Article

Statistical Tests for Detecting Rare Variants Using Variance-Stabilising Transformations

Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA 52242, USA.
Annals of Human Genetics (Impact Factor: 1.93). 06/2012; 76(5):402-9. DOI: 10.1111/j.1469-1809.2012.00718.x
Source: PubMed

ABSTRACT 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-stabilising 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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Available from: John H Fingert, Jul 31, 2015
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    ABSTRACT: In a recent paper in this journal, the use of variance-stabilising transformation techniques was proposed to overcome the problem of inadequacy in normality approximation when testing association for a low-frequency variant in a case-control study. It was shown that tests based on the variance-stabilising transformations are more powerful than Fisher's exact test while controlling for type I error rate. Earlier in the journal, another study had shown that the likelihood ratio test (LRT) is superior to Fisher's exact test, Wald's test, and Pearson's χ(2) test in testing association for low-frequency variants. Thus, it is of interest to make a direct comparison between the LRT and the tests based on the variance-stabilising transformations. In this commentary, we show that the LRT and the variance-stabilising transformation-based tests have comparable power greater than Fisher's exact test, Wald's test, and Pearson's χ(2) test.
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