Journal article
Statistical tests for detecting rare variants using variance-stabilising transformations
Annals of human genetics, Vol.76(5), pp.402-409
09/2012
DOI: 10.1111/j.1469-1809.2012.00718.x
PMCID: PMC3418475
PMID: 22724536
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.
Details
- Title: Subtitle
- Statistical tests for detecting rare variants using variance-stabilising transformations
- Creators
- Kai Wang - Department of Biostatistics, College of Public Health, The University of Iowa, Iowa City, IA 52242, USA. kai-wang@uiowa.eduJohn H Fingert
- Resource Type
- Journal article
- Publication Details
- Annals of human genetics, Vol.76(5), pp.402-409
- Publisher
- England
- DOI
- 10.1111/j.1469-1809.2012.00718.x
- PMID
- 22724536
- PMCID
- PMC3418475
- ISSN
- 0003-4800
- eISSN
- 1469-1809
- Grant note
- R01 EY018825 / NEI NIH HHS R01EY018825 / NEI NIH HHS
- Language
- English
- Date published
- 09/2012
- Academic Unit
- Biostatistics; Ophthalmology and Visual Sciences
- Record Identifier
- 9983980064302771
Metrics
19 Record Views