2009/11/01 by Michael E. Goddard, Naomi R. Wray, Klara Verbyla +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Mathematics · #Context (archaeology) #Estimation #Estimator #Genetic Associations and Epidemiology #Genetic Mapping and Diversity in Plants and Animals #Genetic and phenotypic traits in livestock #Genetic association #Quantitative trait locus #SNP #Trait #q-bio.GN #stat.ME
paper · pdf · doi:10.1214/09-sts306
published as Statistical Science 2009, Vol. 24, No. 4, 517-529 · Published in at http://dx.doi.org/10.1214/09-STS306 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2009/11/01 · arxiv created 2010/10/22 · arxiv updated 2010/10/25 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.