The accuracy of genomic selection in predicting carcass traits in meat sheep

Genome-wide association studies (GWAS) for quantitative traits in livestock are primarily focused on genomic selection and the prediction of genomic breeding values (GEBV). Genomic selection is a form of marker-assisted selection in which genetic markers covering the whole genome are used so that most QTL are in linkage disequilibrium with at least one marker (Meuwissen et al. 2001). As an alternative to gene discovery, which is genome research focused on mapping and characterising quantitative trait loci (QTL) (Gao et al. 2007) genome-wide association analysis allows prediction of breeding value or phenotype for traits of economic importance using all SNP across the whole genome simultaneously (Lee et al. 2008). Prediction of breeding vale is relevant for the stud sector, for genome assisted selection of breeding animals, whereas prediction of phenotype can be relevant in production systems, for early allocation of animals into specific cohorts. Phenotypic prediction of growth and composition aims to sort animals into homogeneous groups to increase uniformity and profitability (Tedeschi et al. 2004) and therefore helps to satisfy downstream consumers. Meat quality grade, yield grade and growth performance factors explain much of the variation in profit under grid pricing (Greer and Trapp 2000) and the cost penalties when specifications are missed can be large. Trials conducted by (Cox et al. 2006) showed 42% of product did not meet specification in the Australian food service industry. Using phenotypic prediction in the management of animals to increase the proportion that meet specification could increase profitability in sheep meat production by increasing the consistency in meeting consumer requirements. Better allocation of animals to cohorts allows better animal management and can be used to create a relationship between consumers and a product (Walker and Olson 1991) thereby increasing customer retention and satisfaction (Eriksson and Vaghult 2000). In this study we explore the accuracy of predicting phenotype for carcass and growth traits. We use data collected on animal phenotypes and genotypes based on a 50k SNP chip, using a subset of a data to estimate SNP effects and a remaining test set to evaluate the accuracy of genome based prediction of phenotype. Training and test sets were created either randomly; within sire families or across breeds.