Single-step genomic evaluation of lambing ease in Australian terminal sire breed sheep

Abstract

Context Australian sheep breeding values (ASBVs) for the categorical trait of lambing ease are routinely estimated by Sheep Genetics via a threshold model. This has been pedigree-only, and has not utilised genomic information. Aim The present study aimed to update the genetic evaluation model and parameters for lambing ease for terminal sire sheep (dominated by White Suffolk and Poll Dorset breeds). The model includes correlations with birthweight and gestation length. Cross-validation was used to determine the value of the improved models and the inclusion of genomic information. Methods New data-preparation pipelines were developed to accommodate improved data-filtering methods and contemporary group construction. Genetic parameters, including correlations among traits, were estimated using continuous and threshold models, with various combinations of effects in mixed animal models. Cross-validation of breeding values was performed against progeny performance, by using forward prediction. Key results The increased volume of data, improved data preparation steps and enhanced evaluation software now allow a more complex model to be fitted, including maternal, sire by flock-year and genetic group effects, which were significant for all traits, along with the inclusion of multiple sire groups in the pedigree. However, the inclusion of the direct-maternal covariance and sire by flock-year terms resulted in unrealistically inflated estimates of some components, and thus the final covariance matrices required some adjustments. Cross-validation of breeding values was performed against progeny performance using forward prediction. For all traits, the phenotype accuracies and estimated breeding value correlations were higher from the new model without genomics than were those from the current routine evaluation. The benefit from including genomic information based on cross-validation is minimal currently but is expected to improve as the size of the reference population grows. Further work is required to define acceptable data-quality thresholds for the construction of datasets for routine breeding value estimation. Conclusions The new model and parameters resulted in ASBVs with an improved predictive ability, with increased accuracy and reduced bias compared with the current analysis. Furthermore, a small increase in accuracy was observed for all traits from utilising genomic information in the model. Implications The new genetic evaluation procedures and models will be used to update those being applied in the routine Sheep Genetics evaluation system and also support further index development for the terminal sire breeds in Australia.