QTL mapping of melon fruit quality traits using a high-density GBS-based genetic map

[Background]: Melon shows a broad diversity in fruit morphology and quality, which is still underexploited in breeding programs. The knowledge of the genetic basis of fruit quality traits is important for identifying new alleles that may be introduced in elite material by highly efficient molecular breeding tools.[Results]: In order to identify QTLs controlling fruit quality, a recombinant inbred line population was developed using two commercial cultivars as parental lines: “Védrantais”, from the cantalupensis group, and “Piel de Sapo”, from the inodorus group. Both have desirable quality traits for the market, but their fruits differ in traits such as rind and flesh color, sugar content, ripening behavior, size and shape. We used a genotyping-by-sequencing strategy to construct a dense genetic map, which included around five thousand variants distributed in 824 bins. The RIL population was phenotyped for quality and morphology traits, and we mapped 33 stable QTLs involved in sugar and carotenoid content, fruit and seed morphology and major loci controlling external color of immature fruit and mottled rind. The median confidence interval of the QTLs was 942 kb, suggesting that the high density of the genetic map helped in increasing the mapping resolution. Some of these intervals contained less than a hundred annotated genes, and an integrative strategy combining gene expression and resequencing data enabled identification of candidate genes for some of these traits.[Conclusion]: Several QTLs controlling fruit quality traits in melon were identified and delimited to narrow genomic intervals, using a RIL population and a GBS-based genetic map.This work was supported by the Spanish Ministry of Economy and Competitiveness grant AGL2015–64625-C2–1-R, Severo Ochoa Programme for Centres of Excellence in R&D 2016–2010 (SEV-2015-0533) and the CERCA Programme/Generalitat de Catalunya to JGM. LP was supported by a FPI grant from the Spanish Ministry of Economy and Competitiveness. VR was supported by the European Union’s Horizon 2020 research and innovation programme under Marie Skłodowska-Curie grant agreement No 6655919.Peer reviewe