Two‐Stage Designs for Gene–Disease Association Studies with Sample Size Constraints

• Published in: Biometrics Vol. 60; no. 3; pp. 589 - 597
• Main Authors: Satagopan, Jaya M., Venkatraman, E. S., Begg, Colin B.
• Format: Journal Article
• Language: English
• Published: 350 Main Street , Malden , MA 02148 , U.S.A , and P.O. Box 1354, 9600 Garsington Road , Oxford OX4 2DQ , U.K Blackwell Publishing 01.09.2004; International Biometric Society; Blackwell Publishing Ltd
• Subjects: Biometrics; Biometry; Case-Control Studies; cost effectiveness; Design evaluation; Epidemiology; Genetic diseases; Genetic Diseases, Inborn - etiology; Genetic Diseases, Inborn - genetics; Genetic disorders; Genetic loci; Genetic Markers; genotyping; guidelines; Haplotypes; Human genetics; Humans; Linkage Disequilibrium; Medical genetics; Optimal design; Order statistic; Power; Probabilities; risk; Risk Factors; Sample Size; Simulation
• ISSN: 0006-341X; 1541-0420
• DOI: 10.1111/j.0006-341X.2004.00207.x

Gene–disease association studies based on case–control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two‐stage method to identify disease‐susceptibility markers. In the first stage all 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. Using simulations we show that, when the markers are independent and 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. The power of the two‐stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease‐susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near‐optimal power while resulting in a 45% decrease in the total number of marker evaluations.