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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., (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 license (http://creativecommons.org/licenses/by/4.0/).)

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 accuracy 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., (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 license (http://creativecommons.org/licenses/by/4.0/).)

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 pedigree-based 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 validation 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., (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 license (http://creativecommons.org/licenses/by/4.0/).)

Background: Structural chromosome abnormalities are well known as factors that reduce fertility rate in domestic pigs. According to large-scale national cytogenetic screening programs that are implemented in France, it is estimated that new chromosome abnormalities occur at a rate of 0.5 % in fertility-unproven boars., Results: This work aimed at estimating the prevalence and consequences of chromosome abnormalities in commercial swine operations in Canada. We found pig carriers at a frequency of 1.64 % (12 out of 732 boars). Carrier pigs consistently showed lower fertility values. The total number of piglets born for litters from carrier boars was between 4 and 46 % lower than the herd average. Similarly, carrier boars produced litters with a total number of piglets born alive that was between 6 and 28 % lower than the herd average. A total of 12 new structural chromosome abnormalities were identified., Conclusions: Reproductive performance is significantly reduced in sires with chromosome abnormalities. The incidence of such abnormal sires appears relatively high in populations without routine cytogenetic screening such as observed for Canada in this study. Systematic cytogenetic screening of potential breeding boars would minimise the risk of carriers of chromosome aberrations entering artificial insemination centres. This would avoid the large negative effects on productivity for the commercial sow herds and reduce the risk of transmitting abnormalities to future generations in nucleus farms.

Our objectives were to estimate frequencies of previously identified single nucleotide polymorphisms (SNPs) in adiponectin (ADIPOQ) and its receptors (ADIPOR1 and ADIPOR2) in a population of Duroc, Landrace and Yorkshire pigs and evaluate the effect of these alleles on sow productivity estimated breeding values (EBVs). Eight SNPs were genotyped on 446 pigs in the ADIPOQ (c.178G>A, c.*300A>G, c.*1094&#95;1095insC and c.*1779A>C), ADIPOR1 (c.*129A>C) and ADIPOR2 (c.*112G>A, c.*295G>C and c.*1455G>A) genes. Association analyses were performed with sow productivity EBVs based on litter records collected in Canadian breeding farms. There were significant associations between ADIPOQ c.178G>A and c.*1094&#95;1095insC SNPs and studied traits. However, none of these associations remained significant after applying a Bonferroni correction. The ADIPOR2 c.*112G>A SNP was associated with the total number of piglets born (TNB, P < 0.001) and litter weight at weaning (LWW, P < 0.001) EBVs. Associations were also observed between the ADIPOR2 [A;C;G] haplotype and TNB and LWW (P < 0.001). Our results demonstrate that a selection in favor of the c.*112G allele or against the [A;C;G] haplotype may have the potential to increase LWW EBVs. However, the c.*112G allele is also associated with lower TNB EBVs. Some of the alleles of the genes studied showed substantial variability and in general, the results corroborated previously reported findings for an independent sow population. However, careful cost-benefits analyses should be performed before using these markers in selection program as an improvement in TNB may translate into lighter LWW, with its associated negative impact on production traits such as growth performances.

Background: In this study we applied the extreme groups/selective genotyping approach for identifying copy number variations in high and low fertility breeding boars. The fertility indicator was the calculated Direct Boar Effect on litter size (DBE) that was obtained as a by-product of the national genetic evaluation for litter size (BLUP). The two groups of animals had DBE values at the upper (high fertility) and lower (low fertility) end of the distribution from a population of more than 38,000 boars. Animals from these two diverse phenotypes were genotyped with the Porcine SNP60K chip and compared by several approaches in order to prove the feasibility of our CNV analysis and to identify putative markers of fertility., Results: We have identified 35 CNVRs covering 36.5 Mb or ~1.3% of the porcine genome. Among these 35 CNVRs, 14 were specific to the high fertility group, while 19 CNVRs were specific to the low fertility group which overlap with 137 QTLs of various reproductive traits. The identified 35 CNVRs encompassed 50 genes, among them 40 were specific to the low fertility group, seven to the high fertility group, while three were found in regions that were present in both groups but with opposite gain/loss status. A functional analysis of several databases revealed that the genes found in CNVRs from the low fertility group have been significantly enriched in members of the innate immune system, Toll-like receptor and RIG-I-like receptor signaling and fatty acid oxidation pathways., Conclusions: We have demonstrated that our analysis pipeline could identify putative CNV markers of fertility, especially in case of low fertility boars.