BackgroundThe detection of signatures of selection has the potential to elucidate the identities of genes and mutations associated with phenotypic traits important for livestock species. It is also very relevant to investigate the levels of genetic diversity of a population, as genetic diversity represents the raw material essential for breeding and has practical implications for implementation of genomic selection. A total of 1151 animals from nine goat populations selected for different breeding goals and genotyped with the Illumina Goat 50K single nucleotide polymorphisms (SNP) Beadchip were included in this investigation.ResultsThe proportion of polymorphic SNPs ranged from 0.902 (Nubian) to 0.995 (Rangeland). The overall mean HO and HE was 0.374 ± 0.021 and 0.369 ± 0.023, respectively. The average pairwise genetic distance (D) ranged from 0.263 (Toggenburg) to 0.323 (Rangeland). The overall average for the inbreeding measures FEH, FVR, FLEUT, FROH and FPED was 0.129, −0.012, −0.010, 0.038 and 0.030, respectively. Several regions located on 19 chromosomes were potentially under selection in at least one of the goat breeds. The genomic population tree constructed using all SNPs differentiated breeds based on selection purpose, while genomic population tree built using only SNPs in the most significant region showed a great differentiation between LaMancha and the other breeds. We hypothesized that this region is related to ear morphogenesis. Furthermore, we identified genes potentially related to reproduction traits, adult body mass, efficiency of food conversion, abdominal fat deposition, conformation traits, liver fat metabolism, milk fatty acids, somatic cells score, milk protein, thermo-tolerance and ear morphogenesis.ConclusionsIn general, moderate to high levels of genetic variability were observed for all the breeds and a characterization of runs of homozygosity gave insights into the breeds’ development history. The information reported here will be useful for the implementation of genomic selection and other genomic studies in goats. We also identified various genome regions under positive selection using smoothed FST and hapFLK statistics and suggested genes, which are potentially under selection. These results can now provide a foundation to formulate biological hypotheses related to selection processes in goats.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3610-0) contains supplementary material, which is available to authorized users.

BackgroundBasic understanding of linkage disequilibrium (LD) and population structure, as well as the consistency of gametic phase across breeds is crucial for genome-wide association studies and successful implementation of genomic selection. However, it is still limited in goats. Therefore, the objectives of this research were: (i) to estimate genome-wide levels of LD in goat breeds using data generated with the Illumina Goat SNP50 BeadChip; (ii) to study the consistency of gametic phase across breeds in order to evaluate the possible use of a multi-breed training population for genomic selection and (iii) develop insights concerning the population history of goat breeds.ResultsAverage r2 between adjacent SNP pairs ranged from 0.28 to 0.11 for Boer and Rangeland populations. At the average distance between adjacent SNPs in the current 50 k SNP panel (~0.06 Mb), the breeds LaMancha, Nubian, Toggenburg and Boer exceeded or approached the level of linkage disequilibrium that is useful (r2 > 0.2) for genomic predictions. In all breeds LD decayed rapidly with increasing inter-marker distance. The estimated correlations for all the breed pairs, except Canadian and Australian Boer populations, were lower than 0.70 for all marker distances greater than 0.02 Mb. These results are not high enough to encourage the pooling of breeds in a single training population for genomic selection. The admixture analysis shows that some breeds have distinct genotypes based on SNP50 genotypes, such as the Boer, Cashmere and Nubian populations. The other groups share higher genome proportions with each other, indicating higher admixture and a more diverse genetic composition.ConclusionsThis work presents results of a diverse collection of breeds, which are of great interest for the implementation of genomic selection in goats. The LD results indicate that, with a large enough training population, genomic selection could potentially be implemented within breed with the current 50 k panel, but some breeds might benefit from a denser panel. For multi-breed genomic evaluation, a denser SNP panel also seems to be required.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-015-0220-1) contains supplementary material, which is available to authorized users.

BackgroundKnowledge on the levels of linkage disequilibrium (LD) across the genome, persistence of gametic phase between breed pairs, genetic diversity and population structure are important parameters for the successful implementation of genomic selection. Therefore, the objectives of this study were to investigate these parameters in order to assess the feasibility of a multi-herd and multi-breed training population for genomic selection in important purebred and crossbred pig populations in Canada. A total of 3,057 animals, representative of the national populations, were genotyped with the Illumina Porcine SNP60 BeadChip (62,163 markers).ResultsThe overall LD (r 2) between adjacent SNPs was 0.49, 0.38, 0.40 and 0.31 for Duroc, Landrace, Yorkshire and Crossbred (Landrace x Yorkshire) populations, respectively. The highest correlation of phase (r) across breeds was observed between Crossbred animals and either Landrace or Yorkshire breeds, in which r was approximately 0.80 at 1 Mbp of distance. Landrace and Yorkshire breeds presented r ≥ 0.80 in distances up to 0.1 Mbp, while Duroc breed showed r ≥ 0.80 for distances up to 0.03 Mbp with all other populations. The persistence of phase across herds were strong for all breeds, with r ≥ 0.80 up to 1.81 Mbp for Yorkshire, 1.20 Mbp for Duroc, and 0.70 Mbp for Landrace. The first two principal components clearly discriminate all the breeds. Similar levels of genetic diversity were observed among all breed groups. The current effective population size was equal to 75 for Duroc and 92 for both Landrace and Yorkshire.ConclusionsAn overview of population structure, LD decay, demographic history and inbreeding of important pig breeds in Canada was presented. The rate of LD decay for the three Canadian pig breeds indicates that genomic selection can be successfully implemented within breeds with the current 60 K SNP panel. The use of a multi-breed training population involving Landrace and Yorkshire to estimate the genomic breeding values of crossbred animals (Landrace × Yorkshire) should be further evaluated. The lower correlation of phase at short distances between Duroc and the other breeds indicates that a denser panel may be required for the use of a multi-breed training population including Duroc.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-017-0473-y) contains supplementary material, which is available to authorized users.

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.

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.

Improvement of prediction accuracy of estimated breeding values (EBVs) can lead to increased profitability for swine breeding companies. This study was performed to compare the accuracy of different popular genomic prediction methods and traditional best linear unbiased prediction (BLUP) for future performance of back-fat thickness (BFT), average daily gain (ADG), and loin muscle depth (LMD) in Canadian Duroc, Landrace, and Yorkshire swine breeds. In this study, 17,019 pigs were genotyped using Illumina 60K and Affymetrix 50K panels. After quality control and imputation steps, a total of 41,304, 48,580, and 49,102 single-nucleotide polymorphisms remained for Duroc (n = 6,649), Landrace (n = 5,362), and Yorkshire (n = 5,008) breeds, respectively. The breeding values of animals in the validation groups (n = 392–774) were predicted before performance test using BLUP, BayesC, BayesCπ, genomic BLUP (GBLUP), and single-step GBLUP (ssGBLUP) methods. The prediction accuracies were obtained using the correlation between the predicted breeding values and their deregressed EBVs (dEBVs) after performance test. The genomic prediction methods showed higher prediction accuracies than traditional BLUP for all scenarios. Although the accuracies of genomic prediction methods were not significantly (P > 0.05) different, ssGBLUP was the most accurate method for Duroc-ADG, Duroc-LMD, Landrace-BFT, Landrace-ADG, and Yorkshire-BFT scenarios, and BayesCπ was the most accurate method for Duroc-BFT, Landrace-LMD, and Yorkshire-ADG scenarios. Furthermore, BayesCπ method was the least biased method for Duroc-LMD, Landrace-BFT, Landrace-ADG, Yorkshire-BFT, and Yorkshire-ADG scenarios. Our findings can be beneficial for accelerating the genetic progress of BFT, ADG, and LMD in Canadian swine populations by selecting more accurate and unbiased genomic prediction methods.

A multitude of model and non-model species studies have now taken full advantage of powerful high-throughput genotyping advances such as SNP arrays and genotyping-by-sequencing (GBS) technology to investigate the genetic basis of trait variation. However, due to incomplete genome coverage by these technologies, the identified SNPs are likely in linkage disequilibrium (LD) with the causal polymorphisms, rather than be causal themselves. In addition, researchers could benefit from annotations for the identified candidate SNPs and, simultaneously, for all neighboring genes in genetic linkage. In such case, LD extent estimation surrounding the candidate SNPs is required to determine the regions encompassing genes of interest. We describe here an automated pipeline, “LD-annot,” designed to delineate specific regions of interest for a given experiment and candidate polymorphisms on the basis of LD extent, and furthermore, provide annotations for all genes within such regions. LD-annot uses standard file formats, bioinformatics tools, and languages to provide identifiers, coordinates, and annotations for genes in genetic linkage with each candidate polymorphism. Although the focus lies upon SNP arrays and GBS data as they are being routinely deployed, this pipeline can be applied to a variety of datasets as long as genotypic data are available for a high number of polymorphisms and formatted into a vcf file. A checkpoint procedure in the pipeline allows to test several threshold values for linkage without having to rerun the entire pipeline, thus saving the user computational time and resources. We applied this new pipeline to four different sample sets: two breeding populations GBS datasets, one within-pedigree SNP set coming from whole genome sequencing (WGS), and a very large multi-varieties SNP dataset obtained from WGS, representing variable sample sizes, and numbers of polymorphisms. LD-annot performed within minutes, even when very high numbers of polymorphisms are investigated and thus will efficiently assist research efforts aimed at identifying biologically meaningful genetic polymorphisms underlying phenotypic variation. LD-annot tool is available under a GPL license from .

No abstract