Statistical Tests for Detecting Rare Variants Using Variance‐Stabilising Transformations
Year: 2012
Type: article
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.
Source: Annals of Human Genetics
Authors: Kai Wang, John H. Fingert
Institution: University of Iowa
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Funder: National Eye Institute
OpenAlex ID: W1831738118