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Biostatistics Advance Access originally published online on November 13, 2007
Biostatistics 2008 9(3):391-399; doi:10.1093/biostatistics/kxm039
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

Genetic model selection in two-phase analysis for case–control association studies

Gang Zheng

Office of Biostatistics Research, National Heart, Lung and Blood Institute, 6701 Rockledge Drive, Bethesda, MD 20892-7931, USA

Hon Keung Tony Ng*

Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue, PO Box 750332, Dallas, TX 75275-0332, USA
ngh{at}mail.smu.edu

* To whom correspondence should be addressed.

The Cochran–Armitage trend test (CATT) is well suited for testing association between a marker and a disease in case–control studies. When the underlying genetic model for the disease is known, the CATT optimal for the genetic model is used. For complex diseases, however, the genetic models of the true disease loci are unknown. In this situation, robust tests are preferable. We propose a two-phase analysis with model selection for the case–control design. In the first phase, we use the difference of Hardy–Weinberg disequilibrium coefficients between the cases and the controls for model selection. Then, an optimal CATT corresponding to the selected model is used for testing association. The correlation of the statistics used for selection and the test for association is derived to adjust the two-phase analysis with control of the Type-I error rate. The simulation studies show that this new approach has greater efficiency robustness than the existing methods.

Keywords: Cochran–Armitage trend test; Disease risk; Efficiency robustness; Hardy–Weinberg disequilibrium; SNP

Received April 16, 2007; revised July 25, 2007; revised October 1, 2007; accepted for publication October 12, 2007.


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