Genomic selection for crossbred performance

Crossbreeding programs are used intensively in livestock production systems. The aim of selective-breeding programs in many of these systems is to maximize crossbred performance (CP), where selection is carried out within pure-lines using data from purebred animals. However, selection based on performance of purebred parents may not maximize performance of their crossbred descendants due to the genetic and environmental differences between purebred and crossbred animals. Genomic selection (GS) can be used to select purebreds for CP and has some advantages, such as it does not require pedigree information on crossbreds and can makes accommodating non-additive gene action easier. The overall objective of this PhD project was to assess the possibilities of using dominance in genomic crossbreeding programs. Dominance is important in crossbreeding programs as it is the likely genetic basis of heterosis. It is also expected to be one of the factors causing the genetic correlations between crossbred and purebred performance to be smaller than one. Using stochastic simulations, response to selection in a two-way crossbreeding system was investigated. Under the hypothesis that performance of crossbred animals differs from that of purebred animals due to dominance, it was found that a dominance model can be used for GS of purebred individuals for CP, without using crossbred data. Furthermore, results showed that, if the correlation of linkage disequilibrium phase between pure lines is high, accuracy of selection can be increased by combining the two pure lines into a single reference population to estimate marker effects. In addition, response to selection of crossbreds with either a purebred or crossbred training population under a dominance model was compared. It was found that response to selection in crossbreeding programs can be increased by training on crossbred genotypes and phenotypes. Moreover, if the reference population is sufficiently large and both pure lines are not very closely related, tracing the line origin of alleles in crossbreds improved the accuracy of genomic prediction. Finally, real data of purebred Landrace and Yorkshire pigs were analysed to compare the predictive ability of genomic prediction models with either additive, or both additive and dominance effects, when the validation criterion was CP. The results showed some gains in prediction accuracy for CP by including dominance and combining both pure lines into a single reference population for training. In conclusion, GS can be used for efficient selection of purebreds for CP by addressing the factors that cause the genetic correlations between crossbreds and purebreds to be lower than one.

Bibliographic Details

Main Author:	Esfandyari, Hadi
Other Authors:	van Arendonk, Johan
Format:	Doctoral thesis biblioteca
Language:	English
Published:	Wageningen University
Subjects:	Life Science,
Online Access:	https://research.wur.nl/en/publications/genomic-selection-for-crossbred-performance
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