Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures.
Cauchy combination test: a powerful test with analytic p-value calculation under arbitrary dependency structures.
复制标题
DOI：
10.1080/01621459.2018.1554485
复制
发表时间：
2020
影响因子：
3.7
通讯作者：
Xie J
中科院分区：
文献类型：
--
作者：
Liu Y;Xie J
组合单个p值以聚合多个小效应在统计学中有着长期的兴趣，可以追溯到经典的Fisher组合检验。在现代大规模数据分析中，相关性和稀疏性是数据的共同特征，高效的计算是处理海量数据的必要条件。为了克服这些挑战，我们提出了一个新的测试，利用柯西分布。我们的检验统计量具有简单的形式，并且被定义为单个p值的柯西变换的加权和。我们证明了一个非渐近的结果，我们提出的检验统计量的零分布的尾部可以很好地近似于任意依赖结构下的柯西分布。基于这一理论结果，我们提出的检验的p值计算不仅准确，而且与经典的z检验或t检验一样简单，使我们的检验非常适合分析海量数据。我们进一步表明，所提出的测试的功率是渐近最优的强稀疏设置。大量的模拟表明，所提出的测试既有强大的力量对稀疏的替代品和一个很好的精度相对于p值的计算，特别是对于非常小的p值。拟议的测试也被应用到克罗恩病的全基因组关联研究，并与现有的几个测试进行比较。
Combining individual p-values to aggregate multiple small effects has a long-standing interest in statistics, dating back to the classic Fisher’s combination test. In modern large-scale data analysis, correlation and sparsity are common features and efficient computation is a necessary requirement for dealing with massive data. To overcome these challenges, we propose a new test that takes advantage of the Cauchy distribution. Our test statistic has a simple form and is defined as a weighted sum of Cauchy transformation of individual p-values. We prove a non-asymptotic result that the tail of the null distribution of our proposed test statistic can be well approximated by a Cauchy distribution under arbitrary dependency structures. Based on this theoretical result, the p-value calculation of our proposed test is not only accurate, but also as simple as the classic z-test or t-test, making our test well suited for analyzing massive data. We further show that the power of the proposed test is asymptotically optimal in a strong sparsity setting. Extensive simulations demonstrate that the proposed test has both strong power against sparse alternatives and a good accuracy with respect to p-value calculations, especially for very small p-values. The proposed test has also been applied to a genome-wide association study of Crohn’s disease and compared with several existing tests.
登录
查看更多内容
影响因子：
30.8
作者：
Yang, Jian;Ferreira, Teresa;Morris, Andrew P.;Medland, Sarah E.;Madden, Pamela A. F.;Heath, Andrew C.;Martin, Nicholas G.;Montgomery, Grant W.;Weedon, Michael N.;Loos, Ruth J.;Frayling, Timothy M.;McCarthy, Mark I.;Hirschhorn, Joel N.;Goddard, Michael E.;Visscher, Peter M.
通讯作者：
Visscher, Peter M.
影响因子：
3.7
作者：
Xie, Minge;Singh, Kesar;Strawderman, William E.
通讯作者：
Strawderman, William E.
影响因子：
30.8
作者：
Finucane HK;Bulik-Sullivan B;Gusev A;Trynka G;Reshef Y;Loh PR;Anttila V;Xu H;Zang C;Farh K;Ripke S;Day FR;ReproGen Consortium;Schizophrenia Working Group of the Psychiatric Genomics Consortium;RACI Consortium;Purcell S;Stahl E;Lindstrom S;Perry JR;Okada Y;Raychaudhuri S;Daly MJ;Patterson N;Neale BM;Price AL
通讯作者：
Price AL
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
4.5
作者：
Arias-Castro, Ery;Candes, Emmanuel J.;Plan, Yaniv
通讯作者：
Plan, Yaniv