Journal
BIOMETRIKA
Volume 98, Issue 2, Pages 273-290Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asr003
Keywords
False discovery rate; False non-discovery rate; High-dimensional data; Multiple testing; Oracle exact recovery
Funding
- National Institutes of Health
- National Science Foundation
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [0854973] Funding Source: National Science Foundation
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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.
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