New software developments enable efficient large-scale routine genomic evaluations

In collaboration with the Technical University of Delft and VORtech, a company specialized in software engineering, researchers at Wageningen University & Research, Animal Breeding and Genomics (WUR-ABG), recently implemented new algorithms and computational strategies, resulting in an efficient software that allows large routine single-step genomic evaluations to predict genomic breeding values more accurately.

First tests of this new software were performed on large datasets provided by the Breed4Food partner CRV BV and the Irish Cattle Breeding Federation (ICBF). Their results showed promising efficiency.


Efficient large-scale genomic evaluations

The four international breeding companies involved in Breed4Food (CRV BV, Hendrix Genetics, Topigs Norsvin, and Cobb Europe) use the so-called single-step genomic methods to estimate genomic breeding values for their selection candidates. The single-step methods are appealing due to their ability of combining traditionally recorded data with recently recorded genomic information. Unfortunately, the fast increase of the amount of genomic information limits the feasibility of routine single-step genomic evaluations with software and algorithms currently used.


New algorithms and computational strategies in animal breeding

Within Breed4Food, and in collaboration with Prof. Kees Vuik of the Technical University of Delft and with VORtech, researchers at WUR-ABG have implemented new algorithms and computational strategies in their software. This allows to perform large single-step genomic evaluations efficiently with millions of phenotypes and genotypes.


First results are promising

First tests of this new software were performed on large datasets provided by the Breed4Food partner CRV BV and the Irish Cattle Breeding Federation. These results showed promising efficiency of the new software, which will allow to perform routinely large single-step genomic evaluations. Furthermore, it has been shown that these new computational strategies and algorithms could be easily implemented in software currently used in animal breeding.

Published articles available: https://doi.org/10.1186/s12711-019-0472-8 and https://doi.org/10.1186/s12711-020-00543-9


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