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Updated: Aug 9, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Statistical methods for the analysis of genetic association studies
1Robarts Clinical Trials, Robarts Research Institute, PO Box 5015, 100 Perth Drive, London, Ontario, Canada N6A 5K8. gzou@robarts.ca
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.
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