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2019 ASHS Annual Conference

Accuracy of Genomic Prediction Approaches for Enhancing Resistance to Phytophthora Crown Rot in Strawberry

Wednesday, July 24, 2019
Cohiba 5-11 (Tropicana Las Vegas)
Randi A. Famula, University of California, Davis, Davis, CA
Nicolas Cobo, University of California, Davis, Davis, CA
Dominique D.A. Pincot, University of California, Davis, Davis, CA
Michael A. Hardigan, University of California, Davis, Davis, CA
Glenn S. Cole, University of California, Davis, Davis, CA
Kelly Ivors, California Polytechnic State University, San Luis Obispo, CA
Steven J. Knapp, University of California, Davis, Davis, CA
Phytophthora crown rot, a soil-borne disease caused by the oomycete pathogen Phytophthora cactorum, damages the roots and crowns of strawberry (Fragaria x ananassa) plants. This disease is especially prevalent and problematic in heavy and wet soils and has been a persistent problem in strawberry production. Soil fumigation has previously been widely used to suppress this pathogen. Earlier studies have indicated that the genetics of resistance to this pathogen are complex with heritabilities in the 30-50% range, although one or two large-effect loci have been reported in certain populations. Here, we report the results of a genome-wide study of the genetics of Phytophthora crown rot resistance in octoploid strawberry. We developed a training population comprised of over 400 germplasm accessions including heirloom and modern cultivars that sample demographic and phenotypic diversity worldwide. The training population was genotyped with a 49,000-SNP genotyping array and phenotyped for resistance to Phytophthora crown rot in 2017-18 and 2018-19 replicated field experiments. Plants were artificially inoculated and grown in soils that had been fumigated to eliminate the confounding effects of other soil-borne pathogens. We observed a normal distribution among individuals in the training population, with phenotypes spanning the range from highly resistant to highly susceptible. The broad-sense heritability of resistance was 0.5. We did not observe the segregation of large effect loci in the genome-wide association study. The accuracy of genomic predictions suggests that breeding for resistance to this pathogen can be enhanced by using whole-genome regression and similar approaches.