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Armidale, Australia

Granleese T.,Sheep Cooperative Research Center | Granleese T.,University of New England of Australia | Clark S.A.,University of New England of Australia | Swan A.A.,Sheep Cooperative Research Center | And 3 more authors.
Genetics Selection Evolution | Year: 2015

Background: Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro embryo production and embryo transfer (JIVET) can boost rates of genetic gain but they can also increase rates of inbreeding. Inbreeding can be managed using the principles of optimal contribution selection (OCS), which maximizes genetic gain while placing a penalty on the rate of inbreeding. We evaluated the potential benefits and synergies that exist between genomic selection (GS) and reproductive technologies under OCS for sheep and cattle breeding programs. Methods: Various breeding program scenarios were simulated stochastically including: (1) a sheep breeding program for the selection of a single trait that could be measured either early or late in life; (2) a beef breeding program with an early or late trait; and (3) a dairy breeding program with a sex limited trait. OCS was applied using a range of penalties (severe to no penalty) on co-ancestry of selection candidates, with the possibility of using multiple ovulation and embryo transfer (MOET) and/or juvenile in vitro embryo production and embryo transfer (JIVET) for females. Each breeding program was simulated with and without genomic selection. Results: All breeding programs could be penalized to result in an inbreeding rate of 1 % increase per generation. The addition of MOET to artificial insemination or natural breeding (AI/N), without the use of GS yielded an extra 25 to 60 % genetic gain. The further addition of JIVET did not yield an extra genetic gain. When GS was used, MOET and MOET + JIVET programs increased rates of genetic gain by 38 to 76 % and 51 to 81 % compared to AI/N, respectively. Conclusions: Large increases in genetic gain were found across species when female reproductive technologies combined with genomic selection were applied and inbreeding was managed, especially for breeding programs that focus on the selection of traits measured late in life or that are sex-limited. Optimal contribution selection was an effective tool to optimally allocate different combinations of reproductive technologies. Applying a range of penalties to co-ancestry of selection candidates allows a comprehensive exploration of the inbreeding vs. genetic gain space. © 2015 Granleese et al. Source

Newton J.E.,University of New England of Australia | Newton J.E.,Animal Genetics and Breeding Unit | Newton J.E.,CSIRO | Brown D.J.,Animal Genetics and Breeding Unit | And 3 more authors.
Animal Production Science | Year: 2014

The aims of this study were to quantify the relationship between age of first oestrus and yearling reproductive performance in maternal-cross ewes in the Information Nucleus Flock data and to estimate genetic and phenotypic correlations between early and later reproductive performance defined as three ages, yearling, hogget and adult in both Merino and maternal-cross ewes. Information on 2218 yearling records, 2047 hogget records and 910 age of first oestrus records were used in the analysis of maternal-cross ewes, whereas 3286 hogget and 2518 adult reproductive records were used in analysis of Merino ewes. Heritability estimates for yearling reproductive performance in maternal-cross ewes ranged from 0.08 ± 0.09 for ewe fecundity to 0.16 ± 0.05 for number of lambs born and were generally higher than hogget heritability estimates for both maternal-cross and Merino ewes. Age at first oestrus was found to have a low heritability, 0.02 with standard errors of 0.07 and 0.06 with and without weight fitted as a covariate. Genetic correlations between age at first oestrus with and without weight fitted as a covariate and yearling reproductive performance were positive, ranging from 0.07 ± 0.49 with lamb survival to 0.94 ± 0.39 with number of lambs born, which was unexpected. Correlations between traits from the same age class were high in both breed groups. Genetic correlations between yearling and hogget performance in maternal-cross ewes were generally lower than one, ranging from 0.46 ± 0.68 for lamb survival and 0.79 ± 0.50 for fertility suggesting that yearling and later reproductive performance are related but genetically different traits. In Merino ewes, the genetic correlations between hogget and adult performance followed a similar pattern. The small number of records in this study generated high standard errors for estimates, which restricts the conclusions that can be drawn. Overall, this study supports current practice used by 'Sheep Genetics', the Australian genetic evaluation system for sheep, in considering yearling reproductive performance as a trait separate from later parities for genetic evaluation purposes. © CSIRO 2014. Source

Bolormaa S.,Australian Department of Primary Industries and Fisheries | Pryce J.E.,Australian Department of Primary Industries and Fisheries | Kemper K.,University of Melbourne | Savin K.,Australian Department of Primary Industries and Fisheries | And 13 more authors.
Journal of Animal Science | Year: 2013

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction [BayesR and genomic BLUP (GBLUP)] either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identifed many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait. © 2013 American Society of Animal Science. All rights reserved. Source

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