S01 - Session O3 - Genetic control of tomato fruit quality : from QTL to GWAS and breeding

Authors: Juliette Benejam *, Estelle Bineau, Marie Brault, Jiantao Zhao, Yolande Carrretero, Esther Pelpoir, Karine Pellegrino, Frédérique Bitton, Mathilde Causse

Tomato flavor has changed over the course of domestication and later during breeding although it was not a target for breeders until recently. Consumers then complained about the taste of modern varieties. Although tomato taste is influenced by environmental and post-harvest conditions, the varieties show a large diversity for fruit composition and texture. We will present a review of past and present work intended to improve tomato fruit quality. First, results from sensory analysis allowed identifying the most important traits for consumer preferences. Then, to dissect the genetic control of flavor related traits, several QTL analyses were performed since 20 years and a few major QTL were identified. Since the availability of large numbers of SNP, genome-wide association studies (GWAS) were performed on several panels of tomato lines. In 2019, we performed a meta-analysis of GWAS for 18 traits, combining results on 775 tomato accessions and 2,316,117 SNPs from three GWAS panels. We discovered 305 significant associations for the contents of sugars, acids, amino acids and flavor-related volatiles. We showed for instance that fruit citrate and malate contents have been impacted by selection during domestication and improvement, while sugar content has undergone less stringent selection. Volatile organic compound contents also evolved and some trends for improvement were identified. Results suggested that it may be possible to significantly increase volatiles that positively contribute to consumer preferences while reducing the unpleasant ones, by selection of the relevant allele combinations. More recently we checked the inheritance of volatiles at the hybrid level in order to help the production of F1 hybrids with good quality. We are now assessing how genomic prediction could help selecting better tomato lines.