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Improving the Signal‐to‐Noise ratio in genome‐wide association studies
Author(s) -
Martin Lisa J.,
Gao Guimin,
Kang Guolian,
Fang Yixin,
Woo Jessica G.
Publication year - 2009
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.20469
Subject(s) - bonferroni correction , false positive paradox , statistical power , genome wide association study , type i and type ii errors , multiple comparisons problem , genome , computational biology , genetic association , genetics , false discovery rate , statistical hypothesis testing , biology , computer science , statistics , machine learning , mathematics , single nucleotide polymorphism , gene , genotype
Genome‐wide association studies employ hundreds of thousands of statistical tests to determine which regions of the genome may likely harbor disease‐causing alleles. Such large‐scale testing simultaneously requires stringent control over type I error and maintenance of sufficient power to detect true associations. These contradictory goals have led some researchers beyond Bonferroni correction of P ‐values to an exploration of methods to improve the detection of a few true effects in the presence of many unassociated loci. This article reviews how Genetic Analysis Workshop 16 Group 5 investigators proposed to adjust for multiple tests while simultaneously using information about the structure of the genome to improve the detection of true positives. Genet. Epidemiol . 33 (Suppl. 1):S29–S32, 2009. © 2009 Wiley‐Liss, Inc.