Genomic Prediction for Hybrid Performance in Rapeseed

In oilseed rape breeding programs, performance prediction of parental combinations is of fundamental importance. Due to the phenomenon of heterosis, per se performance is not a reliable indicator for F1-hybrid performance, and selection of well-paired parents based only on phenotype requires the testing of large quantities of hybrid combinations in extensive field trials. However, the number of potential hybrid combinations generally dramatically exceeds normal breeding capacity and budget. Integration of genomic selection (GS) could substantially streamline rapeseed breeding programs. GS models can be used to predict the performance of untested individuals based only on their genotypic profiles, using marker effects previously calculated in a training population. This allows for a preselection of promising genotypes, enabling a more efficient allocation of resources. The Illumina Brassica 60k SNP genotyping array provides a cheap and straightforward platform for hybrid prediction in commercial breeding programs based on whole-genome marker data. We evaluated the usefulness of the 60k SNP array for genomic prediction with regard to marker density and genome-wide linkage disequilibrium. We further compared different linear and Bayesian prediction models that consider general and specific combining ability (GCA and SCA, respectively). A total of 448 hybrids were produced in a commercial breeding program from unbalanced crosses between 220 paternal double haploid lines and five maternal male-sterile inbred lines. Predictive ability was evaluated for seven agronomical traits using 200 cross-validations, randomly partitioning the hybrid population into training and validation panels. Our results emphasize the high potential of GS for rapeseed breeding.