2026-04-14T04:56:14Zhttps://recercat.cat/oai/requestoai:recercat.cat:20.500.12327/31302025-10-22T11:34:22Zcom_2072_4428com_2072_4427col_2072_487898
RECERCAT
author
Ibanez-Escriche, Noelia
author
Fernando, Rohan L
author
Toosi, Ali
author
Dekkers, Jack CM
other
Producció Animal
group
Genètica i Millora Animal
2025-10-22T11:34:22Z
2025-10-22T11:34:22Z
2009-01-15
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
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.
eng
Attribution 4.0 International
Genomic selection of purebreds for crossbred performance
info:eu-repo/semantics/article