GenoMiX: Utilizing crossbred information to accelerate genetic progress

Crossbred performance is the ultimate breeding goal of many livestock breeding programs. At present, however, selection is mainly based on performance measured on purebred animals, in highly controlled environments. For many traits, the genetic correlation between purebred and crossbred performance (rpc) is considerably lower than unity. This indicates that selection based on purebred performance ignores part of the genetic variation, leading to suboptimal selection responses in crossbred performance.

In this project, we showed that the rpc can considerably differ from unity for several traits by reviewing the rpc for pigs and estimating it for body weight in broilers. Estimated rpc values were shown to be more precise using genotype-based compared to pedigree-based models. Theoretical work showed that rpc values can be lower than one due to interactions within and between genes (dominance and epistasis), leading to differences in genetic trait expression between purebred and crossbreds. The rpc decreases when the genetic distance between parental lines increases. Our results also show that in specific situations the rpc value can be derived from genetic parameters in and genetic correlations between the parental lines, without requiring to collect crossbred data. Within this project, we also developed a multi-population genomic relationship matrix and investigated the properties of genetic markers required for an unbiased estimate of the genetic correlations between populations.

Moreover, we evaluated genomic prediction for crossbred performance in purebred lines. Our empirical results in broilers show that crossbred data improves the accuracy of breeding values for a trait with an rpc of 0.8, but not for a trait with an rpc of 0.96. Using simulations, we showed that in general the benefit of a crossbred reference population becomes larger when the crossbred population is more related to the purebred selection candidates, the rpc is lower, or the reference population is larger.

Finally, we developed a machine learning method for the prediction of genotypic values using Bayesian neural networks. We found that our method provided slightly more accurate predictions of genotypic values than ordinary methods when the number of causal loci was small. We concluded that sparse neural networks are promising for prediction of genotypic values in animal breeding, although large computational costs can hinder its use in practice.

Published results in this project are:


Duenk, P. 2020. Genetics of crossbreeding. PhD thesis.

Duenk, P., P. Bijma, M. P. L. Calus, Y. C. J. Wientjes, and J. H. J. Van der Werf. 2020. The impact of non-additive effects on the genetic correlation between populations. G3 (Bethesda). 10:783-795.

Duenk, P., M. P. L. Calus, Y. C. J. Wientjes, V. P. Breen, J. M. Henshall, R. Hawken, and P. Bijma. 2019. Validation of genomic predictions for body weight in broilers using crossbred information and considering breed-of-origin of alleles. Genet. Sel. Evol. 51:38.

Duenk, P., M.P.L. Calus, Y.C.J. Wientjes, V.P. Breen, J.M. Henshall, R.J. Hawken, and P. Bijma. 2019. Estimating the purebred-crossbred genetic correlation of body weight in broiler chicken with pedigree or genomic relationships. Genet. Sel. Evol. 51:6.

Wientjes, Y. C. J., M. P. L. Calus, P. Duenk, and P. Bijma. 2018. Required properties for markers used to calculate unbiased estimates of the genetic correlation between populations. Genet. Sel. Evol. 50:65.

Wientjes, Y. C. J. and M. P. L. Calus. 2017. BOARD INVITED REVIEW: The purebred-crossbred correlation in pigs: A review of theory, estimates, and implications. J. Anim. Sci. 95:3467-3478.

Duenk, P., M. P. L. Calus, Y. C. J. Wientjes, and P. Bijma. 2017. Benefits of dominance over additive models for the estimation of average effects in the presence of dominance. G3 (Bethesda). 7:3405-3414.

Wientjes, Y. C. J., P. Bijma, J. Vandenplas, and M. P. L. Calus. 2017. Multi-population genomic relationships for estimating current genetic variances within and genetic correlations between populations. Genetics. 207:503-515.

Project Leader: Piter Bijma

Project supporters:

project partner Topigs Norsvin  project partner Cobb  project partner CRV  project partner Hendrix Genetics


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