2026-04-14T07:06:42Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/31302025-10-22T11:34:22Zcom_2072_4428com_2072_4427col_2072_487898

Genomic selection of purebreds for crossbred performance Ibanez-Escriche, Noelia Fernando, Rohan L Toosi, Ali Dekkers, Jack CM Producció Animal Genètica i Millora Animal Background: One of the main limitations of many livestock breeding programs is that selection is in pure breeds housed in high-health environments but the aim is to improve crossbred performance under field conditions. Genomic selection (GS) using high-density genotyping could be used to address this. However in crossbred populations, 1) effects of SNPs may be breed specific, and 2) linkage disequilibrium may not be restricted to markers that are tightly linked to the QTL. In this study we apply GS to select for commercial crossbred performance and compare a model with breed-specific effects of SNP alleles (BSAM) to a model where SNP effects are assumed the same across breeds (ASGM). The impact of breed relatedness (generations since separation), size of the population used for training, and marker density were evaluated. Trait phenotype was controlled by 30 QTL and had a heritability of 0.30 for crossbred individuals. A Bayesian method (Bayes-B) was used to estimate the SNP effects in the crossbred training population and the accuracy of resulting GS breeding values for commercial crossbred performance was validated in the purebred population. Results: Results demonstrate that crossbred data can be used to evaluate purebreds for commercial crossbred performance. Accuracies based on crossbred data were generally not much lower than accuracies based on pure breed data and almost identical when the breeds crossed were closely related breeds. The accuracy of both models (ASGM and BSAM) increased with marker density and size of the training data. Accuracies of both models also tended to decrease with increasing distance between breeds. However the effect of marker density, training data size and distance between breeds differed between the two models. BSAM only performed better than AGSM when the number of markers was small (500), the number of records used for training was large (4000), and when breeds were distantly related or unrelated. Conclusion: In conclusion, GS can be conducted in crossbred population and models that fit breed-specific effects of SNP alleles may not be necessary, especially with high marker density. This opens great opportunities for genetic improvement of purebreds for performance of their crossbred descendents in the field, without the need to track pedigrees through the system. 2025-10-22T11:34:22Z 2025-10-22T11:34:22Z 2025-10-22T11:34:22Z 2009-01-15 info:eu-repo/semantics/article Ibáñez-Escriche, Noelia, Rohan L Fernando, Ali Toosi, and Jack Cm Dekkers. 2009. “Genomic Selection of Purebreds for Crossbred Performance.” Genetics Selection Evolution 41 (1): 12. doi: 10.1186/1297-9686-41-12 0999-193X http://hdl.handle.net/20.500.12327/3130 https://doi.org/10.1186/1297-9686-41-12 eng Genetics Selection Evolution http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Attribution 4.0 International BMC