An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.
An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.
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DOI：
10.1038/srep34323
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发表时间：
2016-10-03
影响因子：
4.6
通讯作者：
Zhang S
中科院分区：
文献类型：
--
作者：
Liang X;Wang Z;Sha Q;Zhang S
目前，大多数全基因组关联研究（GWAS）的分析都是在单一表型上进行的。越来越多的证据表明，基因多效性在复杂疾病中是一种普遍现象。因此，仅使用一种单一表型可能会失去识别潜在遗传机制的统计能力。越来越需要开发和应用强大的统计测试来检测多个表型和遗传变异之间的关联。在本文中，我们开发了一个自适应Fisher组合（AFC）的联合分析的关联研究中的多个表型。AFC方法通过使用由数据确定的最佳p值数量合并标准单变量GWAS中获得的p值。我们进行了广泛的模拟，以评估AFC方法的性能，并比较我们的方法的功率与功率的TATES，Tippett的方法，Fisher的组合检验，MANOVA，MultiPhen，和SUMSCORE。我们的模拟研究表明，该方法具有正确的I类错误率，是最强大的测试或最强大的测试相媲美。最后，我们通过分析肺功能研究的全基因组基因分型数据来说明我们提出的方法。
Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher’s Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett’s method, Fisher’s combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.
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