Optimal two-stage genome-wide association designs based on false discovery rate

摘要

Genome-wide association studies are likely to be conducted in large scale in the near future. In such studies, searching over hundreds of thousands of markers for the few ones that are associated with disease brings out the multiple-hypothesis testing problem in its severe form. We explore, in a two-stage design, how the use of false discovery rate (FDR) can alleviate the burden of a prohibitively strict significance level for single marker tests and still control the number of false positive findings, when there is more than one causal variant. FDR is the expected proportion of false positives among all significant findings.