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Knowledge of population structure and breed composition of a population can be advantageous for a number of reasons; these include designing optimal (cross)breeding strategies in order to maximise non-additive genetic effects, maintaining flockbook integrity by authenticating animals being registered and as a quality control measure in the genotyping process. The objectives of the present study were to 1) describe the population structure of 24 sheep breeds, 2) quantify the breed composition of both flockbook-recorded and crossbred animals using single nucleotide polymorphism BLUP (SNP-BLUP), and 3) quantify the accuracy of breed composition prediction from low-density genotype panels containing between 2000 and 6000 SNPs. In total, 9334 autosomal SNPs on 11 144 flockbook-recorded animals and 1172 crossbred animals were used. The population structure of all breeds was characterised by principal component analysis (PCA) as well as the pairwise breed fixation index (Fst). The total number of animals, all of which were purebred, included in the calibration population for SNP-BLUP was 2579 with the number of animals per breed ranging from 9 to 500. The remaining 9559 flockbook-recorded animals, composite breeds and crossbred animals represented the test population; three breeds were excluded from breed composition prediction. The breed composition predicted using SNP-BLUP with 9334 SNPs was considered the gold standard prediction. The pairwise breed Fst ranged from 0.040 (between the Irish Blackface and Scottish Blackface) to 0.282 (between the Border Leicester and Suffolk). Principal component analysis revealed that the Suffolk from Ireland and the Suffolk from New Zealand formed distinct, non-overlapping clusters. In contrast, the Texel from Ireland and that from New Zealand formed integrated, overlapping clusters. Composite animals such as the Belclare clustered close to its founder breeds (i.e., Finn, Galway, Lleyn and Texel). When all 9334 SNPs were used to predict breed composition, an animal that had a majority breed proportion predicted to be ≥0.90 was defined as purebred for the present study. As the panel density decreased, the predicted breed proportion threshold, used to identify animals as purebred, also decreased (≥0.85 with 6000 SNPs to ≥0.60 with 2000 SNPs). In all, results from the study suggest that breed composition for purebred and crossbred animals can be determined with SNP-BLUP using ≥5000 SNPs.

The generally low usage of artificial insemination and single-sire mating in sheep, compounded by mob lambing (and lambing outdoors), implies that parentage assignment in sheep is challenging. The objective here was to develop a low-density panel of single nucleotide polymorphisms (SNPs) for accurate parentage verification and discovery in sheep. Of particular interest was where SNP selection was limited to only a subset of chromosomes, thereby eliminating the ability to accurately impute genome-wide denser marker panels. Data used consisted of 10,933 candidate SNPs on 9,390 purebred sheep. These data consisted of 1,876 validated genotyped sire–offspring pairs and 2,784 validated genotyped dam–offspring pairs. The SNP panels developed consisted of 87 SNPs to 500 SNPs. Parentage verification and discovery were undertaken using 1) exclusion, based on the sharing of at least one allele between candidate parent–offspring pairs, and 2) a likelihood-based approach. Based on exclusion, allowing for one discordant offspring–parent genotype, a minimum of 350 SNPs was required when the goal was to unambiguously identify the true sire or dam from all possible candidates. Results suggest that, if selecting SNPs across the entire genome, a minimum of 250 carefully selected SNPs are required to ensure that the most likely selected parent (based on the likelihood approach) was, in fact, the true parent. If restricting the SNPs to just a subset of chromosomes, the recommendation is to use at least a 300-SNP panel from at least six chromosomes, with approximately an equal number of SNPs per chromosome.

Proper quality control of data prior to downstream analyses is fundamental to ensure integrity of results; quality control of genomic data is no exception. While many metrics of quality control of genomic data exist, the objective of the present study was to quantify the genotype and allele concordance rate between called single nucleotide polymorphism (SNP) genotypes differing in GenCall (GC) score; the GC score is a confidence measure assigned to each Illumina genotype call. This objective was achieved using Illumina beadchip genotype data from 771 cattle (12 428 767 genotypes in total post-editing) and 80 sheep (1 557 360 SNPs genotypes in total post-editing) each genotyped in duplicate. The called genotype with the lowest associated GC score was compared to the genotype called for the same SNP in the same duplicated animal sample but with a GC score of >0.90 (assumed to represent the true genotype). The mean genotype concordance rate for a GC score of

Accurate genomic analyses are predicated on access to a large quantity of accurately genotyped and phenotyped animals. Because the cost of genotyping is often less than the cost of phenotyping, interest is increasing in generating genotypes for phenotyped animals. In some instances this may imply the requirement to genotype older animals with greater phenotypic information content. Biological material for these older informative animals may, however, no longer exist. The objective of the present study was to quantify the ability to impute 11 129 single nucleotide polymorphism (SNP) genotypes of non-genotyped animals (in this instance sires) from the genotypes of their progeny with or without including the genotypes of the progenys’ dams (i.e. mates of the sire to be imputed). The impact on the accuracy of genotype imputation by including more progeny (and their dams’) genotypes in the imputation reference population was also quantified. When genotypes of the dams were not available, genotypes of 41 sires with at least 15 genotyped progeny were used for the imputation; when genotypes of the dams were available, genotypes of 21 sires with at least 10 genotyped progeny were used for the imputation. Imputation was undertaken exploiting family and population level information. The mean and variability in the proportion of genotypes per individual that could not be imputed reduced as the number of progeny genotypes used per individual increased. Little improvement in the proportion of genotypes that could not be imputed was achieved once genotypes of seven progeny and their dams were used or genotypes of 11 progeny without their respective dam’s genotypes were used. Mean imputation accuracy per individual (depicted by both concordance rates and correlation between true and imputed) increased with increasing progeny group size. Moreover, the range in mean imputation accuracy per individual reduced as more progeny genotypes were used in the imputation. If the genotype of the mate of the sire was also used, high accuracy of imputation (mean genotype concordance rate per individual of 0.988), with little additional benefit thereafter, was achieved with seven genotyped progeny. In the absence of genotypes on the dam, similar imputation accuracy could not be achieved even using genotypes on up to 15 progeny. Results therefore suggest, at least for the SNP density used in the present study, that it is possible to accurately impute the genotypes of a non-genotyped parent from the genotypes of its progeny and there is a benefit of also including the genotype of the sire’s mate (i.e. dam of the progeny).

The objective of the present study was to quantify the extent of genetic variation in three health-related traits namely dagginess, lameness and mastitis, in an Irish sheep population. Each of the health traits investigated pose substantial welfare implications as well as considerable economic costs to producers. Data were also available on four body-related traits, namely body condition score (BCS), live weight, muscle depth and fat depth. Animals were categorised as lambs (

peer-reviewed The economic value of a trait in a breeding objective can be defined as the value of a unit change in an individual trait, while keeping all other traits constant and are widely used in the development of breeding objectives internationally. The objective of this study was to provide a description of the development of economic values for the pertinent traits included in the Irish national sheep breeding objectives using a whole farm system bio-economic model. A total of fourteen traits of economic importance representing maternal, lambing, production and health characteristics were calculated within a whole farm bio-economic model. The model was parameterised to represent an average Irish flock of 107 ewes with a mean lambing date in early March, stocked at 7.5 ewes per hectare and weaning 1.5 lambs per ewe joined to the ram. The economic values (units in parenthesis) calculated for maternal traits were: €39.76 for number of lambs born (per lamb), €0.12 for ewe mature weight cull value (per kg), −€0.57 for ewe mature weight maintenance value (per kg), −€0.09 for ewe mature weight replacement value (per kg) and −€0.84 for ewe replacement rate (per%). The economic values calculated for lambing traits were: €54.84 for lamb surviving at birth (per lamb), −€0.27 and −€0.30 for direct lambing difficulty in single and multiple-bearing ewes, respectively (per%); the corresponding values for maternal single and multiple lambing difficulty (per%) were −€0.25 and −€0.27, respectively. The calculated economic values for production traits were: −€0.25 for days to slaughter (per day), €3.70 for carcass Conformation (per EUROP grade) and −€0.84 for carcass fat (per fat score). The economic values for health traits were: −€0.24 for ewe lameness (per%), −€0.08 for lamb lameness (per%), −€0.25 for mastitis (per%), −€0.34 for dag score (per dag score) and −€0.08 for faecal egg count (per 50 eggs/g). Within the two Irish breeding objectives, the terminal and replacement breeding objective, the greatest emphasis was placed on production traits across both the terminal (62.56%) and replacement (41.65%) breeding objectives. The maternal and lambing traits accounted for the 34.19% and 23.45% of the emphasis within the replacement breeding objective, respectively. Results from this study will enable the implementation of new economic values within the national terminal and replacement Irish sheep breeding objectives which highlights the traits of importance for increasing overall farm profitability.

The objective of the present study was to quantify the accuracy of imputing medium-density single nucleotide polymorphism (SNP) genotypes from lower-density panels (384 to 12,000 SNPs) derived using alternative selection methods to select the most informative SNPs. Four different selection methods were used to select SNPs based on genomic characteristics (i.e., minor allele frequency (MAF) and linkage disequilibrium (LD)) within five sheep breeds (642 Belclare, 645 Charollais, 715 Suffolk, 440 Texel, and 620 Vendeen) separately. Selection methods evaluated included (i) random, (ii) splitting the genome into blocks of equal length and selecting SNPs within block based on MAF and LD patterns, (iii) equidistant location while optimizing MAF, (iv) a combination of MAF, distance from already selected SNPs, and weak LD with the SNP(s) already selected. All animals were genotyped on the Illumina OvineSNP50 Beadchip containing 51,135 SNPs of which 44,040 remained after edits. Within each breed separately, the youngest 100 animals were assumed to represent the validation population; the remaining animals represented the reference population. Imputation was undertaken under three different conditions: (i) SNPs were selected within a given breed and imputed for all breeds individually, (ii) all breeds were collectively used to select SNPs and were included as the reference population, and (iii) the SNPs were selected for each breed separately and imputation was undertaken for all breeds but excluding from the reference population, the breed from which the SNPs were selected. Regardless of SNP selection method, mean animal allele concordance rate improved at a diminishing rate while the variability in mean animal allele concordance rate reduced as the panel density increased. The SNP selection method impacted the accuracy of imputation although the effect reduced as the density of the panel increased. Overall, the most accurate SNP selection method for panels with

Early detection of karyotype abnormalities, including aneuploidy, could aid producers in identifying animals which, for example, would\ud not be suitable candidate parents. Genome-wide genetic marker data in the form of single nucleotide polymorphisms (SNPs) are now\ud being routinely generated on animals. The objective of the present study was to describe the statistics that could be generated from the\ud allele intensity values from such SNP data to diagnose karyotype abnormalities; of particular interest was whether detection of\ud aneuploidy was possible with both commonly used genotyping platforms in agricultural species, namely the Applied BiosystemsTM\ud AxiomTM and the Illumina platform. The hypothesis was tested using a case study of a set of dizygotic X-chromosome monosomy 53,X\ud sheep twins. Genome-wide SNP data were available from the Illumina platform (11 082 autosomal and 191 X-chromosome SNPs) on\ud 1848 male and 8954 female sheep and available from the AxiomTM platform (11 128 autosomal and 68 X-chromosome SNPs) on\ud 383 female sheep. Genotype allele intensity values, either as their original raw values or transformed to logarithm intensity ratio (LRR),\ud were used to accurately diagnose two dizygotic (i.e. fraternal) twin 53,X sheep, both of which received their single X chromosome from\ud their sire. This is the first reported case of 53,X dizygotic twins in any species. Relative to the X-chromosome SNP genotype mean allele\ud intensity values of normal females, the mean allele intensity value of SNP genotypes on the X chromosome of the two females\ud monosomic for the X chromosome was 7.45 to 12.4 standard deviations less, and were easily detectable using either the AxiomTM or\ud Illumina genotype platform; the next lowest mean allele intensity value of a female was 4.71 or 3.3 standard deviations less than the\ud population mean depending on the platform used. Both 53,X females could also be detected based on the genotype LRR although this\ud was more easily detectable when comparing the mean LRR of the X chromosome of each female to the mean LRR of their respective\ud autosomes. On autopsy, the ovaries of the two sheep were small for their age and evidence of prior ovulation was not appreciated. In\ud both sheep, the density of primordial follicles in the ovarian cortex was lower than normally found in ovine ovaries and primary follicle\ud development was not observed. Mammary gland development was very limited. Results substantiate previous studies in other species\ud that aneuploidy can be readily detected using SNP genotype allele intensity values generally already available, and the approach\ud proposed in the present study was agnostic to genotype platform.

peer-reviewed Background Accurate genomic analyses are predicated upon access to accurate genotype input data. The objective of this study was to quantify the reproducibility of genotype data that are generated from the same genotype platform and from different genotyping platforms. Methods Genotypes based on 51,121 single nucleotide polymorphisms (SNPs) for 84 animals that were each genotyped on Illumina and Affymetrix platforms and for another 25 animals that were each genotyped twice on the same Illumina platform were compared. Genotypes based on 11,323 SNPs for an additional 21 animals that were genotyped on two different Illumina platforms by two different service providers were also compared. Reproducibility of the results was measured as the correlation between allele counts and as genotype and allele concordance rates. Results A mean within-animal correlation of 0.9996 was found between allele counts in the 25 duplicate samples that were genotyped on the same Illumina platform and varied from 0.9963 to 1.0000 per animal. The mean (minimum, maximum) genotype and allele concordance rates per animal between the 25 duplicate samples were equal to 0.9996 (0.9968, 1.0000) and 0.9993 (0.9937, 1.0000), respectively. The concordance rate between the two different Illumina platforms was also near 1. A mean within-animal correlation of 0.9738 was found between genotypes that were generated on the Illumina and Affymetrix platforms and varied from 0.9505 to 0.9812 per animal. The mean (minimum, maximum) within-animal genotype and allele concordance rates between the Illumina and Affymetrix platforms were equal to 0.9711 (0.9418, 0.9798) and 0.9845 (0.9695, 0.9889), respectively. The genotype concordance rate across all genotypes increased from 0.9711 to 0.9949 when the SNPs used were restricted to those with three high-resolution genotype clusters which represented 75.2% of the called genotypes. Conclusions and implications Our results suggest that, regardless of the genotype platform or service provider, high genotype concordance rates are achieved especially if they are restricted to high-quality extracted DNA and SNPs that result in high-quality genotypes. Financial support from the Irish Department of Agriculture, Stimulus Research Fund project OVIGEN is gratefully acknowledged. The contribution from the International Sheep Genomics Consortium in the development of the low-density custom genotyping platform is also acknowledged.