Optimizing genomic selection for breeding heat-tolerant cultivars in tomato.

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Practices

Tomato production in the southern region of the United States faces extreme climate conditions, including heat stress caused by higher temperatures and more frequent heat waves. Heat stress negatively affects plant growth and development and leads to poor fruit set, reduced yields, and diminished fruit quality. Therefore, the development of heat-tolerant tomato cultivars is crucial for the sustainability of tomato production in the tropical and sub-tropical regions. Heat tolerance is a complex trait and the development of heat-tolerant cultivars through traditional breeding methods is time-consuming, labor-intensive, and difficult to achieve. Genomic selection is opening new opportunities for improving complex traits like heat tolerance by considering genome-wide marker data. This proposed work aims to explore genomic selection for its effective implementation in breeding for heat-tolerant tomato cultivars. A panel of 150 fresh-market tomato genotypes will be phenotyped for traits associated with heat tolerance in tomato across two fall seasons in field experiments. Genotyping of individuals will be done using the AgriPlex SNP panel of fresh market tomato. A suitable genomic prediction model, the optimum number of individuals, and markers for implementing genomic selection in breeding for heat tolerance in tomato will be identified. The outcomes from this research will establish genomic selection as a tool to improve breeding for heat-tolerant tomato cultivars, ultimately increasing the efficiency of breeding programs and providing solutions to tomato growers in the southern USA and around the world.

The purpose of this project is to build and optimize a genomic selection model that will be used to support the breeding of heat-tolerant fresh-market tomato cultivars.

Specific objectives to achieve this include:

1. Phenotype a diverse panel of tomato genotypes under open-field heat stress conditions, collecting data on key physiological and reproductive traits associated with heat tolerance.
2. Generate and process genome-wide SNP data for the genotyped individuals, including marker filtering and imputation for genomic prediction.
3. Build and validate genomic prediction models using univariate and multivariate approaches and apply the best-performing models to a breeding population.
4. Identify the optimal training population size and marker density to maximize prediction accuracy and inform efficient implementation of genomic selection for heat stress tolerance.