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Rice diversity panel provides accurate genomic predictions for complex traits in the progenies of biparental crosses involving members of the panel
- Ben Hassen, M., Cao, T.V., Bartholomé, J., Orasen, G., Colombi, C., Rakotomalala, J., Razafinimpiasa, L., Bertone, C., Biselli, C., Volante, A., Desiderio, F., Jacquin, L., Valè, G., Ahmadi, N.
- Theoretical and applied genetics 2018 v.131 no.2 pp. 417-435
- breeding programs, crossing, gene frequency, genomics, heritability, linkage disequilibrium, models, pedigree, phenotype, plant improvement, prediction, progeny, rice, statistical analysis
- KEY MESSAGE: Rice breeding programs based on pedigree schemes can use a genomic model trained with data from their working collection to predict performances of progenies produced through rapid generation advancement. So far, most potential applications of genomic prediction in plant improvement have been explored using cross validation approaches. This is the first empirical study to evaluate the accuracy of genomic prediction of the performances of progenies in a typical rice breeding program. Using a cross validation approach, we first analyzed the effects of marker selection and statistical methods on the accuracy of prediction of three different heritability traits in a reference population (RP) of 284 inbred accessions. Next, we investigated the size and the degree of relatedness with the progeny population (PP) of sub-sets of the RP that maximize the accuracy of prediction of phenotype across generations, i.e., for 97 F₅–F₇ lines derived from biparental crosses between 31 accessions of the RP. The extent of linkage disequilibrium was high (r ² = 0.2 at 0.80 Mb in RP and at 1.1 Mb in PP). Consequently, average marker density above one per 22 kb did not improve the accuracy of predictions in the RP. The accuracy of progeny prediction varied greatly depending on the composition of the training set, the trait, LD and minor allele frequency. The highest accuracy achieved for each trait exceeded 0.50 and was only slightly below the accuracy achieved by cross validation in the RP. Our results thus show that relatively high accuracy (0.41–0.54) can be achieved using only a rather small share of the RP, most related to the PP, as the training set. The practical implications of these results for rice breeding programs are discussed.