Journal article

Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data

Sang Hong Lee, Julius HJ van der Werf, Ben J Hayes, Michael E Goddard, Peter M Visscher

PLOS GENETICS | PUBLIC LIBRARY SCIENCE | Published : 2008

Abstract

Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogene..

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University of Melbourne Researchers