Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple
Jazyk angličtina Země Velká Británie, Anglie Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
34893831
PubMed Central
PMC9210277
DOI
10.1093/g3journal/jkab420
PII: 6459174
Knihovny.cz E-zdroje
- Klíčová slova
- Malus domestica, GenPred, Genomic Prediction, Shared Data Resource, genomic selection, germplasm, population combination, training set design,
- MeSH
- fenotyp MeSH
- genom MeSH
- genomika metody MeSH
- genotyp MeSH
- jednonukleotidový polymorfismus MeSH
- Malus * genetika MeSH
- modely genetické MeSH
- selekce (genetika) MeSH
- šlechtění rostlin MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Genomic selection is an attractive strategy for apple breeding that could reduce the length of breeding cycles. A possible limitation to the practical implementation of this approach lies in the creation of a training set large and diverse enough to ensure accurate predictions. In this study, we investigated the potential of combining two available populations, i.e., genetic resources and elite material, in order to obtain a large training set with a high genetic diversity. We compared the predictive ability of genomic predictions within-population, across-population or when combining both populations, and tested a model accounting for population-specific marker effects in this last case. The obtained predictive abilities were moderate to high according to the studied trait and small increases in predictive ability could be obtained for some traits when the two populations were combined into a unique training set. We also investigated the potential of such a training set to predict hybrids resulting from crosses between the two populations, with a focus on the method to design the training set and the best proportion of each population to optimize predictions. The measured predictive abilities were very similar for all the proportions, except for the extreme cases where only one of the two populations was used in the training set, in which case predictive abilities could be lower than when using both populations. Using an optimization algorithm to choose the genotypes in the training set also led to higher predictive abilities than when the genotypes were chosen at random. Our results provide guidelines to initiate breeding programs that use genomic selection when the implementation of the training set is a limitation.
Department of Agricultural Sciences University of Bologna Bologna Italy
Department of Plant Breeding Swedish University of Agricultural Sciences Kristianstad Sweden
Plant Breeding and Biodiversity Centre Wallon de Recherches Agronomiques Gembloux Belgium
Univ Angers INRAE Institut Agro IRHS SFR QuaSaV F 49000 Angers France
Výzkumný a Šlechtitelský ústav Ovocnářský Holovousy s r o Holovousy Czech Republic
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