bioRxivPlant BiologyPreprint
Postharvest tomato shelf life is genetically distinct from fruit firmness: evidence from two F2 populations
Carlo RosatiiD, Fabien Tirado, Giuseppe ApreaiD, Frédérique Bitton CiD, Marie BraultiD, Renaud Duboscq, Paola FerranteiD, Karine Pellegrino, Catia StamignaiD, Giovanni GiulianoiD, Mathilde CausseiD
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Abstract
Reducing postharvest fruit loss without compromising fruit quality is a major goal in tomato breeding. Fruit shelf life is a complex trait, influenced by postharvest changes in fruit firmness and weight, as well as by both genetic and environmental factors. A QTL mapping experiment was conducted to identify loci associated with fruit shelf life-related traits using two distinct F2 tomato populations. Fruit weight, firmness, shelf life (evaluated as both loss of fruit weight and loss of firmness over time), and colour space components were measured, and QTLs were mapped using a commercial low-density SNP genotyping panel and bulk segregant sequencing analysis experiments. We show that fruit firmness at harvest is only weakly predictive of postharvest firmness loss, indicating that shelf life should be treated as a dynamic trait rather than a static firmness phenotype. Across the two populations, 60 QTLs defining 26 genomic regions were identified, including both population-specific loci and shared regions on chromosomes 9 and 12. Several narrow intervals contained candidate genes related to ethylene signaling, cell-wall remodeling, calcium transport, aquaporin-mediated water balance and stress responses. The identified QTLs and candidate genes are directly relevant to breeding programmes seeking to improve postharvest performance without using major ripening mutants that compromise fruit quality. Key messageComparative QTL mapping and BSA-Seq in two F2 tomato populations show that postharvest shelf life is only partly explained by fruit firmness at harvest and identify candidate loci for firmness loss and weight loss during storage.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- approach: statistical model
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