Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program

Abstract

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.

Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations

“…Similar to results presented by Shumbusho et al. () and Tribout et al. (), the economic benefits obtained in this study were largely dependent on the genotyping cost.…”

“…These results are similar to those presented by Shumbusho et al. (), who obtained the highest returns by combining GS with phenotypic information, after limiting the number of genotyped progeny of selection candidates in a breeding scheme for meat sheep. Tribout, Larzul, and Phocas () also reduced the implementation costs of GS by reducing the number of genotyped candidates in a pig breeding scheme.…”

Prediction of genetic gain in finite populations with heterogeneous predicted breeding values accuracies

“…The set of selected individuals will be denoted RðxÞ. However, in most of the models underlying selection intensity computations, the optimal selection rule (S inf ) was shown to be d i = 1 only if g i is higher than a threshold k, implicitly given by Smith 1936;Hazel 1943;Cochran 1951;Falconer, 1960;Goffinet & Elsen 1984). This expectation is over the space of selection events x varying in g andĝ i .…”

“…semen sexing, embryo transfer, SNP chips, genome editing) or effect of changes in agriculture policies. In the recent past, genomic breeding programmes have been compared with classical ones using such approaches (Schaeffer 2006;K€ onig et al 2009a;Pryce et al 2010;Shumbusho et al 2013Shumbusho et al , 2015.…”

Editorial: Genomic selection with numerically small reference populations

“…Of course GS is also promising for other livestock species than dairy cattle, although the economic return on GS may be more limited, because the relative benefit compared with pedigree-based selection is smaller, and therefore does not justify the costs to compile a large reference population. Shumbusho et al (2016) animal investigated the potential of GS in the meat sheep industry, considering different combinations of source of information used to select males. Assuming that an initial reference population was already available, carefully chosen GS strategies were able to yield up to 15% higher response than traditional selection, despite the higher costs of the selection process.…”