Statistical tests for detecting rare variants using variance-stabilising transformations.
Statistical tests for detecting rare variants using variance-stabilising transformations.
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DOI：
10.1111/j.1469-1809.2012.00718.x
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发表时间：
2012-09
影响因子：
1.9
通讯作者：
Fingert JH
中科院分区：
文献类型：
--
作者：
Wang K;Fingert JH
下一代测序在检测复杂人类特征的罕见变异方面前景广阔。由于它们的等位基因频率极低，比例的正态近似不再有效。Fisher精确方法似乎是合适的，但它是保守的。我们研究了各种方差稳定化变换在稀有变异单标记关联分析中的实用性。与比例本身不同，转换后的比例的方差不再取决于比例，这使得将这种转换应用于罕见变异关联分析非常有吸引力。仿真研究表明，基于这种转换的测试是更强大的比费舍尔的精确测试，同时控制I型错误率。基于理论上的考虑和模拟研究的结果，我们建议测试的基础上的Anscombe变换与其他变换的测试。
Next generation sequencing holds great promise for detecting rare variants underlying complex human traits. Due to their extremely low allele frequencies, the normality approximation for a proportion no longer works well. The Fisher’s exact method appears to be suitable but it is conservative. We investigate the utility of various variance-stabilizing transformations in single marker association analysis on rare variants. Unlike a proportion itself, the variance of the transformed proportions no longer depends on the proportion, making application of such transformations to rare variant association analysis extremely appealing. Simulation studies demonstrate that tests based on such transformations are more powerful than the Fisher’s exact test while controlling for type I error rate. Based on theoretical considerations and results from simulation studies, we recommend the test based on the Anscombe transformation over tests with other transformations.
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影响因子：
30.8
作者：
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通讯作者：
Sebat, Jonathan
影响因子：
9.8
作者：
Wu, Michael C.;Kraft, Peter;Lin, Xihong
通讯作者：
Lin, Xihong
影响因子：
9.8
作者：
Li, Bingshan;Leal, Suzanne M.
通讯作者：
Leal, Suzanne M.
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2.1
作者：
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通讯作者：
Ziegler, Andreas
影响因子：
3.7
作者：
MOSTELLER, F;TUKEY, JW
通讯作者：
TUKEY, JW