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Dissertation_Caleb_Orchard_10.10.22.pdf (1.22 MB)

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Pre-breeding to Combine Genes for Resistance and Agronomic Traits in Processing and Fresh-Market Tomato

Orchard, Caleb J
http://rave.ohiolink.edu/etdc/view?acc_num=osu1665395873317211

Abstract Details

2022, Doctor of Philosophy, Ohio State University, Horticulture and Crop Science.
Contemporary processing and fresh-market tomato varieties grown in humid environments must possess a combination of yield, fruit quality, and disease resistance traits to produce marketable fruit amidst the challenging growing conditions present in the field. The focus of this dissertation was to use assays for DNA variation, molecular markers, to enable strategies to efficiently combine resistances to multiple diseases and predict performance for traits across populations and environments. The first objective was to select for coupling-phase recombination events on chromosome 11 of tomato that combine resistances to four tomato pathogens. Resistances to bacterial spot (QTL-11 and Xv3/Rx4) caused by Xanthomonas spp., gray leaf spot (Sm) caused by Stemphylium spp., fusarium wilt (I2) caused by race 2 of Fusarium oxysporum f. sp. lycopersici, and tomato yellow leaf curl virus (Ty-2) caused by begomoviruses are located on chromosome 11. We used molecular markers linked to these resistance loci to select for rare recombination events and created a linked cassette that can be inherited together in future crosses. Notably, we created a novel coupling of Xv3/Rx4 and Ty-2, with recombination between the two genes estimated as 0.056 cM. Progeny from the recombinant plants had resistance levels comparable to resistant controls when tested in inoculated seedling trials, demonstrating that effective combinations of resistance genes can be established using publicly available germplasm. The second objective was to investigate genomic selection (GS) models to predict yield and quality traits within the context of a processing tomato breeding program. Genomic selection is a widely used tool in plant breeding, but applications of GS in vegetable crops have been limited compared to grain crops, primarily due to resource and population size limitations. We developed models for two inbred line populations to predict inbred and hybrid performance. Both individual inbred populations and a combined dataset were used for modeling. Predictions based on different training population sizes and marker numbers were performed for eight yield and quality traits and compared to actual performance in field trials. Models based on inbred lines could predict the performance of other inbred populations as well as hybrids for most traits (P < 0.05). Prediction accuracies ranged from 0.06 to 0.45 for inbred populations and 0.10 to 0.81 for hybrids. Increasing training population size had only a limited effect on hybrid prediction, with little gains in accuracy after 125 individuals. Hybrid prediction accuracies plateaued at relatively small marker numbers (600-900), with minimal decreases in accuracy when using even fewer markers. Together, these results suggest that GS models developed using population sizes manageable by vegetable breeding programs and low-density marker coverage of the genome are sufficient to predict hybrid performance in tomato and can potentially save tomato breeders time in evaluating undesirable hybrid combinations. The third objective was to develop GS models for disease resistance and yield-related traits in a fresh-market tomato breeding program for the tropics and evaluate the accuracy of these models for predicting performance among early generation inbred families and hybrids. Mixed infections from multiple pathogens are common in fresh-market tomatoes grown in the tropics, as are stresses from high temperature and precipitation. Breeding programs exist for specific markets encompassing both the monsoon rainy season and cool season in specific countries. At the same time, breeders also aim to create commercial varieties that perform across multiple environments. GS models were developed for important rainy season traits such as pathogen non-specific resistance to foliar diseases, resistance to the viral pathogen tomato yellow leaf curl virus (TYLCV), and yield-related traits. I compared GS models to phenotypic methods and predicted performance in early generation inbred families and hybrids grown in multiple seasons and environments. Phenotypic correlations for yield traits between rainy season trials were generally higher compared to rainy versus cool season comparisons. Few correlations between trials were significant for foliar disease resistance, likely due to differences between foliar pathogens and symptom severity among environments. GS could predict yield and disease resistance traits during inbreeding and in hybrids (P < 0.05). Limited prediction was observed for foliar disease resistance (r = 0.13-0.32, P = 0.04-0.006). Accuracies for TYLCV prediction were significant and ranged from 0.22-0.52 (P = 0.05- <0.001). GS models for yield traits developed from inbred families could predict performance across environments with moderate to low accuracy (r = 0.11-0.55, P = 0.04- <0.001). Early generation inbred families could predict the performance of progeny in the form of partially inbred families and hybrids, but only among certain training and testing test combinations. In general, the highest prediction accuracies were observed when the training set included data from the same country or season as the prediction set, demonstrating a use of GS within breeding programs focused on specific seasons or markets. This dissertation provides applied outcomes in the form of germplasm with improved resistance as well as valuable information regarding the application of marker-based techniques, including genomic selection, to processing and fresh-market tomato breeding programs.
David Francis (Advisor)
Clay Sneller (Committee Member)
Sally Miller (Committee Member)
Leah McHale (Committee Member)
150 p.
Agriculture; Genetics; Horticulture; Plant Pathology; Plant Sciences
Solanum lycopersicum; tomato breeding; genomic selection; genomic prediction; marker-assisted selection

Recommended Citations

Citations

  • Orchard, C. J. (2022). Pre-breeding to Combine Genes for Resistance and Agronomic Traits in Processing and Fresh-Market Tomato [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1665395873317211

    APA Style (7th edition)

  • Orchard, Caleb. Pre-breeding to Combine Genes for Resistance and Agronomic Traits in Processing and Fresh-Market Tomato. 2022. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1665395873317211.

    MLA Style (8th edition)

  • Orchard, Caleb. "Pre-breeding to Combine Genes for Resistance and Agronomic Traits in Processing and Fresh-Market Tomato." Doctoral dissertation, Ohio State University, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=osu1665395873317211

    Chicago Manual of Style (17th edition)

osu1665395873317211
17
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