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Saint Petersburg, Russia

Artemyeva A.M.,Research Institute of Plant Industry VIR | Rudneva E.N.,Research Institute of Plant Industry VIR | Volkova A.I.,Research Institute of Plant Industry VIR | Kocherina N.V.,Research Institute of Plant Industry VIR | And 2 more authors.
Acta Horticulturae | Year: 2013

Quantitative Trait Loci (QTL) analysis has been used to identify genes related to a wide range of morphological and disease resistance traits in Brassica rapa L. Two DH mapping populations resulted from crosses between Yellow sarson YS-143, Pak choi PC175, and Vegetable turnip VT115 accessions (DH38: PC175 × YS143 and DH30: VT115 × YS143) have been used at present work. Both populations were evaluated for 43 morphological and disease resistance traits (flowering time, growthrelated traits, leaf, seed, flower and fruit traits) as well as for resistance to strains of four Xanthomonas campestris pv. campestris Pam. (Dow.) races and two races X. arboricola. Based on obtained results it was assumed that QTL determined phenotypic traits may have an overall effect on plant development and suggest a pattern of genetic integration of morphological traits as well as existence of blocks of evolutionary co-adapted genes. The inheritance of potential broad-spectrum quantitative resistance of B. rapa to X. campestris pv. campestris has been elucidated and we report for the first time in Russia the identification of QTL that control resistance to the most important races of the pathogen in B. rapa. The quantitative, potential broad-spectrum disease resistance studied at present work could be transferred into other economically important B. rapa cultivars or could be introgressed into B. oleracea cultivars through interspecific hybridisation. By identifying molecular markers linked with QTL determining each component of studied morphological and disease resistance traits we have been able to 'dissect' the complex morphology and resistance traits and will be able to provide plant breeders with tools to breed these agronomically important traits using marker-assisted selection. Source

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