title: Sample size and power analysis for sparse signal recovery in genome-wide association studies.
abstract: Genome-wide association studies have successfully identified hundreds of novel genetic variants associated with many complex human diseases. However, there is a lack of rigorous work on evaluating the statistical power for identifying these variants. In this paper, we consider sparse signal identification in genome-wide association studies and present two analytical frameworks for detailed analysis of the statistical power for detecting and identifying the disease-associated variants. We present an explicit sample size formula for achieving a given false non-discovery rate while controlling the false discovery rate based on an optimal procedure. Sparse genetic variant recovery is also considered and a boundary condition is established in terms of sparsity and signal strength for almost exact recovery of both disease-associated variants and nondisease-associated variants. A data-adaptive procedure is proposed to achieve this bound. The analytical results are illustrated with a genome-wide association study of neuroblastoma.
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19010, U.S.A. ,  jichun@mail.med.upenn.edu.
authors.forename: Jichun
authors.initials: J
authors.lastname: Xie
authors.forename: T Tony
authors.initials: TT
authors.lastname: Cai
authors.forename: Hongzhe
authors.initials: H
authors.lastname: Li