Selection on functional longevity in a commercial population of dairy goats translates into significant differences in longevity in a common farm environment.

Resilience is the ability of an animal to cope with environmental disturbances, such as pathogens or negative energy balance. To improve resilience through breeding, we need resilience indicators. Functional longevity might be a good indicator of a dairy goat's lifetime resilience as it results from the ability to cope with and recover from all the challenges faced throughout its lifetime. The aim of this study was to validate the use of functional longevity as an indicator of resilience for selection. To address this question, we created 2 genetic lines of Alpine goats using hyperselected artificial insemination bucks with the most extreme estimated breeding values for functional longevity and the same milk yield performance. A total of 440 goats, 228 in the high longevity (high_LGV) and 221 in the low longevity (low_LGV) lines, were bred and monitored for 4 yr. Health treatments, serum IgG concentration as a proxy of passive immune transfer in early life, kidding, age, and reason of culling were systematically noted. Weight and body morphology were monitored. Weight and growth during the first year of life were similar in both goat lines. In contrast, the low_LGV goats had a lower weight during the beginning of first lactation than high_LGV goats. The milk fat-to-protein ratio was also significantly higher in low_LGV goats during first lactation. A multivariable Cox regression was fitted to the data to decipher survival at different stages of life in the 2 lines. The overall survival of high_LGV goats was significantly better than low_LGV goats (hazard ratio = 0.63, confidence interval = 0.47; 0.86) even after we included treatment, growth, serum IgG concentration at birth, and year effects in the model. The line effect was not constant over time; no significant effect was found during the first year, and the difference was observed after first kidding. This result suggested that survival at an early stage of life and during later productive life are under different genetic regulation. Altogether, this monitoring of the goat lines indicated that functional longevity-based selection helps to improve resilience by improving survival and mitigating some indicators of fat mobilization during early lactation.

Divergent selection on milk somatic cell count in goats improves udder health and milk quality with no effect on nematode resistance.

Milk somatic cell count (SCC) is commonly higher in goats than in cattle and sheep. Furthermore, the ability of milk SCC to predict mastitis is considered lower in goats than in cattle and sheep, and the relevance of somatic cell score (SCS)-based selection in this species has been questioned. To address this issue, we created 2 divergent lines of Alpine goats using artificially inseminated bucks with extreme estimated breeding values for SCS. A total of 287 goats, 158 in high- and 129 in low-SCS lines, were scrutinized for mastitis infections. We subjected 2,688 milk samples to conventional bacteriological analyses on agarose and bacterial counts were estimated for positive samples. The SCS, milk yield, fat content, and protein content were recorded every 3 wk. Clinical mastitis was systematically noted. A subset of 40 goats (20 from each line) was subsequently challenged with Haemonchus contortus and monitored for anemia (blood packed cell volume) and fecal egg counts to see if SCS-based selection had an indirect effect on resistance to gastrointestinal nematodes. Milk production traits, including milk quantity, fat content, and protein content, were similar in both goat lines. In contrast, the raw milk SCC almost doubled between the lines, with 1,542,000 versus 855,000 cells/mL in the high- and low-SCS lines, respectively. The difference in breeding value for SCS between lines was 1.65 genetic standard deviation equivalents. The Staphylococcus spp. most frequently isolated from milk were S. xylosus, S. caprae, S. epidermidis, and S. aureus. The frequency of positive bacteriology samples was significantly higher in the high-SCS line (49%) than in the low-SCS line (33%). The highest odds ratio was 3.49 (95% confidence interval: 11.95-6.25) for S. aureus. The distribution of bacterial species in positive samples between lines was comparable. The average quantity of bacteria in positive samples was also significantly higher in high-SCS goats (69 ± 80 growing colonies) than in low-SCS goats (38 ± 62 growing colonies). Clinical cases were rare and equally distributed between high- (n = 4; 2.5%) and low-SCS (n = 3; 2.3%) lines. Furthermore, the larger the amounts of bacteria in milk the higher the SCS level. Conversely, goats with repeatedly culture-negative udders exhibited the lowest SCC levels, with an average of below 300,000 cells/mL. We therefore confirmed that SCS is a relevant predictor of intramammary infection and hygienic quality of milk in goats and can be used for prophylactic purposes. After challenge with H. contortus, goats were anemic with high fecal egg counts but we found no difference between the genetic lines. This result provides initial evidence that resistance to mastitis or to gastrointestinal nematodes infections is under independent genetic regulation. Altogether, this monitoring of the goat lines indicated that SCS-based selection helps to improve udder health by decreasing milk cell counts and reducing the incidence of infection and related bacterial shedding in milk. Selection for low SCC should not affect a goat's ability to cope with gastrointestinal nematodes.

Genetic diversity analysis of French goat populations reveals selective sweeps involved in their differentiation.

After domestication 11 000 years ago in Asia Minor, the goat followed human migration to Europe and Asia. It was then introduced in Africa and is now raised all over the world. In this study, we exploited a dataset composed of 54 000 SNPs (Illumina goat DNA chip) to analyze the genetic diversity of 223 individuals belonging to eight French breeds (Alpine, Angora, Corse, Fossés, Poitevine, Provençale, Pyrénées and Saanen). Analyses carried out included individual-based approaches (principal component analysis and population structure) and population-based approaches (phylogenetic tree constructions). The results of the genetic diversity analyses revealed that French breeds are clearly differentiated, in particular, the Angora breed that originates from south west Asia. The Provençale breed shows a very original genetic pattern that could be the result of ancient admixture. Then, selection signatures were detected by identifying regions of outlying genetic differentiation between populations. Five genomic regions were detected under selection on chromosomes 5, 6, 11, 13 and 20, revealing mainly soft selective sweeps and a few hard selective sweeps and highlighting candidate genes that had been selected for during the evolutionary history of these breeds. Among them, two coat coloration genes (ADAMTS20 and ASIP) and one gene related to milk composition (CSN1S1) were involved.

Optimal mating strategies to manage a heterozygous advantage major gene in sheep.

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.

Genetic and economic effects of the increase in female paternal filiations by parentage assignment in sheep and goat breeding programs.

In sheep and goat breeding programs, the proportion of females for which the sire is known (known paternity rate [KPR]) can be very low. In this context, paternity assignment using SNP is an attractive tool. The annual genetic gain (AGG) is impacted by the accuracy of the EBV. In populations with a low KPR, the number of known relatives for a given individual is low, and the EBV that are based on this information are imprecise. However, the impact of partially known paternal filiations, in terms of potential genetic and economic losses, has never been quantitatively evaluated in situations where natural mating is the main reproductive mode. A deterministic model was developed to assess, for a panel of real breeding programs, the influence of the female KPR on the AGG and economic benefit. First, males were divided into categories according to their status (natural mating or AI sire) and breeding cycle and females according to parity, sire status (including unknown sire), and breeding cycle of the sire. Second, a demographic model described, for each category, the accumulation of known records for individuals and their close relatives. The output from this model was used to compute the average accuracy of the EBV per category. Then, a genetic model based on the gene flow between categories over time was described. Using the average accuracy of EBV per category, it provided the asymptotic AGG of the nucleus given its KPR. In the economic studies, changes to the mean genetic values in the nucleus and the commercial population after an increase in KPR and various gain:cost ratios (monetary gain due to an extra genetic SD of the selected trait divided by the cost of 1 assignment) were considered. Relative profit and payback periods were computed. We showed that SNP-based parentage assignment aimed at increasing the female KPR was not always profitable and that the type of breeding program and the size of the commercial population should be taken into consideration. Notably, achieving a profit was largely dependent on obtaining a favorable gain:cost ratio. The maximum supplementary AGG (16.9%) was obtained for breeding programs using only natural mating. In such programs without AI, a gain:cost ratio of 5 was needed to make assignment profitable at the nucleus level whereas a gain:cost ratio of 2 was sufficient if the nucleus represented a third of the total population.

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

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.

Genetic parameter estimation for major milk fatty acids in Alpine and Saanen primiparous goats.

Genetic parameters for 18 fatty acids or groups of fatty acids (FA), milk production traits, and somatic cell score (SCS) were estimated by restricted maximum likelihood with a repeatability animal model, using 45,259 test-day records from the first lactations of 13,677 Alpine and Saanen goats. Fatty acid data were collected as part of an extensive recording scheme (PhénoFinLait), and sample testing was based on mid-infrared spectra estimates. The total predicted FA content in milk was approximately 3.5% in Alpine and Saanen goats. Goat milk fat showed similar saturated FA to cattle and sheep, but higher contents of capric (C10:0) FA (~ 9.7 g/100g of milk fat). Heritability estimates ranged from 0.18 to 0.49 for FA and estimates were generally higher when FA were expressed in g/100g of milk fat compared with g/100g of milk. In general, the 3 specific short- and medium-chain goat FA, caproic acid (C6:0), caprylic acid (C8:0), and especially capric (C10:0) acid, had among the highest heritability estimates (from 0.21 to 0.37; average of 0.30). Heritability estimates for milk yield, fat and protein contents, and SCS were 0.22, 0.23, 0.39, 0.09, and 0.24, 0.20, 0.40, and 0.15, in Alpine and Saanen goats, respectively. When FA were expressed in g/100g of milk, genetic correlations between fat content and all FA were high and positive. Genetic correlations between the fat content and FA groups expressed in g/100g of fat led to further investigation of the association between fat content and FA profile within milk fat. Accordingly, in both Saanen and Alpine breeds, no significant genetic correlations were found between fat content and C16:0, whereas the correlations between fat content and specific goat FA (C6:0 to C10:0) were positive (0.17 to 0.59). In addition, the genetic correlation between fat content and C14:0 was negative (-0.17 to -0.35). The values of the genetic correlations between protein content and individual FA were similar, although genetic correlations between protein content and FA groups were close to zero. Genetic correlations of milk yield or SCS with the FA profile were weak. Results for genetic parameters for FA, however, should be further validated, because the low predicting ability of certain FA using mid-infrared spectra and the limited calibration data set might have resulted in low accuracy. In conclusion, our results indicated substantial genetic variation in goat milk FA that supported their amenability for genetic selection. In addition, selection on protein and fat contents is not expected to have an undesirable effect on the FA profile in regard to specificity of goat products and human health.

Prediction of fatty acid profiles in cow, ewe, and goat milk by mid-infrared spectrometry.

Mid-infrared (MIR) spectrometry was used to estimate the fatty acid (FA) composition in cow, ewe, and goat milk. The objectives were to compare different statistical approaches with wavelength selection to predict the milk FA composition from MIR spectra, and to develop equations for FA in cow, goat, and ewe milk. In total, a set of 349 cow milk samples, 200 ewe milk samples, and 332 goat milk samples were both analyzed by MIR and by gas chromatography, the reference method. A broad FA variability was ensured by using milk from different breeds and feeding systems. The methods studied were partial least squares regression (PLS), first-derivative pretreatment + PLS, genetic algorithm + PLS, wavelets + PLS, least absolute shrinkage and selection operator method (LASSO), and elastic net. The best results were obtained with PLS, genetic algorithm + PLS and first derivative + PLS. The residual standard deviation and the coefficient of determination in external validation were used to characterize the equations and to retain the best for each FA in each species. In all cases, the predictions were of better quality for FA found at medium to high concentrations (i.e., for saturated FA and some monounsaturated FA with a coefficient of determination in external validation >0.90). The conversion of the FA expressed in grams per 100mL of milk to grams per 100g of FA was possible with a small loss of accuracy for some FA.

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

The objectives of this study were to describe, using the goat SNP50 BeadChip (Illumina Inc., San Diego, CA), molecular data for the French dairy goat population and compare the effect of using genomic information on breeding value accuracy in different reference populations. Several multi-breed (Alpine and Saanen) reference population sizes, including or excluding female genotypes (from 67 males to 677 males, and 1,985 females), were used. Genomic evaluations were performed using genomic best linear unbiased predictor for milk production traits, somatic cell score, and some udder type traits. At a marker distance of 50kb, the average r(2) (squared correlation coefficient) value of linkage disequilibrium was 0.14, and persistence of linkage disequilibrium as correlation of r-values among Saanen and Alpine breeds was 0.56. Genomic evaluation accuracies obtained from cross validation ranged from 36 to 53%. Biases of these estimations assessed by regression coefficients (from 0.73 to 0.98) of phenotypes on genomic breeding values were higher for traits such as protein yield than for udder type traits. Using the reference population that included all males and females, accuracies of genomic breeding values derived from prediction error variances (model accuracy) obtained for young buck candidates without phenotypes ranged from 52 to 56%. This was lower than the average pedigree-derived breeding value accuracies obtained at birth for these males from the official genetic evaluation (62%). Adding females to the reference population of 677 males improved accuracy by 5 to 9% depending on the trait considered. Gains in model accuracies of genomic breeding values ranged from 1 to 7%, lower than reported in other studies. The gains in breeding value accuracy obtained using genomic information were not as good as expected because of the limited size (at most 677 males and 1,985 females) and the structure of the reference population.

Potential benefits of genomic selection on genetic gain of small ruminant breeding programs.

In conventional small ruminant breeding programs, only pedigree and phenotype records are used to make selection decisions but prospects of including genomic information are now under consideration. The objective of this study was to assess the potential benefits of genomic selection on the genetic gain in French sheep and goat breeding designs of today. Traditional and genomic scenarios were modeled with deterministic methods for 3 breeding programs. The models included decisional variables related to male selection candidates, progeny testing capacity, and economic weights that were optimized to maximize annual genetic gain (AGG) of i) a meat sheep breeding program that improved a meat trait of heritability (h(2)) = 0.30 and a maternal trait of h(2) = 0.09 and ii) dairy sheep and goat breeding programs that improved a milk trait of h(2) = 0.30. Values of ±0.20 of genetic correlation between meat and maternal traits were considered to study their effects on AGG. The Bulmer effect was accounted for and the results presented here are the averages of AGG after 10 generations of selection. Results showed that current traditional breeding programs provide an AGG of 0.095 genetic standard deviation (σa) for meat and 0.061 σa for maternal trait in meat breed and 0.147 σa and 0.120 σa in sheep and goat dairy breeds, respectively. By optimizing decisional variables, the AGG with traditional selection methods increased to 0.139 σa for meat and 0.096 σa for maternal traits in meat breeding programs and to 0.174 σa and 0.183 σa in dairy sheep and goat breeding programs, respectively. With a medium-sized reference population (nref) of 2,000 individuals, the best genomic scenarios gave an AGG that was 17.9% greater than with traditional selection methods with optimized values of decisional variables for combined meat and maternal traits in meat sheep, 51.7% in dairy sheep, and 26.2% in dairy goats. The superiority of genomic schemes increased with the size of the reference population and genomic selection gave the best results when nref > 1,000 individuals for dairy breeds and nref > 2,000 individuals for meat breed. Genetic correlation between meat and maternal traits had a large impact on the genetic gain of both traits. Changes in AGG due to correlation were greatest for low heritable maternal traits. As a general rule, AGG was increased both by optimizing selection designs and including genomic information.

An ABC estimate of pedigree error rate: application in dog, sheep and cattle breeds.

On the basis of correlations between pairwise individual genealogical kinship coefficients and allele sharing distances computed from genotyping data, we propose an approximate Bayesian computation (ABC) approach to assess pedigree file reliability through gene-dropping simulations. We explore the features of the method using simulated data sets and show precision increases with the number of markers. An application is further made with five dog breeds, four sheep breeds and one cattle breed raised in France and displaying various characteristics and population sizes, using microsatellite or SNP markers. Depending on the breeds, pedigree error estimations range between 1% and 9% in dog breeds, 1% and 10% in sheep breeds and 4% in cattle breeds.

Pedigree analysis of seven small French sheep populations and implications for the management of rare breeds.

Pedigree information was analyzed in 7 small populations of sheep raised in France (Bleu du Maine, Charmoise, Cotentin, on-farm Romanov, Romanov ex situ in vivo, Roussin de la Hague, Solognote) to estimate their genetic variability. The pedigree information for each breed, estimated by the number of equivalent generations traced, ranged from rather poor (4.6) to very good (10.5) when compared with other studies. On the basis of probabilities of gene origin, the effective number of ancestors ranged from 17 (on-farm Romanov breed) to 59 (Bleu du Maine). On the basis of the rate of inbreeding, the realized effective size was found to range from 65 (Romanov breed ex situ) to 231 (Bleu du Maine). The average kinship coefficients between rams from which semen doses are available in the French National Cryobank and the active ram and ewe populations were also computed. Results found in each breed were analyzed by taking into consideration the demographic evolution of the breeds, their management practices, and the use of cryopreservation as a way to preserve genetic variability. It appeared quite clear that, in populations in which AI with frozen semen is seldom used, factors that mainly affect the genetic variability are the female-to-male ratio, which should be as small as possible, and the number of reproducing female offspring by males, which should be as balanced as possible. Finally, our work showed that all populations under study have fairly good genetic variability in comparison with other species, despite their scarce numbers.