Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

Gene-disease association studies based on case-control designs are commonly used to investigate candidate polymorphisms (markers) conferring disease risk. When evaluating a large number of markers, genotyping all the markers on all samples may be inefficient in resource utilization. An alternative two-stage approach is proposed here. In Stage 1 all the markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost-effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Simulation studies show that, when the markers are independent as well as when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the samples in Stage 1 and evaluating the most promising 10% of the markers on the remaining subjects in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations.