So many correlated tests, so little time!: Rapid adjustment of P values for multiple correlated tests
So many correlated tests, so little time!: Rapid adjustment of P values for multiple correlated tests
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DOI：
10.1086/522036
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发表时间：
2007-12-01
影响因子：
9.8
通讯作者：
Boehnke, Michael
中科院分区：
文献类型：
--
作者：
Conneely, Karen N.;Boehnke, Michael
当代遗传关联研究可能会对数十万种遗传变异进行关联测试，通常涉及多个二分类和连续性状，或者在不止一种遗传模式下进行。由于附近标记之间的连锁不平衡以及性状和模式之间的相关性，许多这些关联测试可能相互关联。由于当测试相关时，诸如邦费罗尼校正等常规方法过于保守，所以常常采用置换检验和基于模拟的方法来对相关测试组进行多重检验调整。我们在此提出一种计算针对相关测试调整后的P值（P - ACT）的方法，该方法能在更短的计算时间内达到置换检验或基于模拟的检验的准确性，并且我们表明我们的方法适用于许多基于多个性状、标记和遗传模型的常见关联测试。模拟结果表明，P - ACT具有置换检验的功效，并能对数百个相关联的关联测试进行有效的调整。在作为芬兰 - 美国非胰岛素依赖型糖尿病遗传学研究（FUSION）一部分所分析的数据中，我们观察到P - ACT与相应的基于置换的P值之间几乎是一一对应的关系（r² > 0.999），达到了与置换检验相同的精度，但速度快数千倍。
Contemporary genetic association studies may test hundreds of thousands of genetic variants for association, often with multiple binary and continuous traits or under more than one model of inheritance. Many of these association tests may be correlated with one another because of linkage disequilibrium between nearby markers and correlation between traits and models. Permutation tests and simulation-based methods are often employed to adjust groups of correlated tests for multiple testing, since conventional methods such as Bonferroni correction are overly conservative when tests are correlated. We present here a method of computing P values adjusted for correlated tests (P-ACT) that attains the accuracy of permutation or simulation-based tests in much less computation time, and we show that our method applies to many common association tests that are based on multiple traits, markers, and genetic models. Simulation demonstrates that P-ACT attains the power of permutation testing and provides a valid adjustment for hundreds of correlated association tests. In data analyzed as part of the Finland-United States Investigation of NIDDM Genetics (FUSION) study, we observe a near one-to-one relationship (r(2) > .999) between P-ACT and the corresponding permutation-based P values, achieving the same precision as permutation testing but thousands of times faster.