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Title: Estimating the number of true discoveries in genome-wide association studies
Authors: Lee, W.
Gusnanto, A.
Salim, A. 
Magnusson, P.
Sim, X. 
Tai, E.S.
Pawitan, Y.
Keywords: Complex diseases
False discovery rate
High-throughput data
Statistical genetics
Issue Date: May-2012
Citation: Lee, W., Gusnanto, A., Salim, A., Magnusson, P., Sim, X., Tai, E.S., Pawitan, Y. (2012-05). Estimating the number of true discoveries in genome-wide association studies. Statistics in Medicine 31 (11-12) : 1177-1189. ScholarBank@NUS Repository.
Abstract: Recent genome-wide association studies have reported the discoveries of genetic variants of small to moderate effects. However, most studies of complex diseases face a great challenge because the number of significant variants is less than what is required to explain the disease heritability. A new approach is needed to recognize all potential discoveries in the data. In this paper, we present a practical model-free procedure to estimate the number of true discoveries as a function of the number of top-ranking SNPs together with the confidence bounds. This approach allows a practical methodology of general utility and produces relevant statistical quantities with simple interpretation. © 2011 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
ISSN: 02776715
DOI: 10.1002/sim.4391
Appears in Collections:Staff Publications

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