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Estimating the number of true discoveries in genome‐wide association studies
Author(s) -
Lee Woojoo,
Gusnanto A.,
Salim A.,
Magnusson P.,
Sim Xueling,
Tai E.S.,
Pawitan Y.
Publication year - 2011
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4391
Subject(s) - genetic association , ranking (information retrieval) , genome wide association study , computer science , heritability , computational biology , single nucleotide polymorphism , statistics , biology , machine learning , evolutionary biology , mathematics , genetics , genotype , gene
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. Copyright © 2011 John Wiley & Sons, Ltd.