Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction

Sarah A. Kostick , Rex Bernardo , James J. Luby

Horticulture Research ›› 2023, Vol. 10 ›› Issue (6) : 088

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Horticulture Research ›› 2023, Vol. 10 ›› Issue (6) :088 DOI: 10.1093/hr/uhad088
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Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction
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Abstract

Many fruit quality traits in apple (Malus domestica Borkh.) are controlled by multiple small-effect quantitative trait loci (QTLs). Genomewide selection (genomic selection) might be an effective breeding approach for highly quantitative traits in woody perennial crops with long generation times like apple. The goal of this study was to determine if genomewide prediction is an effective breeding approach for fruit quality traits in an apple scion breeding program. Representative apple scion breeding germplasm (nindividuals = 955), high-quality single nucleotide polymorphism (SNP) data (nSNPs = 977), and breeding program fruit quality trait data at harvest were analyzed. Breeding parents ‘Honeycrisp’ and ‘Minneiska’ were highly represented. Moderate to high predictive abilities were observed for most fruit quality traits at harvest. For example, when 25% random subsets of the germplasm set were used as training sets, mean predictive abilities ranged from 0.35 to 0.54 across traits. Trait, training and test sets, family size for within family prediction, and number of SNPs per chromosome affected model predictive ability. Inclusion of large-effect QTLs as fixed effects resulted in higher predictive abilities for some traits (e.g. percent red overcolor). Postdiction (i.e. retrospective) analyses demonstrated the impact of culling threshold on selection decisions. The results of this study demonstrate that genomewide selection is a useful breeding approach for certain fruit quality traits in apple.

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Sarah A. Kostick, Rex Bernardo, James J. Luby. Genomewide selection for fruit quality traits in apple: breeding insights gained from prediction and postdiction. Horticulture Research, 2023, 10 (6) : 088 DOI:10.1093/hr/uhad088

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Acknowledgements

We would like to thank Elizabeth Blissett whose Ph.D. dissertation research provided the impetus for this study. We would also like to thank Baylee Miller, John Tillman, and the undergraduate and graduate research assistants in the University of Minnesota apple breeding program who aided with data collection. This study was funded by the Minnesota State Agricultural Experiment Station- University of Minnesota Projects MIN-21-040 and MIN-21-097 and by the United States Department of Agriculture-National Institute of Food and Agriculture Specialty Crop Initiative Projects 2009-51181-05808 and 2014-51181-22378.

Author Contributions

Conceptualization of study, JJL, RB, and SAK; formal analysis, SAK; writing-original draft preparation, SAK; writing-review and editing, JJL and RB. All authors contributed to the final draft of this manuscript.

Data Availability

The data presented in this study are available in the tables, figures, and supplementary materials.

Conflict of interest statement

The University of Minnesota receives royalty payments related to the “Honeycrisp”, “Minnewashta”, “Wildung”, “Minneiska”, “MN55”, MN80, and “MN33” apple cultivars. JJL and the University of Minnesota have a royalty interest in these cultivars. These relationships have been reviewed and managed by the University of Minnesota in accordance with its conflicts of interest policies.

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