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Sample size and power analysis for sparse signal recovery in genome-wide association studies.

Publication ,  Journal Article
Xie, J; Cai, TT; Li, H
Published in: Biometrika
June 2011

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.

Duke Scholars

Published In

Biometrika

DOI

ISSN

0006-3444

Publication Date

June 2011

Volume

98

Issue

2

Start / End Page

273 / 290

Location

England

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics
 

Citation

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Xie, J., Cai, T. T., & Li, H. (2011). Sample size and power analysis for sparse signal recovery in genome-wide association studies. Biometrika, 98(2), 273–290. https://doi.org/10.1093/biomet/asr003
Xie, Jichun, T Tony Cai, and Hongzhe Li. “Sample size and power analysis for sparse signal recovery in genome-wide association studies.Biometrika 98, no. 2 (June 2011): 273–90. https://doi.org/10.1093/biomet/asr003.
Xie, Jichun, et al. “Sample size and power analysis for sparse signal recovery in genome-wide association studies.Biometrika, vol. 98, no. 2, June 2011, pp. 273–90. Pubmed, doi:10.1093/biomet/asr003.
Journal cover image

Published In

Biometrika

DOI

ISSN

0006-3444

Publication Date

June 2011

Volume

98

Issue

2

Start / End Page

273 / 290

Location

England

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics