Genomic evaluations are routine in most plant and livestock breeding programs but are used infrequently in dairy goat breeding schemes. In this context, the purpose of this study was to investigate the use of the single-step genomic BLUP method for predicting genomic breeding values for milk production traits (milk, protein, and fat yields; protein and fat percentages) in Canadian Alpine and Saanen dairy goats. There were 6,409 and 12,236 Alpine records and 3,434 and 5,008 Saanen records for each trait in first and later lactations, respectively, and a total of 1,707 genotyped animals (833 Alpine and 874 Saanen). Two validation approaches were used, forward validation (i.e., animals born after 2013 with an average estimated breeding value accuracy from the full data set ≥0.50) and forward cross-validation (i.e., subsets of all animals included in the forward validation were used in successive replications). The forward cross-validation approach resulted in similar validation accuracies (0.55 to 0.66 versus 0.54 to 0.61) and biases (−0.01 to -0.07 versus −0.03 to 0.11) to the forward validation when averaged across traits. Additionally, both single and multiple-breed analyses were compared, and similar average accuracies and biases were observed across traits. However, there was a small gain in accuracy from the use of multiple-breed models for the Saanen breed. A small gain in validation accuracy for genomically enhanced estimated breeding values (GEBV) relative to pedigreebased estimated breeding values (EBV) was observed across traits for the Alpine breed, but not for the Saanen breed, possibly due to limitations in the validation design, heritability of the traits evaluated, and size of the training populations. Trait-specific gains in theoretical accuracy of GEBV relative to EBV for the vali-dation animals ranged from 17 to 31% in Alpine and 35 to 55% in Saanen, using the cross-validation approach. The GEBV predicted from the full data set were 12 to 16% more accurate than EBV for genotyped animals, but no gains were observed for nongenotyped animals. The largest gains were found for does without lactation records (35-41%) and bucks without daughter records (46-54%), and consequently, the implementation of genomic selection in the Canadian dairy goat population would be expected to increase selection accuracy for young breeding candidates. Overall, this study represents the first step toward implementation of genomic selection in Canadian dairy goat populations.

Genetic parameters were estimated for growth, ultrasound, and carcass traits in a Canadian crossbred heavy lamb population. Traits analyzed included birth, weaning, post-weaning, and ultrasound scanning weights; pre-and post-weaning average daily gain; ultrasonically measured eye muscle and fat depths; hot carcass weight; fat depth at the GR site (110 mm from the midline on the 12th rib); carcass conformation scores; saleable meat yield; price grid value; and total carcass value. The impact of three alternative slaughter endpoints (slaughter age, carcass weight, and carcass fatness) on genetic parameter estimates was also evaluated. In general, carcass traits were found to be moderately heritable, with heritability estimates ranging from 0.17 ± 0.02 for hot carcass weight at a constant slaughter age to 0.34 ± 0.02 for average carcass conformation score at a constant carcass weight. Heritability estimates were similar when observations were adjusted to alternative slaughter endpoints, but for some traits, phenotypic variance and genetic correlation estimates differed. Genetic correlations between carcass

Increasing the productivity of Canadian dairy goats is critical to the competitiveness of the sector; however, little is known about the underlying genetic architecture of economically important traits in these populations. Consequently, the objectives of this study were as follows: (1) to perform a single-step GWAS for milk production traits (milk, protein, and fat yields, and protein and fat percentages in first and later lactations) and conformation traits (body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats) in the Canadian Alpine and Saanen breeds; and (2) to identify positional and functional candidate genes related to these traits. The data available for analysis included 305-d milk production records for 6,409 Alpine and 3,434 Saanen does in first lactation and 5,827 Alpine and 2,632 Saanen does in later lactations; as well as linear type conformation records for 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Both single-breed and multiple-breed GWAS were performed using single-trait animal models. Positional and functional candidate genes were then identified in downstream analyses. The GWAS identified 189 unique SNP that were significant at the chromosomal level, corresponding to 271 unique positional candidate genes within 50 kb up-and downstream, across breeds and traits. This study provides evidence for the economic importance of several candidate genes (e.g., CSN1S1, CSN2, CSN1S2, CSN3, DGAT1, and ZNF16) in the Canadian Alpine and Saanen populations that have been previously reported in other dairy goat populations. Moreover, several novel positional and functional candidate genes (e.g., RPL8, DCK, and MOB1B) were also identified. Overall, the results of this study have provided greater insight into the genetic architecture of milk production and conformation traits in the Canadian Alpine and Saanen populations. Greater understanding of these traits will help to improve dairy goat breeding programs.

Conformation traits are functional traits known to affect longevity, production efficiency, and profitability of dairy goats. However, genetic progress for these traits is expected to be slower than for milk production traits due to the limited number of herds participating in type classification programs, and often lower heritability estimates. Genomic selection substantially accelerates the rate of genetic progress in many species and industries, especially for lowly heritable, difficult, or expensive to measure traits. Therefore, the main objectives of this study were (1) to evaluate the potential benefits of the implementation of single-step genomic evaluations for conformation traits in Canadian Alpine and Saanen dairy goats, and (2) to investigate the effect of the use of single-and multiple-breed training populations. The phenotypes used in this study were linear conformation scores, on a 1-to-9 scale, for 8 traits (i.e., body capacity, dairy character, fore udder, feet and legs, general appearance, rear udder, medial suspensory ligament, and teats) of 5,158 Alpine and 2,342 Saanen does. Genotypes were available for 833 Alpine and 874 Saanen animals. Averaged across all traits, the use of multiple-breed analyses increased validation accuracy for Saanen, and reduced bias of genomically enhanced breeding values (GEBV) for both Alpine and Saanen compared with single-breed analyses. Little benefit was observed from the use of GEBV relative to pedigree-based EBV in terms of validation accuracy and bias, possibly due to limitations in the validation design, but substantial gains of 0.14 to 0.21 (32-50%) were observed in the theoretical accuracy of validation animals when averaged across traits for single-and multiple-breed analyses. Across the whole genotyped population, average gains in theoretical ac-curacy for GEBV compared with EBV across all traits ranged from 0.15 to 0.17 (32-37%) for Alpine and 0.17 to 0.19 (40-41%) for Saanen, depending on the model used. The largest gains were observed for does without classification records (0.19-0.22 or 50-55%) and bucks without daughter classification records (0.20-0.27 or 57-82%), which have the least information contributing to their traditional EBV. The use of multiple-breed rather than single-breed models was most beneficial for the Saanen breed, which had fewer phenotypic records available for the analyses. These results suggest that the implementation of genomic selection could increase the accuracy of breeding values for conformation traits in Canadian dairy goats.

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The use of multiple-breed models can increase the accuracy of estimated breeding values (EBV) when few phenotypes are available for a trait. However, pooling breeds is not always beneficial for genomic evaluations due to the low consistency of gametic phase between individual breeds. The objective of this study was to compare the expected gain in accuracy of single-step genomic breeding values (GEBV) for conformation traits of Canadian Alpine and Saanen goats predicted using single and multiple-breed models. The traits considered were body capacity, dairy character, feet and legs, fore udder, general appearance, rear udder, suspensory ligament, and teats, all recorded by trained classifiers, using a 1 to 9 scale. The full datasets included a total of 7,500 phenotypes for each trait (5,158 Alpine and 2,342 Saanen) and 1,707 50K genotypes (833 Alpine, 874 Saanen). Standard errors of prediction (SEP) were obtained for EBV and GEBV predicted using single-trait animal models on full or validation datasets. Breed difference was accounted for as a fixed effect in the multiple-breed models. Average theoretical accuracies were calculated from the SEP. For Saanen, with fewer records, expected accuracies of EBV and GEBV for the validation animals (selection candidates) were consistently higher for the multiple-breed models. Trait specific gains in theoretical accuracy of GEBV relative to EBV for the selection candidates ranged from 30 to 48% for Alpine and 41 to 61% for Saanen. Averaged across all traits, GEBV predicted from the full dataset were 32 to 38% more accurate than EBV for genotyped animals and the largest gains were found for does without conformation records (49 to 55%) and bucks without daughter records (56 to 82%). Overall, the implementation of genomic selection would substantially increase selection accuracy for young breeding candidates and, consequently, the rate of genetic improvement for conformation traits in Canadian dairy goats.