WP4 - Genomic indices for multi-breed selection in different environments

WP Leader: Mogens Lund - Aarhus University (AU)
Associate WP leader: Jan Lassen - AU
Involved partners: AU, INRA, TEAGASC, LfL, IDELE, SRUC, DLO // WR, ALLICE, INTERBULL
Genomic breeding indices exist for different species internationally to rank potential candidates. Such indices are mostly designed for specific breeds within a specific environment and do often not include important traits of efficiency and resilience. This is due to both a lack of data and the fact that genotype-environment interactions cause difficulties in defining appropriate traits. Concerning efficiency and resilience, genomic ranking will be highly different between environments for these two traits since production circumstances are highly variable between European regions. In addition, it is difficult for many smaller breeds to achieve a sufficiently high accuracy of genomic predictions due to the limited size of the reference population.

​Breeding decisions are suboptimal at the moment because they consider neither the full genetic diversity (which is in fact available) nor the specific environmental influences. Therefore, genomic prediction models need to be improved. WP4 will further develop and apply these models.
Objectives
WP4 aims to develop genomic indices for multi-breed selection on resilience and efficiency. These indices will be directly transferred to the relevant industry partners in conjunction with WP7. In addition, they will be key inputs for the development of both the management indices in WP5 and the predictive modelling in WP6. 
Tasks
  • T4.1 – Multi-breed prediction: Genomic prediction using a multi-breed reference of purebred and crossbred individuals
  • T4.2 – Genomic predictions across multiple environments
  • T4.3 – Multi-trait genomic prediction for production- and feed efficiency
  • T4.4 – Multi-trait genomic prediction for resilience
PROJECT
WORK PACKAGES
GenTORE is a Horizon 2020 project running from 1 June 2017 to 31 May 2022. This research received funding from the European Union's H2020 Research and Innovation Program under agreement No. 727213. Privacy Policy