QTL mapping of fruit nutritional and flavor components in tomato (Solanum lycopersicum) using genome-wide SSR markers and recombinant inbred lines ... View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-11

AUTHORS

Junji Kimbara, Akio Ohyama, Hiroshi Chikano, Hirotaka Ito, Katsutoshi Hosoi, Satomi Negoro, Koji Miyatake, Hirotaka Yamaguchi, Tsukasa Nunome, Hiroyuki Fukuoka, Takeshi Hayashi

ABSTRACT

Fruit nutritional and flavor components are important targets for breeding new cultivars of tomato (Solanum lycopersicum L.). We developed 108 recombinant inbred lines (the K39 RILs) in the F6 generation from a cross between two phenotypically different breeding lines, K03 and K09. A linkage map was constructed using 172 genome-wide simple sequence repeat markers, 3 single-nucleotide polymorphism markers, and 2 phenotypic markers. The K39 RIL map consists of 12 linkage groups (LGs) and covers a genetic distance of 1089 cM. We measured the fruit soluble solids content (SSC), titratable acidity (TA), glutamic acid content (GLU), and lycopene content (LYC) of each line in four generations (F6, F8, F10, F11), β-carotene content (CAR) in two generations, and pH in one generation. By composite interval mapping that considered yearly variations in components as non-genetic effects, we detected three quantitative trait loci (QTLs) for SSC, four for TA, two for CAR, and one each for GLU, LYC, and pH. Among them, we found two QTLs for TA in LGs 6 and 11, those for GLU and LYC were candidates for novel QTLs. QTLs detected in this study were clustered in five LGs, but we observed no apparent trade-off relationships among the QTLs in each LG. Being derived from an intra-specific cross of tomato breeding materials, these QTLs can be used in practical breeding for improving fruit quality with low risk of linkage drag. More... »

PAGES

210

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10681-018-2295-z

DOI

http://dx.doi.org/10.1007/s10681-018-2295-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107867962


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