GenoMiX: Utilizing crossbred information to accelerate genetic progress

Crossbred performance is the ultimate breeding goal of many livestock breeding programs. At present, selection is however mainly based on performance measured on purebred animals, in highly controlled environments. For many traits, the genetic correlation between purebred and crossbred performance 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. The overall objective of this project is to improve accuracy of prediction of phenotypes of crossbred animals by utilizing the total genetic variance, including additive, dominance and epistatic effects. Few methods thus far have been developed that can simultaneously estimate these effects, and use these for prediction of phenotypes. Likewise, little is known of optimal design of breeding programs using genomic selection that uses both purebred and crossbred performance records of multiple lines and crosses. The project focusses on developing parametric and non-parametric models that efficiently estimate additive and non-additive effects, required to improve accuracy of prediction of crossbred performance. For genomic prediction across multiple lines, we will partition relationships into a component due to close pedigree relationships within line, and relationships due to linkage disequilibrium over short distances, which also acts between lines. Parametric models will include additive and dominance effects, while non-parametric models will also include epistasis. To allow inclusion of epistasis, we will extend Gaussian Process Regression (GPR) methods to predict phenotypes for the future crossbred offspring of purebred breeding animals. The power of GPR to quantify non-additive effects has recently been demonstrated in yeast, where nearly the full broad-sense heritability was accounted for and phenotypes were predicted with significantly lower mean-squared error than a linear mode l. Connected to the research on prediction models, optimal breeding program designs for accurate estimation of additive and non-additive effects will be derived. This combination of development of prediction tools and optimization of genomic breeding programs has the potential to considerably strengthen crossbreeding programs of the industry.

  • Projectnummer / Project number: 14291
  • Deelnemende kennisinstellingen / Participating institutes: Universitair Medisch Centrum - St. Radboud - Wageningen Universiteit en Researchcentrum
  • Gebruikers / Users: 4 bedrijven / 4 companies 
  • Projectleider / Project leader: dr. ir. P. Bijma - Wageningen Universiteit & Researchcentrum – Dierwetenschappen - Fokkerij & Genetica (ABG)
  • Type project / Type project: Lopend
  • Startdatum / Start date: 1-11-2015
  • Programma / Programme: Partnership-programma STW-Breed4Food
  • Vakgebied / Discipline: Dierwetenschappen


Mw. dr. T. (Titia) Plantinga (Senior programmamedewerker) Mw. dr. T. (Titia) Plantinga (Senior programmamedewerker) +31 (0)30 6001307