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Context: Genetic evaluation of Australian sheep is conducted for millions of animals for more than 100 traits. Currently, the Australian sheep genetic-analysis software (OVIS) applies a pre-adjustment of phenotype for fixed effects rather than fitting all fixed and random effects jointly in a linear mixed model to estimate breeding values. However, the current correction factors might be outdated and potential interactions among fixed effects not accounted for, which could lead to bias in estimated breeding values (EBVs). Aims: This study aimed to assess whether correction factors used in OVIS for early bodyweights recorded in meat sheep breeds are appropriate, so as to explore whether the pre-adjustment method is still suitable and how this compares with a linear mixed model, and to estimate the significance of interactions between fixed effects. Methods: Correlations between EBVs from different models and regression slopes from forward prediction were calculated, using weaning-weight data on 365 956 White Suffolk and 370 649 Poll Dorset sheep and post-weaning weight data on 292 538 White Suffolk and 303 864 Poll Dorset sheep. Key results: The current OVIS procedure resulted in regression slopes of progeny performance on sire EBVs (averaged over breeds) of 0.37 and 0.35 for weaning and post-weaning weights respectively. Updated pre-adjustment factors improved the regression slopes to 0.40 and 0.38 respectively. Analysis with a linear mixed model produced significantly better regression slopes than did pre-adjustment (0.47 and 0.44 respectively). Further, regression slopes obtained from the linear mixed model with flock by sex by age interaction averaged over breeds were 0.48 for weaning and 0.46 for post-weaning weight respectively, which was a moderate improvement over the current OVIS model. Including a flock by sex by age interaction produced significantly better improvement in Poll Dorset sheep and modest improvement in White Suffolk sheep than did linear mixed model without interaction. Conclusions: Using a linear mixed model with a flock by sex by age interaction significantly improves the utility of estimated breeding values for weaning and post-weaning weight in predicting the performance of future progeny. Implications: To account for systematic environmental effects, a linear mixed model should be used in OVIS to jointly estimate the fixed effects and EBVs. Genetic evaluation of Australian sheep is conducted for millions of animals for more than 100 traits. Prediction of genetic merit of animals includes correcting observed phenotypes for environmental effects to allow fair genetic comparisons among animals, and is conducted by using old sheep data solutions because it was hard to fit these effects in each analysis. In the current scenario with more computing power, fitting all effects together and accounting for flock by sex by age interaction improves the utility of the model in predicting the performance of future progeny for weight traits. [ABSTRACT FROM AUTHOR]

A model of the sheep breeding industry with nucleus flocks, multiplier flocks and commercial sheep flocks was used to examine the value of genomic selection. The model reflected a dual-purpose Merino breeding objective, with genomic information improving selection accuracy by 39% for rams at 6 months of age and by 17% at 18 months. The current level of net dollar benefit to the sheep industry from selection, but without genomic testing, can be improved by 10-14% for a closed three-tiered breeding structure with rams used at 18 months. If the rams are first used at 6-7 months then the dollar gains can be improved by 15-17%, since genomic information can provide proportionately greater gains for young animals that have limited phenotypic information. In a two-tiered breeding system, with nucleus flocks selling rams direct to commercial producers, rather than through multiplier flocks, the dollar gains to industry from genomic testing increased to ~12-13% for rams bred at 18 months, and 20-22% if nucleus rams are used at 6-7 months. The optimal structure requires two-stage selection, with an initial selection based on information available without genomic testing, to limit the cost of testing to only the superior rams. However, the optimum proportion of rams tested depends on the system and the cost of testing. In order to recover the cost of genomic testing, the nucleus flocks must recover up to 5% of the extra genetic gain as extra profit from sale of rams to commercial sheep producers. A model of the sheep breeding industry examined the value of genomic information in sheep breeding programs. Benefits can be obtained by both the traditional three-tiered wool industry structure and studs using genomic information and selling directly to commercial producers, with most of the benefits retained by the commercial producers. The proportion of nucleus rams that use genetic tests must be optimised to achieve the maximum economic benefit. [ABSTRACT FROM AUTHOR]

Female reproductive technologies such as multiple ovulation and embryo transfer (MOET) and juvenile in vitro fertilisation and embryo transfer (JIVET) can produce multiple offspring per mating in sheep and cattle. In breeding programs this allows for higher female selection intensity and, in the case of JIVET, a reduction in generation interval, resulting in higher rates of genetic gain. Low selection accuracy of young females entering JIVET has often dissuaded producers from using this technology. However, genomic selection (GS) could increase selection accuracy of candidates at a younger age to help increase rates of genetic gain. This increase might vary for different traits in multiple trait breeding programs depending on genetic parameters and the practicality of recording, particularly for hard to measure traits. This study used both stochastic (animals) and deterministic (GS) simulation to evaluate the effect of reproductive technologies on the genetic gain for various traits in sheep breeding programs, both with and without GS. Optimal contribution selection was used to manage inbreeding and to optimally assign reproductive technologies to individual selection candidates. Two Australian sheep industry indexes were used - a terminal sire index that focussed on growth and carcass traits (the 'Lamb 2020' index) and a Merino index that focuses on wool traits, bodyweight, and reproduction (MP+). We observed that breeding programs using artificial insemination or natural mating (AI/N) + MOET, compared with AI/N alone, yielded an extra 39% and 27% genetic gain for terminal and Merino indexes without GS, respectively. However, the addition of JIVET to AI/N + MOET without GS only yielded an extra 1% genetic gain for terminal index and no extra gain in the Merino index. When GS was used in breeding programs, we observed AI/N + MOET + JIVET outperformed AI/N + MOET by 21% and 33% for terminal and Merino indexes, respectively. The implementation of GS increased genetic gain where reproductive technologies were used by 9-34% in Lamb 2020 and 37-98% in MP+. Individual trait response to selection varied in each breeding program. The combination of GS and reproductive technologies allowed for greater genetic gain in both indexes especially for hard to measure traits, but had limited effect on the traits that already had a large amount of early age records. [ABSTRACT FROM AUTHOR]

Genomic selection could be useful in sheep-breeding programs, especially if rams and ewes are first mated at an earlier age than is the current industry practice. However, young-ewe (1 year old) fertility rates are known to be lower and more variable than those of mature ewes. The aim of the present study was to evaluate how young-ewe fertility rate affects risk and expected genetic gain in Australian sheep-breeding programs that use genomic information and select ewes and rams at different ages. The study used stochastic simulation to model different flock age structures and young-ewe fertility levels with and without genomic information for Merino and maternal sheep-breeding programs. The results from 10 years of selection were used to compare breeding programs on the basis of the mean and variation in genetic gain. Ram and ewe age, availability of genomic information on males and young-ewe fertility level all significantly (P < 0.05) affected expected genetic gain. Higher young-ewe fertility rates significantly increased expected genetic gain. Low fertility rate of young ewes (10%) resulted in net genetic gain similar to not selecting ewes until they were 19 months old and did not increase breeding-program risk, as the likelihood of genetic gain being lower than the range of possible solutions from a breeding program with late selection of both sexes was zero. Genomic information was of significantly (P < 0.05) more value for 1-year-old rams than for 2-year-old rams. Unless genomic information was available, early mating of rams offered no greater gain in Merino breeding programs and increased breeding-program risk. It is concluded that genomic information decreases the risk associated with selecting replacements at 7 months of age. Genetic progress is unlikely to be adversely affected if fertility levels above 10% can be achieved. Whether the joining of young ewes is a viable management decision for a breeder will depend on the fertility level that can be achieved in their young ewes and on other costs associated with the early mating of ewes. [ABSTRACT FROM AUTHOR]