Programs for calculating the statistical powers of detecting susceptibility genes in case-control studies based on multistage designs.

Abstract

A two-stage association study is the most commonly used method among multistage designs to efficiently identify disease susceptibility genes. Recently, some SNP studies have utilized more than two stages to detect disease genes. However, there are few available programs for calculating statistical powers and positive predictive values (PPVs) of arbitrary n-stage designs. We developed programs for a multistage case-control association study using R language. In our programs, input parameters include numbers of samples and candidate loci, genome-wide false positive rate and proportions of samples and loci to be selected at the k-th stage (k=1,..., n). The programs output statistical powers, PPVs and numbers of typings in arbitrary n-stage designs. The programs can contribute to prior simulations under various conditions in planning a genome-wide association study.