Potential of in-plant intramuscular fat predictions to enable sheep breeders to incorporate consumer preferences in breeding programmes.

The inclusion of eating quality traits in sheep genetic improvement programmes is desirable. Intramuscular fat (IMF) plays a key role in ensuring consumer satisfaction when eating lamb, but genetic progress for IMF is constrained by a lack of routine data collection. This study investigated the potential for IMF predictor traits to substitute for measured IMF in genetic improvement programmes. Carcass and predicted IMF (near-infrared estimated IMF and marbling score) data were available on 10,113 New Zealand lambs, 1678 of which also had measured chemical IMF on a slice of M. longissimus lumborum on which the predictions of IMF had been made. Genetic antagonisms were observed between carcass lean traits and IMF. The genetic correlation between the predictors and measured IMF approached one, indicating that predictors of IMF can be used in genetic improvement programmes. Through using selection indexes, simultaneous increases in IMF and the existing terminal selection index are possible, provided all traits are measured. This study highlights the importance and potential of predicted IMF to achieve genetic improvement in traits of importance to consumers.

Genetic analysis and genomic selection of stayability and productive life in New Zealand ewes.

Genetic parameters for ewes were estimated for stayability (STAY) and productive life (Prodlife). Records from 658,871 animals from 241 seed stock or ram breeder flocks and one research flock were used to estimate parameters for ram breeder flock STAY and Prodlife. These flocks would have a culling policy based on criteria typical of a commercial enterprise, but including culls based on knowledge such as estimated breeding values. A subset of 35,688 of these animals from 4 ram breeder and one research flock was used to infer the animals that might be culled based on the perspective of a commercial flock as these flocks recorded reasons for culling. These data were used to estimate parameters for commercial flock STAY and Prodlife. Heritability for ram breeder flock STAY until age 3 to 6 yr ranged from 0.07 to 0.09. The heritability of commercial flock STAY was similar in yr 5 and 6, but lower at 0.05 (age 3) and 0.07 (age 4). Heritability of Prodlife for ram breeder flock and commercial flock was respectively 0.10 and 0.13. The genetic correlations between ram breeder flock STAY and commercial flock STAY ranged from 0.20 to 0.99. Both STAY and Prodlife were most correlated genetically with the trait number of lambs born, at about 0.4, and to a lesser degree, live weight up to the age of 12 mo for the traits analyzed with lower genetic correlations estimated for the commercial flocks. Live weight at 18 mo and fleece weight had little or no correlation with STAY and Prodlife. From the data set used to estimate genetic parameters from a ram breeder flock, 4,330 animals were genotyped with the Illumina OvineSNP50 BeadChip and the genotypes used to develop genomic predictions via genomic best linear unbiased prediction. The animals used in the training data set were multibreed, but were heavily Romney breed based. The genomic prediction accuracy for ram breeder flock STAY, according to breed, was dependent on sample size and ranged from 0.4 to 0.44 for Romney and 0.26 to 0.4 for Coopworth. Accuracies for Perendale and Composites, for which sample sizes were less, were more variable. This study has derived genetic parameters for STAY and Prodlife and investigated genomic predictions for these traits. As these traits are of low heritability, sex limited and expressed late in life genomic selection will be useful.

Genomic prediction of breeding values in the New Zealand sheep industry using a 50K SNP chip.

The aim of genomic prediction is to predict breeding value from genomic data. We describe the development of genomic prediction equations and accuracies for molecular breeding values (MBV) for industry use, focusing on the methodology used to deal with predictions for the New Zealand sheep population structure. This is made up of a mixture of pure and crossbred animals, but principally Romney based. In particular, we used pedigree-based EBV for 8 traits (weaning weight as a direct effect, weaning weight as a maternal effect, live weight at 8 mo, live weight at 12 mo, greasy fleece weight at 12 mo, lamb fleece weight, adult fleece weight, and number of lambs born) and Illumina OvineSNP50 BeadChip genotypes from 13,420 animals to investigate BLUP with different genomic relationship matrices (GRM) based on SNP markers and to investigate varying sets of older animals (training sets) to predict the MBV of younger animals (validation sets). The GRM tested included modifications to account for allele frequency differences between breeds, rescaling so that the mean GRM is equal to the mean of the traditional pedigree numerator relationship matrix A: , and combining of the GRM with A: using a convex combination with a weight estimated by maximizing a conditional restricted likelihood. We found that these modifications were beneficial and recommend using a breed-adjusted GRM combined with A: . Training data sets with Romney, Coopworth, and Perendale animals all together usually predicted better than using just a pure breed training data set for all traits. But predictions for the breed Perendale were more accurate with a Perendale training set for 3 of the 8 traits. We concluded that using a mixed-breed training set for all combinations of traits and breeds was best but advise that increasing the number of Perendale animals genotyped should be a priority to increase the MBV accuracies obtained for that breed.