Comparison of two-phase analyses for case-control genetic association studies.

To test for genetic association between a marker and a complex disease using a case-control design, Cochran-Armitage trend tests (CATTs) and Pearson's chi-square test are often employed. Both tests are genotype-based. Song and Elston (Statist. Med. 2006; 25:105-126) introduced the Hardy-Weinberg disequilibrium trend test and combined it with CATT to test for association. Compared to using a single statistic to test for case-control genetic association (referred to as single-phase analysis), two-phase analysis is a new strategy in that it employs two test statistics in one analysis framework, each statistic using all available case-control data. Two such two-phase analysis procedures were studied, in which Hardy-Weinberg equilibrium (HWE) in the population is a key assumption, although the procedures are robust to moderate departure from HWE. Our goal in this article is to study a new two-phase procedure and compare all three two-phase analyses and common single-phase procedures by extensive simulation studies. For illustration, the results are applied to real data from two case-control studies. On the basis of the results, we conclude that with an appropriate choice of significance level for the analysis in phase 1, some two-phase analyses could be more powerful than commonly used test statistics.