Interpretive Summary: Review: optimizing genomic selection for crossbred performance by model improvement and data collection
By Anne Zinn
The practice of crossbreeding allowed breeders to benefit from specific breed characteristics by selecting parental lines for different traits and combining the traits in the crossbreeds. Crossbreeding enables breeders to capitalize on heterosis, which is the phenomenon of superior average performance of crossbreds compared with the average performance of their parental lines. Breeding programs aiming to improve the performance of crossbreds may benefit from genomic prediction of crossbred performance for purebred selection candidates.
To date, several studies have compared accuracy of genomic estimated breeding values and response with selection of strategies that differ in the data or model that was used, but a clear overview of these comparisons does not currently exist. A research team from Wageningen University and Research recently published a review in the Journal of Animal Science focusing on strategies to estimate breeding values for crossbred performance of purebred selection candidates using genomic prediction by comparing genomic prediction strategies that differed in the genomic prediction model used or the data used in the reference population. 27 unique studies were evaluated in this review, two of which used deterministic simulation, 11 used stochastic simulation, and 14 real data.
The review demonstrated that the differences in accuracy and response to selection between strategies depend on several factors, the most important one being purebred-crossbred genetic correlation. It is therefore recommended that accurate estimates of purebred-crossbred genetic correlation of all breeding goal traits is obtained. Additionally, knowledge about the importance of components of purebred-crossbred genetic correlation could help breeders decide which model to use and whether to collect data on animals in a crossbred environment. Future research should focus on the development of a tool that predicts accuracy and response to selection from scenario specific parameters.
The full review can be found on the Journal of Animal Science webpage.