QTL Mapping For Drought Resistance And Salt Tolerance Traits At Germination Stage In RIL Population Of Wheat

Wheat is one of the major food crops in China. Drought and salt injury is two of the most important abiotic stress factors that limit the yield and quality of wheat. Researches on genetic mechanism of drought resistance and salt tolerance are important to the food security and to the sustainable development of China. Using a set of recombinant inbred lines(RILs) derived from the cross “Shannong 0431 × Lumai 21” and “ Tainong 18×Linmai 6 ”, 13 morphological traits related with drought resistance and salt tolerance were analyzed at the gemanation stage under hydroponic cultivation with the osmotic stress treatment induced by PEG-6000 and Na Cl. QTL mapping on the charactersin the germination periodwas analysed.1) Analysis of phenotypic variation and simple correlation. There were transgressive segregations for almost all of the traits in the 3 environments and its average value, and continuous distributions were obtained for all of the traits in the three environments, demonstrating the quantitative nature of polygenic inheritance. Drought resistance traits have high significant positive correlation with salt tolerance traits.2) QTL analysis of drought resistance and salt tolerance related traits. SL population:A total of 208 QTLs for the 13 traits were located on 20 chromosomes(In addition to the 3D) in the 3 environments and MV. A single QTL in an environment explained 4.4 to 27.2% of the phenotypic variation. The highest LOD was 13.73 for LV in the MV. 99 QTLs showed positive additive effects with Shannong 0431 increasing the effects of QTLs, whereas 109 QTLs were negative effects with Lumai 21 increasing the QTL effects. TL population:A total of 263 QTLs for the 13 traits were located on 21 chromosomes in the 3 environments and MV. A single QTL in an environment explained 4.8 to 41.7% of the phenotypic variation. The highest LOD was 7.85 for LV in the MV. 133 QTLs showed positive additive effects with Tainong 18 increasing the effects of QTLs, whereas 130 QTLs were negative effects with Linmai 6 increasing the QTL effects.3) Analysis of high frequency QTLs. SL population:5 traits of 8 QTL, was detected in at least fourof the environment, is high frequency QTLs. Of which, QRl.2-2B and QShl.2-4D explained 13.20 and 13.04%(AV) of the phenotypic variations. It indicate that these should be major QTLs.All the RHF-QTLs, except for QRl.1-2B, showed negative additive effects with Lumai 21 increasing the QTL effects. TL population:2 traits of 2 QTL, detected in at least half of the environment, are high frequency QTLs. Of which, QRdw-2A explained 15.64%(AV) of the phenotypic variations,indicating that these should be major QTLs. QRdw-2A showed negative additive effects. QGf.2-1D showed positive additive effects with Tainong 18 increasing the QTL effects.4) Analysis of QTL clusters. SL population: A number of 17 QTL clusters(C1-C17) with more than three traits were mapped on 10 chromosomes: 1A, 1B, 1D, 2B, 3B, 4B, 4D, 5B, 6A and 7B, which were related to all of the investigated traits and involved 54 QTLs(26%). 23 QTLs showed positive additive effects with Shannong 0431 increasing the effects of QTLs, whereas 31 QTLs were negative effects with Lumai 21 increasing the QTL effects. TL population: A number of 16 QTL clusters(C1-C16) with more than three traits were mapped on 13 chromosomes: 1A, 1B, 1D, 2A, 2B, 3A, 3B, 3D, 4A, 4B, 5B, 7A and 7B, which were related to all of the investigated traits and involved 53 QTLs(20.2%). 30 QTLs showed positive additive effects with Tainong 18 increasing the effects of QTLs, whereas 23 QTLs were negative effects with Linmai 6 increasing the QTL effects.