Predicting phenotypes of beef eating quality traits - Forutan, Mehrnush; Lynn, Andrew; Aliloo, Hassan; Clark, Samuel A.; McGilchrist, Peter; Polkinghorne, Rod; Hayes, Ben J. (Frontiers in Genetics, 2023)
Genomic predictions of phenotypes for beef eating quality traits are of interest, for example to sort carcasses into consumer or market value categories, or to allocate animals to longer and more expensive feeding regimes as they enter the feedlot if they are predicted to have higher eating quality. Such predictions could include genetic effects (breed effects, heterosis and breeding value), predicted from genetic markers, as well as fixed effects such as days aged and carcass weight, hump height and ossification and hormone growth promotant (HGP) status. Here we assessed accuracy of predictions for five eating quality traits (tenderness, juiciness, flavour, overall liking and MQ4) in striploins from 1701 animals from a wide variety of backgrounds, including Bos indicus and Bos taurus breeds using genotypes and simple fixed effects including days aged and carcass weight. The genetic components of the predictions were predicted based on 709k single nucleotide polymorphism (SNP) using BayesR model, which assumes some markers may have a moderate to large effect. Fixed effects in the prediction included principal components of the genomic relationship matrix, to account for breed effects, as well as heterosis, days aged and carcass weight. A model which allowed breed effects and heterosis to be captured in the SNP effects (e.g., not explicitly fitting these effects) gave slightly higher accuracies (0.43-0.50) compared to when these effects were explicitly fitted as fixed effects (0.42-0.49), perhaps because breed effects when explicitly fitted were estimated with more error than when incorporated into the (random) SNP effects. Adding estimates of effects of days aged and carcass weight did not increase the accuracy of phenotype predictions in our particular analysis, perhaps because of the uniformity of these effects in our data set. The accuracy of prediction for beef eating quality traits was sufficiently high that such predictions could be useful in sorting animals or carcasses as they enter the processing plant, to enable optimal supply chain value extraction by targeting markets with different quality. The BayesR predictions identified several novel genes potentially associated with beef eating quality.