Sample size and power analysis for sparse signal recovery in genome-wide association studies.
Journal Article (Journal Article)
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.
Full Text
Duke Authors
Cited Authors
- Xie, J; Cai, TT; Li, H
Published Date
- June 2011
Published In
Volume / Issue
- 98 / 2
Start / End Page
- 273 - 290
PubMed ID
- 23049128
Pubmed Central ID
- PMC3419390
International Standard Serial Number (ISSN)
- 0006-3444
Digital Object Identifier (DOI)
- 10.1093/biomet/asr003
Language
- eng
Conference Location
- England