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.

中文翻译：

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将基因组信息纳入南非美利奴羊生产和繁殖性状的遗传评估中

基因组选择 (GS) 在澳大利亚、新西兰、法国和爱尔兰的绵羊育种计划中已很常见，但需要在南非 (SA) 进行验证。本研究旨在比较系谱 BLUP (ABLUP) 和单步基因组 BLUP (ssGBLUP) 对南非美利奴羊生产和繁殖性状的预测能力、偏差和离散度。这项研究中的动物来自五个研究和五个商业美利奴羊群，包括 54,072 至 79,100 个断奶重量 (WW)、一岁体重 (YW)、纤维直径 (FD)、洁净羊毛重量 (CFW) 和短纤维长度 ( SL）。对于繁殖性状，该数据集包括 17,268 只母羊的 58,744 条重复记录，包括出生羔羊数量 (NLB)、断奶羔羊数量 (NLW) 和断奶总重量 (TWW)。使用 PreGS90 软件结合 2811 只具有中等密度 (~50 K) 基因型的动物和 88,600 只动物的谱系来计算单步关系矩阵H 。评估基于使用 ASREML V4.2 软件的单性状分析。预测的准确性通过交叉验证设计根据“LR 方法”进行评估。验证候选人根据场景一分配：在某个时间点之后出生；场景二：出生在特定的羊群中。在情景 I 中，2006 年至 2013 年截止点之间参考群体的基因分型率接近具有表型的动物的 7% 及其父系的 20%。对于繁殖性状，2006 年至 2011 年出生的母羊中约有 20% 及其父系母羊的 15% 进行了基因分型。场景 I 验证集中的候选者及其父系的基因分型率为 3.7%（生产）和 11.4%（繁殖）。允许雄性在参考组和验证组中产生后代。在场景 I 中，ssGBLUP 提高了除 NLB 之外的所有性状的预测准确性，范围在 FD 的 +8% (0.62–0.67) 和 WW 的 +44% (0.36–0.52) 之间。尽管参考人群规模不大，但这表明准确性有希望的提高。在“跨群体验证”（场景 II）中，总体准确度较低，但 ABLUP 和 ssGBLUP 之间的差异较大，TWW 为 +17% (0.12–0.14)，WW 为 +117% (0.18–0.39)。几乎没有迹象表明存在严重偏差，但某些性状容易过度分散，而基因组信息的使用并没有改善这一点。这些结果首次验证了南非美利奴羊基因组信息的益处。然而，由于生产性状具有中等遗传性且易于在早期测量，因此未来的研究应旨在最好地利用 GS​​ 方法对性别限制和/或低遗传性状（如 NLW）进行遗传预测。GS 方法应与专门努力相结合，以增加部门之间的遗传联系并改善商业鸡群的表型。