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Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single‐step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single‐step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single‐trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the 'LR‐method' by a cross‐validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut‐off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut‐off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62–0.67) for FD and +44% (0.36–0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the 'across flock validation' (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12–0.14) for TWW and +117% (0.18–0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex‐limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks. [ABSTRACT FROM AUTHOR]

Contemporary group (CG) estimates of different phenotypes have not been widely explored for pigs. The objective of this study was to extend the traits used to derive environmental descriptors of the growing pig, to include CG estimates of early growth between birth and start of feed intake test (EADG), growth during feed intake test (TADG), lifetime growth (ADG), daily feed intake (DFI), backfat (BF) and muscle depth (MD). Pedigree and performance records (n = 7,746) from a commercial Australian piggery were used to derive environmental descriptors based on CG estimates of these six traits. The CG estimates of growth traits described different aspects of the environment from the CG estimates of carcass traits (r < 0.10). These definitions of the environment then were used in reaction norm analysis of growth, to evaluate sire‐by‐environment interaction (Sire × E) for growth. The most appropriate reaction norm model to evaluate Sire × E for growth was dependent on the environmental descriptor used. If the trait used to derive an environmental descriptor was distinctly different from growth (e.g., BF and MD), CG as an additional random effect was required in the model. If not included, inflated common litter effect and sire intercept variance suggest there was unaccounted environmental variability. There was no significant Sire × E using any of the definitions of the environment, with estimated variance in sire slopes largest when environments were defined by BF (σ^bi2 = 97 ± 83 (g/day)2), followed by environments defined by DFI (σ^bi2 = 39 ± 101 (g/day)2). While there appears to be differences in ability to detect Sire × E, improved data structure is required to better assess these environmental descriptors based on alternative traits. The ideal trait, or combination of traits, used to derive environmental descriptors may be unique for individual herds. Therefore, multiple phenotypes should be further explored for the evaluation of Sire × E for growth in the pig. [ABSTRACT FROM AUTHOR]