4.5 Article

Identification of quantitative trait loci across interspecific F2, F2:3 and testcross populations for agronomic and fiber traits in tetraploid cotton

Journal

EUPHYTICA
Volume 191, Issue 3, Pages 375-389

Publisher

SPRINGER
DOI: 10.1007/s10681-013-0875-5

Keywords

Tetraploid cotton; Linkage map; Testcross; Quantitative trait loci (QTL)

Funding

  1. National Basic Research Program of China (973 Program) [2010CB126006]
  2. National High Technology Research and Development Program of China (863 Program) [2012AA101108]
  3. New Mexico Agricultural Experiment Station, New Mexico, USA
  4. USDA-ARS (M.A.G.)

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The most widely grown tetraploid Gossypium hirsutum and G. barbadense differ greatly in yield potential and fiber quality and numerous quantitative trait loci (QTLs) have been reported. However, correspondence of QTLs between experiments and populations is poor due to limited number of markers, small population size and inaccurate phenotyping. The purpose of the present study was to map QTLs for yield, yield components and fiber quality traits using testcross progenies between a large interspecific F-2 population and a commercial cotton cultivar as the tester. The results were compared to these from its F-2 and F-2:3 progenies. Of the 177 QTLs identified from the three populations, 65 fiber QTLs and 51 yield QTLs were unique with an average of 8-12 QTLs per traits. All the 26 chromosomes carried QTLs, but differed in the number of QTLs and the number of QTLs between fiber and yield QTLs. The congruence of QTLs identified across populations was higher (20-60 %) for traits with higher heritabilities including fiber quality, seed index and lint percentage, but lower (10-25 %) for lower heritability traits-seedcotton and lint yields. Major QTLs, QTL clusters for the same traits and QTL 'hotspots' for different traits were also identified. This research represents the first report using a testcross population in QTL mapping in interspecific cotton crosses and provides useful information for further comparative analysis and marker-assisted selection.

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