Genomic prediction enables selection for improved fruit ripening traits in northern highbush blueberry ( Vaccinium corymbosum L.) breeding populations
6 affiliations
- 1United States Department of Agriculture—Agricultural Research Service, Horticultural Crops Production and Genetic Improvement Research Unit , 3420 NW Orchard Ave., Corvallis, OR 97330 ,
- 2Savanna Institute Research Department, , 2453 Atwood Ave. Suite 209, Madison, WI 53704 ,
- 3Department of Horticulture, Oregon State University , 2750 SW Campus Way, Corvallis, OR 97331 ,
- 4United States Department of Agriculture—Agricultural Research Service, National Clonal Germplasm Repository , 33447 Peoria Rd., Corvallis, OR 97333 ,
- 5Breeding Insight, University of Florida, Institute of Food and Agricultural Sciences , PO Box 110695, Gainesville, FL 32611 ,
- 6Department of Plant Sciences and Plant Pathology, Montana State University, Plant BioScience Building , Bozeman, MT 59715 ,
Abstract
Blueberry (Vaccinium corymbosum L.) breeding is a time-consuming, complex, and labor-intensive process due to the long-lived woody perennial nature of blueberries and their autotetraploid (2n = 4x = 48) genome. Blueberry breeders have recently adopted and implemented molecular tools for enhancing breeding efficiency and accuracy. Genomic prediction (GP) utilizing genome-wide molecular marker information to predict breeding values of untested genotypes could help accelerate development of adapted varieties. GP and its implementation through genomic selection is an attractive accompaniment to traditional breeding for its potential to improve selection efficiency, particularly for complex polygenic traits. In this study, we utilized a low-cost ($15/sample) mid-density genetic marker platform developed for blueberries by the Breeding Insight initiative to test the first application of genomic prediction in a breeding population of Northern Highbush Blueberries in the Pacific Northwest (PNW), United States. We predicted breeding values for 18 traits related to fruit quality, timing of flowering, and fruit ripening time using 1,822 markers and phenotype data from 4,320 seedlings. Within-population cross-validation indicated moderate to moderately high prediction accuracy (0.6 to 0.8). In contrast, genome-wide association study (GWAS) identified significant markers with small effect (0.50% < phenotype variance explained (PVE) < 2.5%) for 14 traits, while genomic prediction regularly exceeded 0.60 for most traits in most years. Low GWAS signal and strong genomic prediction performance for several traits are consistent with predominantly polygenic trait architecture in this breeding population, where prediction benefits more from genome-wide relatedness and cumulative small effects than from a small number of detectable major loci. This study demonstrates that related individuals can be predicted when phenotypes from the same environment or year structure help estimate marker effects. These results support the potential for genomic prediction to assist early-stage selection in the US Department of Agriculture—Agricultural Research Service PNW blueberry breeding program, with possible reductions in time and phenotyping burden pending future validation in independent cohorts and breeding cycles.
Related
- Genome-wide identification of the BES1 gene family and functional study of VcBES1.3 under salt stress in blueberry (Vaccinium corymbosum)
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- A primordium-level ice barrier in Vaccinium corymbosum floral buds is lost as their xylem trace matures
- The chromosome level genome of the Blueberry Stem Gall Wasp, Hemadas nubilipennis (Hymenoptera: Ormyridae) on lowbush blueberry (Vaccinium angustifolium) reveals repeat-driven expansion
- Mean fitness is maximized in small populations under stabilizing selection on highly polygenic traits
BibTeX
@article{hislop2026genomic,
title = {{Genomic prediction enables selection for improved fruit ripening traits in northern highbush blueberry ( Vaccinium corymbosum L.) breeding populations}},
author = {Lillian M Hislop and Todd Anderson and Kathleen Rhoades and Nahla Bassil and Dongyan Zhao and Craig T Beil and Moira J Sheehan and Claire H Luby and Michael A Hardigan and Jenyne Loarca},
journal = {G3: Genes, Genomes, Genetics},
year = {2026},
month = oct,
doi = {10.1093/g3journal/jkag271},
url = {https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkag271/9004044},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Genomic prediction enables selection for improved fruit ripening traits in northern highbush blueberry ( Vaccinium corymbosum L.) breeding populations
AU - Lillian M Hislop
AU - Todd Anderson
AU - Kathleen Rhoades
AU - Nahla Bassil
AU - Dongyan Zhao
AU - Craig T Beil
AU - Moira J Sheehan
AU - Claire H Luby
AU - Michael A Hardigan
AU - Jenyne Loarca
PY - 2026
DA - 2026/10/28
JO - G3: Genes, Genomes, Genetics
DO - 10.1093/g3journal/jkag271
UR - https://academic.oup.com/g3journal/advance-article/doi/10.1093/g3journal/jkag271/9004044
AB - Blueberry (Vaccinium corymbosum L.) breeding is a time-consuming, complex, and labor-intensive process due to the long-lived woody perennial nature of blueberries and their autotetraploid (2n = 4x = 48) genome. Blueberry breeders have recently adopted and implemented molecular tools for enhancing breeding efficiency and accuracy. Genomic prediction (GP) utilizing genome-wide molecular marker information to predict breeding values of untested genotypes could help accelerate development of adapted varieties. GP and its implementation through genomic selection is an attractive accompaniment to traditional breeding for its potential to improve selection efficiency, particularly for complex polygenic traits. In this study, we utilized a low-cost ($15/sample) mid-density genetic marker platform developed for blueberries by the Breeding Insight initiative to test the first application of genomic prediction in a breeding population of Northern Highbush Blueberries in the Pacific Northwest (PNW), United States. We predicted breeding values for 18 traits related to fruit quality, timing of flowering, and fruit ripening time using 1,822 markers and phenotype data from 4,320 seedlings. Within-population cross-validation indicated moderate to moderately high prediction accuracy (0.6 to 0.8). In contrast, genome-wide association study (GWAS) identified significant markers with small effect (0.50% < phenotype variance explained (PVE) < 2.5%) for 14 traits, while genomic prediction regularly exceeded 0.60 for most traits in most years. Low GWAS signal and strong genomic prediction performance for several traits are consistent with predominantly polygenic trait architecture in this breeding population, where prediction benefits more from genome-wide relatedness and cumulative small effects than from a small number of detectable major loci. This study demonstrates that related individuals can be predicted when phenotypes from the same environment or year structure help estimate marker effects. These results support the potential for genomic prediction to assist early-stage selection in the US Department of Agriculture—Agricultural Research Service PNW blueberry breeding program, with possible reductions in time and phenotyping burden pending future validation in independent cohorts and breeding cycles.
SN - 2160-1836
ER -