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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.