Location: Horticultural Crops Production and Genetic Improvement Research Unit
Project Number: 2072-21000-060-000-D
Project Type: In-House Appropriated
Start Date: Feb 13, 2023
End Date: Feb 12, 2028
Objective:
Objective 1: Characterize important genetic traits in blueberry, blackberry, and raspberry to improve selection efficiency and identify novel sources of genetic resistance to disease.
Sub-objective 1.A: Screen germplasm for useful horticultural traits, climate adaptation, and disease resistance and incorporate into breeding populations.
Sub-objective 1.B: Develop and implement molecular breeding tools for genetic mapping, DNA fingerprinting, genome-wide association studies, and assessment of population genetic diversity.
Objective 2: Breed improved blueberry, blackberry, and raspberry cultivars for the commercial small fruit industry including high yielding, virus tolerant, high-quality fruits for the fresh and processing markets.
Approach:
Objective 1: Blueberry Shock Virus & Luteovirus – We will test 139 advanced breeding lines from our breeding program, 19 check cultivars at OSU-NWREC & 301 Vaccinium accessions at the NCGR in Corvallis, OR. We will perform phenotypic evaluation of shock by counting the number of canes per blueberry plant showing the characteristic symptoms of defoliation, flower necrosis, & cane die-back.
Blueberry Genomic Prediction using the Breeding Insight Platform – We will screen PNW highbush blueberry populations housed in the USDA-ARS breeding program for fruit quality related traits including size, firmness, sugars, acidity, & seasonality-related traits including bloom & ripening dates. We will use genetic marker platforms developed by the Breeding Insight project to test accuracy of genomic prediction of quality & seasonality traits.
Red Raspberry Resistance to Raspberry Bushy Dwarf Virus – We will assemble a diversity panel of 50 historic wild & cultivated red raspberry accessions at NCGR to screen for RBDV resistance. The accessions used for RBDV screening will support a bulk-segregant analysis approach for physical mapping based on SNP frequencies in resistant & susceptible bulk pools. The NCGR accessions will be sequenced to 30x coverage on an Illumina HiSeq & sequences will be used to perform alignments & variant calling against the R. occidentalis V3 genome assembly. The bi-parental mapping population used for RBDV screening will be used for genetic mapping to validate the locus identified by bulk-segregant analysis.
Genomic Prediction of Red Raspberry Resistance to Root Lesion Nematode (RLN) – We will clonally replicate a population of 275 raspberry genotypes from various breeding programs. Nematode resistance will be measured as the difference between sample means for biomass accumulation & RLN density in non-inoculated & inoculated replicates. We will implement GWAS & genomic prediction using phenotype data from the RLN experiment.
Black Raspberry Resistance to Large Raspberry Aphid – We will generate black raspberry mapping populations consisting of full-sib families segregating for sources of A. agathonica resistance. We will physically map the sources of aphid resistance & develop source-specific DNA markers.
Linkage Mapping of Hexaploid Blackberry – We will attempt genetic mapping of a hexaploid blackberry using a ‘Columbia Star’ x ‘Black Pearl’ F1 population segregating for the ‘Lincoln Logan’ source of thornlessness.
Objective 2: We will use conventional & marker-assisted breeding strategies to increase beneficial alleles & reduce the frequency of wild or deleterious alleles in the various outcrossing, clonally propagated small fruit crops. New hybrid families are tested annually & stepwise evaluations will be used to cull thousands of seedlings down to a handful. We employ a form of phenotypic recurrent selection in which selections from one generation serve as the parents for the next generation & elite individuals with valuable traits demonstrated combining ability may be used again in subsequent years. We will build on conventional breeding approaches by implementing marker-assisted selection & genomic prediction.
