Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.)
| Autor: | Peer Wilde, Viktor Korzun, Christina Lehermeier, Hans-Peter Piepho, Manfred Schönleben, Andres Gordillo, Hartwig H. Geiger, Hans-Jürgen Auinger, Eva Bauer, Malthe Schmidt, Chris-Carolin Schön |
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| Jazyk: | angličtina |
| Předmět: |
0106 biological sciences
0301 basic medicine Secale Genotype Breeding program Calibration (statistics) Population Best linear unbiased prediction Polymorphism Single Nucleotide 01 natural sciences 03 medical and health sciences Statistics Genetics Plant breeding education Crosses Genetic Selection (genetic algorithm) education.field_of_study Models Genetic biology business.industry Genomics General Medicine biology.organism_classification Pedigree ddc Biotechnology Plant Breeding Phenotype 030104 developmental biology Sample size determination Original Article business Agronomy and Crop Science Genome Plant 010606 plant biology & botany |
| Zdroj: | TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik |
| ISSN: | 0040-5752 |
| DOI: | 10.1007/s00122-016-2756-5 |
| Popis: | Key message Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by common ancestors. Abstract In hybrid rye breeding, application of genome-based prediction is expected to increase selection gain because of long selection cycles in population improvement and development of hybrid components. Essentially two prediction scenarios arise: (1) prediction of the genetic value of lines from the same breeding cycle in which model training is performed and (2) prediction of lines from subsequent cycles. It is the latter from which a reduction in cycle length and consequently the strongest impact on selection gain is expected. We empirically investigated genome-based prediction of grain yield, plant height and thousand kernel weight within and across four selection cycles of a hybrid rye breeding program. Prediction performance was assessed using genomic and pedigree-based best linear unbiased prediction (GBLUP and PBLUP). A total of 1040 S2 lines were genotyped with 16 k SNPs and each year testcrosses of 260 S2 lines were phenotyped in seven or eight locations. The performance gap between GBLUP and PBLUP increased significantly for all traits when model calibration was performed on aggregated data from several cycles. Prediction accuracies obtained from cross-validation were in the order of 0.70 for all traits when data from all cycles (N CS = 832) were used for model training and exceeded within-cycle accuracies in all cases. As long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size, aggregating data from several preceding cycles is recommended for predicting genetic values in subsequent cycles despite decreasing relatedness over time. Electronic supplementary material The online version of this article (doi:10.1007/s00122-016-2756-5) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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