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Statistical Methods for the Analysis of Genetic Association Studies

Statistical Methods for the Analysis of Genetic Association Studies This paper applies a retrospective logistic regression model (Prentice, 1976) using a sandwich variance estimator (White, 1982; Zeger et al. 1985) to genetic association studies in which alleles are treated as dependent variables. The validity of switching the positions of allele and trait variables in the regression model is ensured by the invariance property of the odds ratio. The approach is shown to be able to accommodate many commonly seen designs, matched or unmatched alike, having either binary or quantitative traits. The resultant score statistic has potentially higher power than those that have previously appeared in the genetics literature. As a regression model in general, this approach may also be applied to incorporate covariates. Numerical examples implemented with standard software are presented. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Human Genetics Wiley

Statistical Methods for the Analysis of Genetic Association Studies

Zou, G. Y.
Annals of Human Genetics , Volume 70 (2) – Jan 1, 2006
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References (148)

Publisher
Wiley
Copyright
Copyright © 2006 Wiley Subscription Services
ISSN
0003-4800
eISSN
1469-1809
DOI
10.1111/j.1529-8817.2005.00213.x
pmid
16626336
Publisher site
See Article on Publisher Site

Abstract

This paper applies a retrospective logistic regression model (Prentice, 1976) using a sandwich variance estimator (White, 1982; Zeger et al. 1985) to genetic association studies in which alleles are treated as dependent variables. The validity of switching the positions of allele and trait variables in the regression model is ensured by the invariance property of the odds ratio. The approach is shown to be able to accommodate many commonly seen designs, matched or unmatched alike, having either binary or quantitative traits. The resultant score statistic has potentially higher power than those that have previously appeared in the genetics literature. As a regression model in general, this approach may also be applied to incorporate covariates. Numerical examples implemented with standard software are presented.

Journal

Annals of Human Genetics – Wiley

Published: Jan 1, 2006

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