Genetic parameters for milk production and type traits in North American and European Alpine and Saanen dairy goat populations.

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.

Genetic Characterization and Population Connectedness of North American and European Dairy Goats.

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.

Potentials of using milk performance data and FAMACHA score as indicators for Targeted Selective Treatment in Lacaune dairy sheep in Switzerland.

Targeted Selective Treatment (TST) is one approach to slow down the development of anthelmintic resistance. Its success is closely linked to the correct identification of animals in need of treatment. In dairy goats it has been proposed to use milk yield as TST indicator and to focus treatments on high yielding dairy goats. In dairy sheep the relationship between milk performance and infection with gastrointestinal nematodes (GIN) is not well known. The aim of this study was to investigate the relationship between milk yield and GIN infection in dairy sheep and based on this, to evaluate milk performance data as a potential TST indicator. Overall 1159 Lacaune ewes of 15 dairy sheep farms in Switzerland were included in the study. The ewes were phenotyped once between August and December 2019, when they were at least 70 days in milk (DIM). Individual faecal samples were taken from every ewe to determine the nematode egg concentration per gram faeces (EPG). In addition, the clinical parameters FAMACHA score and packed cell volume (PCV) were measured. Linear mixed models were used to analyse the effects of the collected parameters on EPG. EPG increased significantly with increasing test day milk yields (P = 0.002), indicating high yielding ewes to be less resistant to GIN infections than low yielding ewes. The effect was most pronounced in earlier lactation but remained within a moderate range. Overall, our results indicated the potential of using milk yield data of rather early lactation as TST indicator in dairy sheep. On farms with predominantly H. contortus the combination with FAMACHA might improve the correct identification of highly infected ewes, as FAMACHA was correlated with EPG (r = 0.37, P < 0.001).

Potentials of using milk performance data and FAMACHA score as indicators for Targeted Selective Treatment in Lacaune dairy sheep in Switzerland.

Targeted Selective Treatment (TST) is one approach to slow down the development of anthelmintic resistance. Its success is closely linked to the correct identification of animals in need of treatment. In dairy goats it has been proposed to use milk yield as TST indicator and to focus treatments on high yielding dairy goats. In dairy sheep the relationship between milk performance and infection with gastrointestinal nematodes (GIN) is not well known. The aim of this study was to investigate the relationship between milk yield and GIN infection in dairy sheep and based on this, to evaluate milk performance data as a potential TST indicator. Overall 1159 Lacaune ewes of 15 dairy sheep farms in Switzerland were included in the study. The ewes were phenotyped once between August and December 2019, when they were at least 70 days in milk (DIM). Individual faecal samples were taken from every ewe to determine the nematode egg concentration per gram faeces (EPG). In addition, the clinical parameters FAMACHA score and packed cell volume (PCV) were measured. Linear mixed models were used to analyse the effects of the collected parameters on EPG. EPG increased significantly with increasing test day milk yields (P=0.002), indicating high yielding ewes to be less resistant to GIN infections than low yielding ewes. The effect was most pronounced in earlier lactation but remained within a moderate range. Overall, our results indicated the potential of using milk yield data of rather early lactation as TST indicator in dairy sheep. On farms with predominantly H. contortus the combination with FAMACHA might improve the correct identification of highly infected ewes, as FAMACHA was correlated with EPG (r=0.37, P<0.001).

Multi-breed genome-wide association studies across countries for electronically recorded behavior traits in local dual-purpose cows.

Basic bovine behavior is a crucial parameter influencing cattle domestication. In addition, behavior has an impact on cattle productivity, welfare and adaptation. The aim of the present study was to infer quantitative genetic and genomic mechanisms contributing to natural dual-purpose cow behavior in grazing systems. In this regard, we genotyped five dual-purpose breeds for a dense SNP marker panel from four different European countries. All cows from the across-country study were equipped with the same electronic recording devices. In this regard, we analyzed 97,049 longitudinal sensor behavior observations from 319 local dual-purpose cows for rumination, feeding, basic activity, high active, not active and ear temperature. According to the specific sensor behaviors and following a welfare protocol, we computed two different welfare indices. For genomic breed characterizations and multi-breed genome-wide association studies, sensor traits and test-day production records were merged with 35,826 SNP markers per cow. For the estimation of variance components, we used the pedigree relationship matrix and a combined similarity matrix that simultaneously included both pedigree and genotypes. Heritabilities for feeding, high active and not active were in a moderate range from 0.16 to 0.20. Estimates were very similar from both relationship matrix-modeling approaches and had quite small standard errors. Heritabilities for the remaining sensor traits (feeding, basic activity, ear temperature) and welfare indices were lower than 0.09. Five significant SNPs on chromosomes 11, 17, 27 and 29 were associated with rumination, and two different SNPs significantly influenced the sensor traits "not active" (chromosome 13) and "feeding" (chromosome 23). Gene annotation analyses inferred 22 potential candidate genes with a false discovery rate lower than 20%, mostly associated with rumination (13 genes) and feeding (8 genes). Mendelian randomization based on genomic variants (i.e., the instrumental variables) was used to infer causal inference between an exposure and an outcome. Significant regression coefficients among behavior traits indicate that all specific behavioral mechanisms contribute to similar physiological processes. The regression coefficients of rumination and feeding on milk yield were 0.10 kg/% and 0.12 kg/%, respectively, indicating their positive influence on dual-purpose cow productivity. Genomically, an improved welfare behavior of grazing cattle, i.e., a higher score for welfare indices, was significantly associated with increased fat and protein percentages.

Impact of US Brown Swiss genetics on milk quality from low-input herds in Switzerland: Interactions with season.

This study investigated the effect of, and interactions between, US Brown Swiss (BS) genetics and season on milk yield, basic composition and fatty acid profiles, from cows on low-input farms in Switzerland. Milk samples (n = 1,976) were collected from 1,220 crossbreed cows with differing proportions of BS, Braunvieh and Original Braunvieh genetics on 40 farms during winter-housing and summer-grazing. Cows with more BS genetics produced more milk in winter but not in summer, possibly because of underfeeding potentially high-yielding cows on low-input pasture-based diets. Cows with more Original Braunvieh genetics produced milk with more (i) nutritionally desirable eicosapentaenoic and docosapentaenoic acids, throughout the year, and (ii) vaccenic and α-linolenic acids, total omega-3 fatty acid concentrations and a higher omega-3/omega-6 ratio only during summer-grazing. This suggests that overall milk quality could be improved by re-focussing breeding strategies on cows' ability to respond to local dietary environments and seasonal dietary changes.

Short communication: Genomic prediction using imputed whole-genome sequence variants in Brown Swiss Cattle.

The accuracy of genomic prediction determines response to selection. It has been hypothesized that accuracy of genomic breeding values can be increased by a higher density of variants. We used imputed whole-genome sequence data and various single nucleotide polymorphism (SNP) selection criteria to estimate genomic breeding values in Brown Swiss cattle. The extreme scenarios were 50K SNP chip data and whole-genome sequence data with intermediate scenarios using linkage disequilibrium-pruned whole-genome sequence variants, only variants predicted to be missense, or the top 50K variants from genome-wide association studies. We estimated genomic breeding values for 3 traits (somatic cell score, nonreturn rate in heifers, and stature) and found differences in accuracy levels between traits. However, among different SNP sets, accuracy was very similar. In our analyses, sequence data led to a marginal increase in accuracy for 1 trait and was lower than 50K for the other traits. We concluded that the inclusion of imputed whole-genome sequence data does not lead to increased accuracy of genomic prediction with the methods.

Highly accurate sequence imputation enables precise QTL mapping in Brown Swiss cattle.

BACKGROUND: Within the last few years a large amount of genomic information has become available in cattle. Densities of genomic information vary from a few thousand variants up to whole genome sequence information. In order to combine genomic information from different sources and infer genotypes for a common set of variants, genotype imputation is required. RESULTS: In this study we evaluated the accuracy of imputation from high density chips to whole genome sequence data in Brown Swiss cattle. Using four popular imputation programs (Beagle, FImpute, Impute2, Minimac) and various compositions of reference panels, the accuracy of the imputed sequence variant genotypes was high and differences between the programs and scenarios were small. We imputed sequence variant genotypes for more than 1600 Brown Swiss bulls and performed genome-wide association studies for milk fat percentage at two stages of lactation. We found one and three quantitative trait loci for early and late lactation fat content, respectively. Known causal variants that were imputed from the sequenced reference panel were among the most significantly associated variants of the genome-wide association study. CONCLUSIONS: Our study demonstrates that whole-genome sequence information can be imputed at high accuracy in cattle populations. Using imputed sequence variant genotypes in genome-wide association studies may facilitate causal variant detection.

Genome-wide association studies of fertility and calving traits in Brown Swiss cattle using imputed whole-genome sequences.

BACKGROUND: The detection of quantitative trait loci has accelerated with recent developments in genomics. The introduction of genomic selection in combination with sequencing efforts has made a large amount of genotypic data available. Functional traits such as fertility and calving traits have been included in routine genomic estimation of breeding values making large quantities of phenotypic data available for these traits. This data was used to investigate the genetics underlying fertility and calving traits and to identify potentially causative genomic regions and variants. We performed genome-wide association studies for 13 functional traits related to female fertility as well as for direct and maternal calving ease based on imputed whole-genome sequences. Deregressed breeding values from ~1000-5000 bulls per trait were used to test for associations with approximately 10 million imputed sequence SNPs. RESULTS: We identified a QTL on BTA17 associated with non-return rate at 56 days and with interval from first to last insemination. We found two significantly associated non-synonymous SNPs within this QTL region. Two more QTL for fertility traits were identified on BTA25 and 29. A single QTL was identified for maternal calving traits on BTA13 whereas three QTL on BTA19, 21 and 25 were identified for direct calving traits. The QTL on BTA19 co-localizes with the reported BH2 haplotype. The QTL on BTA25 is concordant for fertility and calving traits and co-localizes with a QTL previously reported to influence stature and related traits in Brown Swiss dairy cattle. CONCLUSION: The detection of QTL and their causative variants remains challenging. Combining comprehensive phenotypic data with imputed whole genome sequences seems promising. We present a QTL on BTA17 for female fertility in dairy cattle with two significantly associated non-synonymous SNPs, along with five additional QTL for fertility traits and calving traits. For all of these we fine mapped the regions and suggest candidate genes and candidate variants.

Population structure and genomic inbreeding in nine Swiss dairy cattle populations.

BACKGROUND: Domestication, breed formation and intensive selection have resulted in divergent cattle breeds that likely exhibit their own genomic signatures. In this study, we used genotypes from 27,612 autosomal single nucleotide polymorphisms to characterize population structure based on 9214 sires representing nine Swiss dairy cattle populations: Brown Swiss (BS), Braunvieh (BV), Original Braunvieh (OB), Holstein (HO), Red Holstein (RH), Swiss Fleckvieh (SF), Simmental (SI), Eringer (ER) and Evolèner (EV). Genomic inbreeding (F ROH) and signatures of selection were determined by calculating runs of homozygosity (ROH). The results build the basis for a better understanding of the genetic development of Swiss dairy cattle populations and highlight differences between the original populations (i.e. OB, SI, ER and EV) and those that have become more popular in Switzerland as currently reflected by their larger populations (i.e. BS, BV, HO, RH and SF). RESULTS: The levels of genetic diversity were highest and lowest in the SF and BS breeds, respectively. Based on F ST values, we conclude that, among all pairwise comparisons, BS and HO (0.156) differ more than the other pairs of populations. The original Swiss cattle populations OB, SI, ER, and EV are clearly genetically separated from the Swiss cattle populations that are now more common and represented by larger numbers of cows. Mean levels of F ROH ranged from 0.027 (ER) to 0.091 (BS). Three of the original Swiss cattle populations, ER (F ROH: 0.027), OB (F ROH: 0.029), and SI (F ROH: 0.039), showed low levels of genomic inbreeding, whereas it was much higher in EV (F ROH: 0.074). Private signatures of selection for the original Swiss cattle populations are reported for BTA4, 5, 11 and 26. CONCLUSIONS: The low levels of genomic inbreeding observed in the original Swiss cattle populations ER, OB and SI compared to the other breeds are explained by a lesser use of artificial insemination and greater use of natural service. Natural service results in more sires having progeny at each generation and thus this breeding practice is likely the major reason for the remarkable levels of genetic diversity retained within these populations. The fact that the EV population is regionally restricted and its small census size of herd-book cows explain its high level of genomic inbreeding.

Evaluation of variant identification methods for whole genome sequencing data in dairy cattle.

BACKGROUND: Advances in human genomics have allowed unprecedented productivity in terms of algorithms, software, and literature available for translating raw next-generation sequence data into high-quality information. The challenges of variant identification in organisms with lower quality reference genomes are less well documented. We explored the consequences of commonly recommended preparatory steps and the effects of single and multi sample variant identification methods using four publicly available software applications (Platypus, HaplotypeCaller, Samtools and UnifiedGenotyper) on whole genome sequence data of 65 key ancestors of Swiss dairy cattle populations. Accuracy of calling next-generation sequence variants was assessed by comparison to the same loci from medium and high-density single nucleotide variant (SNV) arrays. RESULTS: The total number of SNVs identified varied by software and method, with single (multi) sample results ranging from 17.7 to 22.0 (16.9 to 22.0) million variants. Computing time varied considerably between software. Preparatory realignment of insertions and deletions and subsequent base quality score recalibration had only minor effects on the number and quality of SNVs identified by different software, but increased computing time considerably. Average concordance for single (multi) sample results with high-density chip data was 58.3% (87.0%) and average genotype concordance in correctly identified SNVs was 99.2% (99.2%) across software. The average quality of SNVs identified, measured as the ratio of transitions to transversions, was higher using single sample methods than multi sample methods. A consensus approach using results of different software generally provided the highest variant quality in terms of transition/transversion ratio. CONCLUSIONS: Our findings serve as a reference for variant identification pipeline development in non-human organisms and help assess the implication of preparatory steps in next-generation sequencing pipelines for organisms with incomplete reference genomes (pipeline code is included). Benchmarking this information should prove particularly useful in processing next-generation sequencing data for use in genome-wide association studies and genomic selection.

A function accounting for training set size and marker density to model the average accuracy of genomic prediction.

Prediction of genomic breeding values is of major practical relevance in dairy cattle breeding. Deterministic equations have been suggested to predict the accuracy of genomic breeding values in a given design which are based on training set size, reliability of phenotypes, and the number of independent chromosome segments ([Formula: see text]). The aim of our study was to find a general deterministic equation for the average accuracy of genomic breeding values that also accounts for marker density and can be fitted empirically. Two data sets of 5'698 Holstein Friesian bulls genotyped with 50 K SNPs and 1'332 Brown Swiss bulls genotyped with 50 K SNPs and imputed to ∼600 K SNPs were available. Different k-fold (k = 2-10, 15, 20) cross-validation scenarios (50 replicates, random assignment) were performed using a genomic BLUP approach. A maximum likelihood approach was used to estimate the parameters of different prediction equations. The highest likelihood was obtained when using a modified form of the deterministic equation of Daetwyler et al. (2010), augmented by a weighting factor (w) based on the assumption that the maximum achievable accuracy is [Formula: see text]. The proportion of genetic variance captured by the complete SNP sets ([Formula: see text]) was 0.76 to 0.82 for Holstein Friesian and 0.72 to 0.75 for Brown Swiss. When modifying the number of SNPs, w was found to be proportional to the log of the marker density up to a limit which is population and trait specific and was found to be reached with ∼20'000 SNPs in the Brown Swiss population studied.