonly used measure, but requires careful calibration when the a priori probability of an association is small, and discards information by not considering the power associated with each test. The q-value is a frequentist method by which the false discovery rate (FDR) may be controlled.Results We advocate the use of the Bayes factor as a summary of the information in the data with respect to the comparison of the null and alternative hypotheses, and describe a recently-proposed approach to the calculation of the Bayes factor that is easily implemented. The combination of data across studies is straightforward using the Bayes factor approach, as are power calculations.Conclusions The Bayes factor and the q-value provide complementary information and when used in addition to the P-value may be used to reduce the number of reported findings that are subsequently not reproduced.