Genomic selection is revolutionizing breeding programs worldwide. It requires establishing a reference population to estimate the so-called SNP-effects used to predict genomic breeding values. For breeding companies with multiple lines or breeds, an important question is whether it is beneficial to use a reference population across those lines or breeds, in order to reduce implementation costs.
The dimensions of genomics datasets used in breeding programs are rapidly increasing. CRV currently has a reference population of ~30,000 Holstein Friesian dairy bulls, partly bulls previously tested in the Netherlands, and partly foreign bulls made available through the EuroGenomics consortium. Furthermore, CRV has smaller reference populations consisting of 1,300 – 2,500 progeny tested bulls, for Jerseys and Friesians held under specific circumstances (“grazing” in New Zealand). Further expansion of those reference populations by genotyping large numbers of cows that are phenotyped for a large number of traits is currently underway. Several issues that are important to make the most out of this great resource are addressed in the project GenomXL.
Many economically important traits in livestock and plant species are genetically complex; i.e. traits are affected by many genes and by environment. The use of whole genome sequence data can lead to a better understanding and better models and predictions of (the background of) these traits. Eventually, this will lead to a more efficient livestock and crop production and, for example, better health and welfare of animals. Therefore, the use of sequence data for genomic prediction in animals and plants is investigated.
Commercial poultry and pig breeding programs make use of crossbreeding schemes to exploit heterosis. The development of a reliable method for predicting heterosis will greatly improve the efficiency of these breeding schemes by reducing their dependency on time-consuming and expensive field-tests of multiple pure-line combinations. We are investigating the applicability and accuracy of using genetic markers to predict heterosis, using data on White Leghorns from ISA-Hendrix Genetics.