title: Optimal designs for two-stage genome-wide association studies.
abstract: Genome-wide association (GWA) studies require genotyping hundreds of thousands of markers on thousands of subjects, and are expensive at current genotyping costs. To conserve resources, many GWA studies are adopting a staged design in which a proportion of the available samples are genotyped on all markers in stage 1, and a proportion of these markers are genotyped on the remaining samples in stage 2. We describe a strategy for designing cost-effective two-stage GWA studies. Our strategy preserves much of the power of the corresponding one-stage design and minimizes the genotyping cost of the study while allowing for differences in per genotyping cost between stages 1 and 2. We show that the ratio of stage 2 to stage 1 per genotype cost can strongly influence both the optimal design and the genotyping cost of the study. Increasing the stage 2 per genotype cost shifts more of the genotyping and study cost to stage 1, and increases the cost of the study. This higher cost can be partially mitigated by adopting a design with reduced power while preserving the false positive rate or by increasing the false positive rate while preserving power. For example, reducing the power preserved in the two-stage design from 99 to 95% that of the one-stage design decreases the two-stage study cost by approximately 15%. Alternatively, the same cost savings can be had by relaxing the false positive rate by 2.5-fold, for example from 1/300,000 to 2.5/300,000, while retaining the same power.
authors.affiliation: Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. askol@uchicago.edu
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Skol
authors.forename: Laura J
authors.initials: LJ
authors.lastname: Scott
authors.forename: Gonçalo R
authors.initials: GR
authors.lastname: Abecasis
authors.forename: Michael
authors.initials: M
authors.lastname: Boehnke
mesh_terms: D005189:False Positive Reactions; D005819:Genetic Markers / Q000235:genetics; D015894:Genome, Human / Q000235:genetics*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D017422:Sequence Analysis, DNA / Q000191:economics
chemical_list: D005819:Genetic Markers