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Background South Africa and Australia shares multiple important sheep breeds. For some of these breeds, genomic breeding values are provided to breeders in Australia, but not yet in South Africa. Combining genomic resources could facilitate development for across country selection, but the influence of population structures could be important to the compatability of genomic data from varying origins. The genetic structure within and across breeds, countries and strains was evaluated in this study by population genomic parameters derived from SNP-marker data. Populations were first analysed by breed and country of origin and then by subpopulations of South African and Australian Merinos. Results Mean estimated relatedness according to the genomic relationship matrix varied by breed (-0.11 to 0.16) and bloodline (-0.08 to 0.06) groups and depended on co-ancestry as well as recent genetic links. Measures of divergence across bloodlines (FST: 0.04–0.12) were sometimes more distant than across some breeds (FST: 0.05–0.24), but the divergence of common breeds from their across-country equivalents was weak (FST: 0.01–0.04). According to mean relatedness, FST, PCA and Admixture, the Australian Ultrafine line was better connected to the SA Cradock Fine Wool flock than with other AUS bloodlines. Levels of linkage disequilibrium (LD) between adjacent markers was generally low, but also varied across breeds (r2: 0.14–0.22) as well as bloodlines (r2: 0.15–0.19). Patterns of LD decay was also unique to breeds, but bloodlines differed only at the absolute level. Estimates of effective population size (Ne) showed genetic diversity to be high for the majority of breeds (Ne: 128–418) but also for bloodlines (Ne: 137–369). Conclusions This study reinforced the genetic complexity and diversity of important sheep breeds, especially the Merino breed. The results also showed that implications of isolation can be highly variable and extended beyond breed structures. However, knowledge of useful links across these population substructures allows for a fine-tuned approach in the combination of genomic resources. Isolation across country rarely proved restricting compared to other structures considered. Consequently, research into the accuracy of across-country genomic prediction is recommended.

International audience; Background The objectives of this study were to investigate the accuracy of genotype imputation from low (12k) to medium (50k Illumina-Ovine) SNP (single nucleotide polymorphism) densities in purebred and crossbred Merino sheep based on a random or selected reference set and to evaluate the impact of using imputed genotypes on accuracy of genomic prediction.MethodsImputation validation sets were composed of random purebred or crossbred Merinos, while imputation reference sets were of variable sizes and included random purebred or crossbred Merinos or a group of animals that were selected based on high genetic relatedness to animals in the validation set. The Beagle software program was used for imputation and accuracy of imputation was assessed based on the Pearson correlation coefficient between observed and imputed genotypes. Genomic evaluation was performed based on genomic best linear unbiased prediction and its accuracy was evaluated as the Pearson correlation coefficient between genomic estimated breeding values using either observed (12k/50k) or imputed genotypes with varying levels of imputation accuracy and accurate estimated breeding values based on progeny-tests.ResultsImputation accuracy increased as the size of the reference set increased. However, accuracy was higher for purebred Merinos that were imputed from other purebred Merinos (on average 0.90 to 0.95 based on 1000 to 3000 animals) than from crossbred Merinos (0.78 to 0.87 based on 1000 to 3000 animals) or from non-Merino purebreds (on average 0.50). The imputation accuracy for crossbred Merinos based on 1000 to 3000 other crossbred Merino ranged from 0.86 to 0.88. Considerably higher imputation accuracy was observed when a selected reference set with a high genetic relationship to target animals was used vs. a random reference set of the same size (0.96 vs. 0.88, respectively). Accuracy of genomic prediction based on 50k genotypes imputed with high accuracy (0.88 to 0.99) decreased only slightly (0.0 to 0.67 % across traits) compared to using observed 50k genotypes. Accuracy of genomic prediction based on observed 12k genotypes was higher than accuracy based on lowly accurate (0.62 to 0.86) imputed 50k genotypes.

Genotyping sheep for genome-wide SNPs at lower density and imputing to a higher density would enable cost-effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low-density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50-475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single-breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.