Combining genetic resources and elite material populations to improve the accuracy of genomic prediction in apple. Issue 3 (10th December 2021)

• Record Type: Journal Article
• Authors: Cazenave, Xabi; Petit, Bernard; Lateur, Marc; Nybom, Hilde; Sedlak, Jiri; Tartarini, Stefano; Laurens, François; Durel, Charles-Eric; Muranty, Hélène
• Editors: Brown, P
• Is Part Of: G3. Volume 12:Issue 3(2022)
• Publication Date: 2021-12-10
• Subjects: genomic selection -- training set design -- population combination -- germplasm -- Malus domestica -- Genomic Prediction -- GenPred -- Shared Data Resource; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab420
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: 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.