A first step toward genomic selection in the multi-breed French dairy goat population

Single-step models including dominance can be an enormous computational task and can even be prohibitive for practical application. In this study, we try to answer the question whether a reduced single-step model is able to estimate breeding values of bulls and breeding values, dominance deviations and total genetic values of cows with acceptable quality. Genetic values and phenotypes were simulated (500 repetitions) for a small Fleckvieh pedigree consisting of 371 bulls (180 thereof genotyped) and 553 cows (40 thereof genotyped). This pedigree was virtually extended for 2,407 non-genotyped daughters. Genetic values were estimated with the single-step model and with different reduced single-step models. Including more relatives of genotyped cows in the reduced single-step model resulted in a better agreement of results with the single-step model. Accuracies of genetic values were largest with single-step and smallest with reduced single-step when only the cows genotyped were modelled. The results indicate that a reduced single-step model is suitable to estimate breeding values of bulls and breeding values, dominance deviations and total genetic values of cows with acceptable quality.

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“…Dominance deviations are by definition included in YD, but not in DYD. The variance of e YD is

σ_{E}^{2}

and of e DYD i (the i ‐th element of e DYD ) is

\frac{2 σ_{A}^{2} + 4 ({normalσ}_{D}^{2} + {normalσ}_{E}^{2})}{n_{dau i}}

(Carillier et al., ). n dau i is the number of daughters’ YD included in the calculation of the i ‐th DYD.…”

Section: Methodsmentioning

confidence: 99%

Considering dominance in reduced single‐step genomic evaluations

Ertl

Edel

Pimentel

et al. 2018

J Animal Breeding Genetics

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“…However, so far, only attempts in France (Carillier et al . , ) and the UK (Mucha et al . ) are being undertaken to implement genomic selection in dairy goat breeding programs, as the global breeding industry is not as well established when compared with that for dairy cattle.…”

Section: Introductionmentioning

confidence: 99%

Genetic diversity analyses reveal first insights into breed‐specific selection signatures within Swiss goat breeds

et al. 2016

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We used genotype data from the caprine 50k Illumina BeadChip for the assessment of genetic diversity within and between 10 local Swiss goat breeds. Three different cluster methods allowed the goat samples to be assigned to the respective breed groups, whilst the samples of Nera Verzasca and Tessin Grey goats could not be differentiated from each other. The results of the different genetic diversity measures show that Appenzell, Toggenburg, Valais and Booted goats should be prioritized in future conservation activities. Furthermore, we examined runs of homozygosity (ROH) and compared genomic inbreeding coefficients based on ROH (F ) with pedigree-based inbreeding coefficients (F ). The linear relationship between F and F was confirmed for goats by including samples from the three main breeds (Saanen, Chamois and Toggenburg goats). F appears to be a suitable measure for describing levels of inbreeding in goat breeds with missing pedigree information. Finally, we derived selection signatures between the breeds. We report a total of 384 putative selection signals. The 25 most significant windows contained genes known for traits such as: coat color variation (MITF, KIT, ASIP), growth (IGF2, IGF2R, HRAS, FGFR3) and milk composition (PITX2). Several other putative genes involved in the formation of populations, which might have been selected for adaptation to the alpine environment, are highlighted. The results provide a contemporary background for the management of genetic diversity in local Swiss goat breeds.

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“…Genomic evaluation is already used routinely in many dairy cattle breeding programs and is expected to be implemented in the near future in other species. Recent studies in sheep and goats reported that the genomic evaluation of young males was more accurate than polygenic, pedigree-based evaluation (Daetwyler et al, 2012;Duchemin et al, 2012;Baloche et al, 2013;Carillier et al, 2013 and2014).…”

Section: Introductionmentioning

confidence: 99%

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

Shumbusho

Raoul

Astruc

et al. 2016

Animal

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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.

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A first step toward genomic selection in the multi-breed French dairy goat population

Cited by 63 publications

References 37 publications

Considering dominance in reduced single‐step genomic evaluations

Considering dominance in reduced single‐step genomic evaluations

Genetic diversity analyses reveal first insights into breed‐specific selection signatures within Swiss goat breeds

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

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