Genetic model selection in two-phase analysis for case-control association studies.
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.
Zheng
Gang
G
Office of Biostatistics Research, National Heart, Lung and Blood Institute, 6701 Rockledge Drive, Bethesda, MD 20892-7931, USA.
Ng
Hon Keung Tony
HK
D000465:Algorithms; D000483:Alleles; D016022:Case-Control Studies; D003198:Computer Simulation; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease / Q000145:classification / Q000453:epidemiology*; D005828:Genetics, Population / Q000379:methods; D005838:Genotype; D006801:Humans; D040582:Inheritance Patterns; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide / Q000502:physiology; D012107:Research Design / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods; D015983:Selection Bias