Estimating the number of true discoveries in genome‐wide association studies | Zendy

Lee Woojoo | Zendy; Gusnanto A. | Zendy; Salim A. | Zendy; Magnusson P. | Zendy; Sim Xueling | Zendy; Tai E.S. | Zendy; Pawitan Y. | Zendy

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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.

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