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Statistical Tests for Detecting Rare Variants Using Variance‐Stabilising Transformations
Author(s) -
Wang Kai,
Fingert John H.
Publication year - 2012
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/j.1469-1809.2012.00718.x
Subject(s) - variance (accounting) , normality , exact test , type i and type ii errors , transformation (genetics) , multiple comparisons problem , analysis of variance , statistics , variance components , mathematics , statistical hypothesis testing , omnibus test , biology , algorithm , computer science , genetics , gene , accounting , business
Summary 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.