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November 2009 Estimating Effects and Making Predictions from Genome-Wide Marker Data
Michael E. Goddard, Naomi R. Wray, Klara Verbyla, Peter M. Visscher
Statist. Sci. 24(4): 517-529 (November 2009). DOI: 10.1214/09-STS306


In genome-wide association studies (GWAS), hundreds of thousands of genetic markers (SNPs) are tested for association with a trait or phenotype. Reported effects tend to be larger in magnitude than the true effects of these markers, the so-called “winner’s curse.” We argue that the classical definition of unbiasedness is not useful in this context and propose to use a different definition of unbiasedness that is a property of the estimator we advocate. We suggest an integrated approach to the estimation of the SNP effects and to the prediction of trait values, treating SNP effects as random instead of fixed effects. Statistical methods traditionally used in the prediction of trait values in the genetics of livestock, which predates the availability of SNP data, can be applied to analysis of GWAS, giving better estimates of the SNP effects and predictions of phenotypic and genetic values in individuals.


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Michael E. Goddard. Naomi R. Wray. Klara Verbyla. Peter M. Visscher. "Estimating Effects and Making Predictions from Genome-Wide Marker Data." Statist. Sci. 24 (4) 517 - 529, November 2009.


Published: November 2009
First available in Project Euclid: 20 April 2010

zbMATH: 1329.62423
MathSciNet: MR2779341
Digital Object Identifier: 10.1214/09-STS306

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.24 • No. 4 • November 2009
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