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Abstract The relationship between type traits and longevity was studied in the French Holstein breed using a survival analysis model. In this model, the phenotypic value adjusted for systematic fixed effects, the estimated breeding value, or the residual value (defined as the difference between the adjusted phenotypic value and the estimated breeding value) of the cow for each type trait was included as a risk factor. This was done separately for two subpopulations (registered and nonregistered herds) and with or without adjustment for production traits, i.e., considering true or functional longevity. For both types of herds, udder traits (and above all, udder depth) clearly influenced the length of productive life. There seemed to be a more pronounced voluntary culling on type traits in registered herds. The correction for the within herd-year class of production traits, as a way to approximate functional longevity, increased the importance of udder traits and decreased the weight of capacity traits. The same results were obtained when the phenotypic value of the cow for type was replaced by her estimated breeding value, whereas residuals had little impact. The relationship between longevity and type traits was most often nonlinear, in particular for udder traits, but in this study, no trait with a clear intermediate optimum was found.

Despite its potential utility for predicting cows' milk yield responses to once-daily milking (ODM), the genetic basis of cow milk trait responses to ODM has been scarcely if ever described in the literature, especially for short ODM periods. This study set out to (1) estimate the genetic determinism of milk yield and composition during a 3-wk ODM period, (2) estimate the genetic determinism of milk yield responses (i.e., milk yield loss upon switching cows to ODM and milk yield recovery upon switching them back to twicedaily milking; TDM), and (3) seek predictors of milk yield responses to ODM, in particular using the first day of ODM. Our trial used 430 crossbred Holstein × Normande cows and comprised 3 successive periods: 1 wk of TDM (control), 3 wk of ODM, and 2 wk of TDM. Implementing ODM for 3 wk reduced milk yield by 27.5% on average, and after resuming TDM cows recovered on average 57% of the milk lost. Heritability estimates in the TDM control period and 3-wk ODM period were, respectively, 0.41 and 0.35 for milk yield, 0.66 and 0.61 for milk fat content, 0.60 and 0.80 for milk protein content, 0.66 and 0.36 for milk lactose content, and 0.20 and 0.15 for milk somatic cell score content. Milk yield and composition during 3-wk ODM and TDM periods were genetically close (within-trait genetic correlations between experimental periods all exceeding 0.80) but were genetically closer within the same milking frequency. Heritabilities of milk yield loss observed upon switching cows to ODM (0.39 and 0.34 for milk yield loss in kg/d and %, respectively) were moderate and similar to milk yield heritabilities. Milk yield recovery (kg/d) upon resuming TDM was a trait of high heritability (0.63). Because they are easy to measure, TDM milk yield and composition and milk yield responses on the first day of ODM were investigated as predictors of milk yield responses to a 3-wk ODM to easily detect animals that are well adapted to ODM. Twice-daily milking milk yield and composition were found to be partly genetically correlated with milk yield responses but not closely enough for practical application. With genetic correlations of 0.98 and 0.96 with 3-wk ODM milk yield losses (in kg/d and %, respectively), milk yield losses on the first day of ODM proved to be more accurate in predicting milk yield responses on longer term ODM than TDM milk yield. [ABSTRACT FROM AUTHOR]

In French dairy sheep, Fourier transform infrared (FTIR) milk spectral data routinely predict the major milk components used in national genetic evaluations. The direct influence of genetic and environmental factors on milk FTIR spectra has been widely studied in dairy cattle, and relatively little in dairy ewes. In this study, 36,873 milk test-day records were available for 4,712 French Lacaune ewes farmed on 8 commercial farms. Our main goals were to provide the first description of spectral data and estimate the genetic parameters of French Lacaune dairy sheep during lactation. Principal component analysis (PCA) results demonstrated the impact of the lactation period on specific wavenumbers, allowing the identification of FTIR spectra collected at early (mo 2-4) and late (mo 5-7) lactation stages. The average estimated heritability (±mean SE) of the FTIR milk spectra from 2,971 to 926 cm -1 (446 wavenumbers) was 0.29 ± 0.02, ranging from 0.13 ± 0.01 to 0.42 ± 0.02. Furthermore, the heritabilities of spectra collected at the beginning or end of lactation changed at each point of the spectrum. However, at each wavenumber, the genomic correlation of transmittance values between these 2 lactation periods was high (>0.77), indicating the absence of a genotype-environment interaction. The genomic correlations between spectral regions and milk production traits (i.e., daily milk yield, fat and protein content, somatic cell score) varied from moderate to high. The results suggested that the most heritable areas of the spectrum were also genetically associated with dairy traits. Finally, the genomic correlations observed between the ewes' feed efficiency traits and the FTIR spectrum were moderate to high, while the genomic correlations between the change in body condition score and spectral data were rather low to moderate. This study confirmed that spectral data from Lacaune ewe milk were heritable, evolved phenotypically and genetically during lactation and were genetically correlated with traits included in breeding goals or traits of interest to the dairy industry., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The development of an across-country genomic evaluation scheme is a promising alternative for enlarging reference populations and successfully implementing genomic selection in small ruminant populations. However, the feasibility of such evaluations depends on the genetic similarity among the populations, and therefore, high connectedness and high genetic correlations between the traits recorded in different countries or populations are needed. In this study, we evaluated the feasibility of performing an across-country genomic evaluation for milk production and type traits in Alpine and Saanen goats from Canada, France, Italy, and Switzerland. Variance components and genetic parameters, including genetic correlations between traits recorded in different countries, were calculated using combined phenotypes, genotypes, and pedigree datasets. The (co)variance component analyses were performed within breed, either based only on pedigree information or also incorporating genomic information. Across-country genetic parameters were calculated for 3 representative traits (i.e., milk yield, fat content, and rear udder attachment). The heritability estimates ranged from 0.10 to 0.50, which are consistent with previous estimates reported in the literature. The genetic correlations for rear udder attachment ranged from 0.75 (between France and Italy, for the Alpine breed without genomic information) to 0.95 (between Canada and France, for the Saanen breed with genomic information), whereas for fat content, between France and Italy, they ranged from 0.75 in the Alpine breed without genomic information to 0.78 in the Alpine breed with genomic information. However, genetic correlations for milk yield were only estimable between France and Italy, with a moderate value of 0.45 for the Alpine breed with or without genomic information, and of 0.22 and 0.26 in the Saanen breed with and without genomic information, respectively. These low genetic correlations for milk yield could be due to several factors, including the trait definition in each country and genotype-by-environment interactions (GxE). The high genetic correlations found for fat content and rear udder attachment indicate that these traits might be more standardized across countries and less affected by GxE effects. Thus, an international genomic evaluation for these traits might be feasible. Further studies should be performed to understand the surprisingly lower genetic correlations between milk yield across countries. Furthermore, additional efforts should be made to increase the genetic connection among the Alpine and Saanen goat populations in the 4 countries included in the analyses., (© 2023.)

Genomic prediction of breeding values is routinely performed in several livestock breeding programs around the world, but the size of the training populations and the genetic structure of populations evaluated have, in many instances, limited the increase in the accuracy of genomic estimated breeding values. Combining phenotypic, pedigree, and genomic data from genetically related populations can be a feasible strategy to overcome this limitation. However, the success of across-population genetic evaluations depends on the pedigree connectedness and genetic relationship among individuals from different populations. In this context, this study aimed to evaluate the genetic connectedness and population structure of Alpine and Saanen dairy goats from four countries involved in the European project SMARTER (SMAll RuminanTs Breeding for Efficiency and Resilience), including Canada, France, Italy, and Switzerland. These analyses are paramount for assessing the potential feasibility of an across-country genomic evaluation in dairy goats. Approximately, 9,855 genotyped individuals (with 51% French genotyped animals) and 6,435,189 animals included in the pedigree files were available across all four populations. The pedigree analyses indicated that the exchange of breeding animals was mainly unilateral with flows from France to the other three countries. Italy has also imported breeding animals from Switzerland. Principal component analyses (PCAs), genetic admixture analysis, and consistency of the gametic phase revealed that French and Italian populations are more genetically related than the other dairy goat population pairs. Canadian dairy goats showed the largest within-breed heterogeneity and genetic differences with the European populations. The genetic diversity and population connectedness between the studied populations indicated that an international genomic evaluation may be more feasible, especially for French and Italian goats. Further studies will investigate the accuracy of genomic breeding values when combining the datasets from these four populations., Competing Interests: The author BB was employed by the company Qualitas AG. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Teissier, Brito, Schenkel, Bruni, Fresi, Bapst, Robert-Granie and Larroque.)

MicroRNAs are small noncoding RNAs that have important roles in the lactation process and milk biosynthesis. Some polymorphisms have been studied in various livestock species from the perspective of pathology or production traits. To target variants that could be the causal variants of dairy traits, genetic variants of microRNAs expressed in the mammary gland or present in milk and localized in dairy quantitative trait loci (QTLs) were investigated in bovine, caprine, and ovine species. In this study, a total of 59,124 (out of 28 millions), 13,427 (out of 87 millions), and 4761 (out of 38 millions) genetic variants in microRNAs expressed in the mammary gland or present in milk were identified in bovine, caprine, and ovine species, respectively. A total of 4679 of these detected bovine genetic variants are located in dairy QTLs. In caprine species, 127 genetic variants are localized in dairy QTLs. In ovine species, no genetic variant was identified in dairy QTLs. This study leads to the detection of microRNA genetic variants of interest in the context of dairy production, taking advantage of whole genome data to identify microRNA genetic variants expressed in the mammary gland and localized in dairy QTLs., (© 2021. The Author(s).)

The enhanced availability of sequence data in livestock provides an opportunity for more accurate predictions in routine genomic evaluations. Such evaluations would therefore no longer rely only on the linkage disequilibrium between a chip marker and the causal mutation. The objective of this study was to assess the usefulness of sequence data in Saanen goats (n = 33) to better capture a quantitative trait locus (QTL) on chromosome 19 (CHI19) and improve the accuracy of predictions for 3 milk production traits, 5 type traits, and somatic cell scores. All 1,207 50K genotypes were imputed to the sequence level. Four scenarios, each using a subset of CHI19 imputed variants, were then tested. Sequence-derived information included all CHI19 variants (529,576), all variants in the QTL region (22,269), 178 variants selected in the QTL region and added to an updated chip, or 178 randomly selected variants on CHI19. Two genomic evaluation models were applied: single-step genomic BLUP and weighted single-step genomic BLUP. All scenarios were compared with single-step genomic BLUP using 50K genotypes. Best overall results were obtained using single-step genomic BLUP on 50K genotypes completed with all variants in the QTL region of chromosome 19 (6.2% average increase in accuracy for 9 traits) with the highest accuracy gain for fat yield (17.9%), significant increases for milk (13.7%) and protein yields (12.5%), and type traits associated with CHI19. Despite its association with the QTL region of chromosome 19, the somatic cell score showed decreased accuracy in every alternative scenario. Using all CHI19 variants led to an overall decrease of 4.8% in prediction accuracy. The updated chip was efficient and improved genomic evaluations by 3.1 to 6.4% on average, depending on the scenario. Indeed, information from only a few carefully selected variants increased accuracies for traits of interest when used in a single-step genomic BLUP model. In conclusion, using QTL region variants imputed from sequence data in single-step genomic evaluations represents a promising perspective for such evaluations in dairy goats. Furthermore, using only a limited number of selected variants in QTL regions, as available on SNP chip updates, significantly increases the accuracy for QTL-associated traits without deteriorating the evaluation accuracy for other traits. The latter approach is interesting, as it avoids time-consuming imputation and data formatting processes and provides reliable genotypes., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The relationship between microbiota and health has been widely reported in humans and animals. We established a link between teat cistern microbiota composition and bovine mastitis, an inflammatory disease often due to bacterial infections. To further decipher the relationships between teat cistern microbiota and immune and microbial responses, a switch from twice- to once-daily milking (ODM) in 31 initially healthy quarters of dairy cows was used to trigger an udder perturbation. In this study, a temporal relationship was reported between initial teat cistern microbiota composition and richness, the immune response to ODM, and mastitis development. Quarters with a low initial microbiota richness and taxonomic markers such as Bacteroidetes and Proteobacteria were associated with a higher rate of mastitis during ODM. Quarters with a higher richness and taxonomic markers such as Firmicutes, including the Lachnospiraceae family, and genera such as Bifidobacterium and Corynebacterium displayed early inflammation following transition to ODM but without developing mastitis (no infection). Short-term compositional shifts of microbiota indicates that microbiotas with a higher initial richness were more strongly altered by transition to ODM, with notably the disappearance of rare OTUs. Microbiota modifications were associated with an early innate immune system stimulation, which, in turn, may have contributed to the prevention of mastitis development., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Rault, Lévêque, Barbey, Launay, Larroque, Le Loir, Germon, Guinard-Flament and Even.)

The development of statistical methods aiming to improve the accuracy of genomic predictions is of utmost value for dairy goat breeding programs. In this context, the use of haplotypes, instead of individual SNP, could improve the accuracy of genomic predictions by better capturing the effect of causal variants, instead of relying solely on linkage disequilibrium with individual SNP. Haplotypes can be included in genomic evaluation models in various ways, such as fitting them as pseudo-SNP (i.e., haplotypes converted into biallelic SNP format). This can be easily incorporated in the software already available for single-step genomic predictions (ssGBLUP). Therefore, the aim of this study was to compare the predictive performances of ssGBLUP and weighted ssGBLUP (WssGBLUP) based on individual SNP or on haplotypes fitted as pseudo-SNP. Performance was compared in terms of accuracy, bias, and weights for SNP versus pseudo-SNP. Genomic predictions were performed on 5 milk production traits, 5 udder type traits, and somatic cell score (SCS). The training population was formed by 307 Alpine and 247 Saanen progeny-tested bucks, genotyped using the Illumina Goat SNP50 BeadChip (Illumina, San Diego, CA). The validation population included 205 Alpine and 146 Saanen young bucks. The accuracy of genomic predictions was evaluated in the validation population as the Pearson correlation between genomic estimated breeding values (GEBV), predicted based on various methods, and daughter deviation (DD) based on the official genetic evaluation of January 2016. Haplotype-based models were shown to improve the performance of genomic predictions for some traits. Gains in accuracy of up to +19% (0.310 to 0.368 for fat yield) in Alpine and up to +3% (0.361 to 0.373 for udder shape) in Saanen were observed with ssGBLUP. The ssGBLUP accuracies averaged across all traits and methods were equal to 0.467 (SNP) versus 0.471 (pseudo-SNP) in Alpine and 0.528 (SNP) versus 0.523 (pseudo-SNP) in Saanen. With WssGBLUP, gains in accuracy of up to 24% (0.298 to 0.370 for fat yield) in Alpine and 14% (0.431 to 0.490 for SCS) in Saanen were observed with WssGBLUP. Accuracies of WssGBLUP averaged across all traits and methods were equal to 0.455 (SNP and pseudo-SNP) in Alpine and 0.542 (SNP) versus 0.528 (pseudo-SNP) in Saanen. The average (±SD) slope of the regression of DD on GEBV for the validation animals, across all breeds, traits and scenarios, were equal to 0.82 ± 0.20 (SNP) and 0.83 ± 0.18 (pseudo-SNP) for ssGBLUP and 0.67 ± 0.16 (SNP) and 0.65 ± 0.16 (pseudo-SNP) for WssGBLUP, which suggest that haplotype-based models and ssGBLUP SNP were similarly biased. However, WssGBLUP was more biased than ssGBLUP, and its gains in accuracies were limited to milk production traits. Despite the fact that genomic predictions based on haplotypes require additional steps and time, the observed gains in GEBV predictive performance indicate that haplotype-based methods could be recommended for some traits., (© 2020, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background: Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats., Results: The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them., Conclusions: A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation.