Genomic analysis applied to tomato improvement: from genetic architecture to genomic selection

• Ano de defesa: 2023
• Autor(a) principal: Vidal, Roberta Luiza
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Crescimento da copa; Genética do sistema radicular; Genomic prediction; Melhoramento de porta enxertos; Predição genômica; Processing tomato yield; Produção de tomate para processamento; Root system genetics; Rootstock breeding; Scion growth
• Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-04012024-115324/

Molecular technologies that can greatly assist genetic studies are currently available for tomato crop (Solanum lycopersicum). This allowed the application of modern analysis to study complex traits, especially within a breeding population context. Phenotypic and genotypic variance found in this scenario could be readily explored by breeders. Thereby, identifying genomic regions and markers associated with important traits could greatly assist tomato breeding. Here different genomic analysis were applied to fresh and processing tomato populations aiming to study from the genetic control of relevant traits to the feasibility of prediction models. The goal of the first chapter of this thesis was to provide base knowledge to rootstock breeding programs. A genome-wide association study was performed on a diversity panel to uncover the genetic control of rootstock performance and root system features. Quantitative traits nucleotides associated with most traits evaluated were identified, as well as genotypes with the potential to be used as rootstock parents. The second chapter was developed at The Ohio State University and used different processing tomato populations to report a yield- related quantitative traits locus (QTL), validate it and incorporate it into genomic prediction models. A yield-related QTL on chromosome five was identified and validated, and adding linkage and gene effect information about it improved prediction accuracies.