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


  • eng

Conference Location

  • England