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Optimal mating strategies to manage a heterozygous advantage major gene in sheep
- J. Raoul, I. Palhière, J. M. Astruc, A. Swan, J. M. Elsen
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Some mutations (or ‘major genes’) have a desirable effect in heterozygous carriers but an undesirable effect in homozygous carriers. When these mutations affect a trait of significant economic importance, their eradication, depending on their effect and frequency, may be counterproductive. This is especially the case of major genes affecting the ovulation rate and thus the prolificacy in meat sheep populations. To manage such situations, a mating design based on the major genotypes of reproducers has to be optimized. Both the effect of the major gene and the cost of genotyping candidates at this locus influence the expected genetic progress and profitability of the breeding plan. The aim of this study was to determine the optimal combination of matings that maximizes profitability at the level of the whole population (nucleus + commercial flocks). A deterministic model was developed and, using sequential quadratic programming methodology, the optimal strategy (optimal combination of matings) that maximized the economic gain achieved by the population across a range of genotype effects and genotyping costs was determined. The optimal strategy was compared with simpler and more practical strategies based on a limited number of parental genotype mating types. Depending on the genotype effect and genotyping costs, the optimal strategy varied, such that either the heterozygous frequency and/or polygenic gain was maximized with a large number of animals genotyped, or when genotyping costs were higher, the optimization led to lower heterozygous frequency and/or polygenic gain with fewer animals genotyped. Comparisons showed that some simpler strategies were close to the optimal strategy. An overlapping model was then derived as an application of the real case of the French Lacaune meat sheep OVI-TEST breeding program. Results showed that a practical strategy based on mating non-carriers to heterozygous carriers was only slightly less effective than the optimal strategy, with a reduction in efficiency from 3% to 8%, depending on the genotyping costs. Based on only two different parental genotype mating types, this strategy would be easy to implement.
Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program
- F. Shumbusho, J. Raoul, J. M. Astruc, I. Palhiere, S. Lemarié, A. Fugeray-Scarbel, J. M. Elsen
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Recent genomic evaluation studies using real data and predicting genetic gain by modeling breeding programs have reported moderate expected benefits from the replacement of classic selection schemes by genomic selection (GS) in small ruminants. The objectives of this study were to compare the cost, monetary genetic gain and economic efficiency of classic selection and GS schemes in the meat sheep industry. Deterministic methods were used to model selection based on multi-trait indices from a sheep meat breeding program. Decisional variables related to male selection candidates and progeny testing were optimized to maximize the annual monetary genetic gain (AMGG), that is, a weighted sum of meat and maternal traits annual genetic gains. For GS, a reference population of 2000 individuals was assumed and genomic information was available for evaluation of male candidates only. In the classic selection scheme, males breeding values were estimated from own and offspring phenotypes. In GS, different scenarios were considered, differing by the information used to select males (genomic only, genomic+own performance, genomic+offspring phenotypes). The results showed that all GS scenarios were associated with higher total variable costs than classic selection (if the cost of genotyping was 123 euros/animal). In terms of AMGG and economic returns, GS scenarios were found to be superior to classic selection only if genomic information was combined with their own meat phenotypes (GS-Pheno) or with their progeny test information. The predicted economic efficiency, defined as returns (proportional to number of expressions of AMGG in the nucleus and commercial flocks) minus total variable costs, showed that the best GS scenario (GS-Pheno) was up to 15% more efficient than classic selection. For all selection scenarios, optimization increased the overall AMGG, returns and economic efficiency. As a conclusion, our study shows that some forms of GS strategies are more advantageous than classic selection, provided that GS is already initiated (i.e. the initial reference population is available). Optimizing decisional variables of the classic selection scheme could be of greater benefit than including genomic information in optimized designs.