Optimum two-stage designs in case–control association studies using false discovery rate

Genetic association studies using case–control designs are often done to identify loci associated with disease susceptibility. These studies are often expensive to perform, due to a large number of genetic markers. Several types of two-stage designs are proposed and used from the point of cost effectiveness. We proposed to control the false discovery rate for multiple-testing correction in two-stage designs, using optimal sample sizes and criteria for selecting markers associated with a disease in each stage to minimize the cost of genotyping. The expected power and cost of two-stage designs were compared with those of one-stage designs, under the assumptions that the genetic markers are independent and total sample size is fixed. The results showed that the proposed two-stage procedure usually reduced the cost of genotyping by 40–60%, with a power similar to that of the one-stage designs. In addition, the sample size and selection criteria, which are optimized parameters, are defined as a function of a prior probability that marker–disease association is true. So, the effects of mis-specification of a prior probability on efficiency were also considered.