| In the present study, we report a new genetic linkage map developed from an F1-derived doubled haploid (DH) population of 168 lines, which was generated from the cross between two elite Chinese common wheat varieties, Huapei 3 and Yumai 57. QTL analyses were performed using the software of QTLNetwork 2.0 based on the mixed linear model approach. QTLs for 16 traits, including net photosynthetic rate (Pn), transpiration rate(E), stomatal conductance (Gs), intercellular CO2 concentration (Ci), and the gas conductance (Ci/Cr), chlorophyll a content (Chla), chlorophyll b content (Chlb), the ratio of chlorophyll content a and b (Chla/b), carotinoids content (Cx), initial fluorescence(Fo), maximum yield of fluorescence in darkness(Fm), variable fluorescence(Fv), maximal photochemical efficiency of PSII(Fv/Fm), dry matter accumulation(DMA) of culm, DMA of leaves, DMA of plant, were analyzed. The main results were as the following:1. A population of 168 DH lines was produced by hybridization with maize pollen grains of wheat F1 hybrid plants from the cross between two Chinese common wheat varieties, Huapei3 (female parent)×Yumai57 (male parent), and used for the construction of the genetic map and QTL mapping. The population map we used for QTL analysis contained 324 markers and covered a total length of 2485.7 cM with an average distance of 7.67 cM between adjacent markers in the map, which resulted in 23 linkage groups. Of these, most markers revealed polymorphism between the parents of the mapping population. The result showed that the mapping population was suitable for map construction. We found some large bins of co-segregating markers near the centromeric regions, which are known to have suppressed recombination. These unresolved regions may provide access to a high density of markers and thereby increase the chance of finding polymorphism among different germplasm materials.2. Based on the genetic map established from the DH population, the QTLs were detected using the software QTLNetwork version 2.0 with the composite interval mapping of the mixture linear model. QTLs for 16 traits including photosynthesis related traits and dry matter accumulation were analyzed. A total of 49 additive QTLs and 37 pairs of epistatic effects were detected and distributed on 21 chromosomes. Among them, 9 additive QTLs were major genes, while 40 additive QTLs were minor genes.3. QTLs for chlorophyll content and chlorophyll fluorescence in field: five additive QTLs and one pair of epistatic QTLs were detected for chlorophyll content. Each QTL could explain ranging from 4.34%-28.49% of the phenotypic variance. Nine additive QTLs and four piars of epistatic QTLs were detected for chlorophyll fluorescence, and each QTL could explain ranging from 0.16-11.08% of the phenotypic variance. Five major additive QTLs were identified: qChla5D(16.12%) , qChlb2D(11.59%) ,qChlb5D(28.49%),qFo2A(11.08%) and qFo1B/qFo7B(12.1%). Four QTLs were detected in same molecular marker regions, where two major QTLs for chlorophyll content were included. Most of the QTLs for chlorophyll content and chlorophyll fluorescence were minor QTLs distributed on 13 chromosomes.4. QTLs for dry matter accumulation and Fv/Fm in field: fifteen additive QTLs and twelve pairs of epistatic QTLs were detected for dry matter accumulation of culm, leaves and plant. Each QTL could explain ranging from 0.04%-14.02% of the phenotypic variance. A major additive QTL- Qculm5D-10(14.02%) and one pair of epistatic QTLs-Qleaves4A-10/Qleaves6B-7(13.11%) explained 14.02% and 13.11% of phenotypic variance, respectively. There were three QTLs were identified for Fv/Fm, which explained 3.85%~9.38% of the phenotypic variance. Three QTLs were detected in same molecular marker regions, where one major QTL for DMA of culm were identified. Most of the QTLs for DMA were minor QTLs distributed on 16 chromosomes.5. QTLs for photosynthesis related traits in seedling: seventeen additive QTLs and twenty pairs of epistatic QTLs were detected for photosynthesis related traits. Each QTL could explain ranging from 0.28%~27.25% of the phenotypic variance. All QTLs have effect with QTLs and environments. Three major additive, QTLs-QCa5D-10(18.23%),QCb5D-10(11.4%) and QCx5D-10(27.25%), were detected. Some minor QTLs explain bigger phenotypic variance in effect with QTLs and environments than themselves alone. QTLs for Pn, Gs, Ci, Ci/Cr, Chla, Chlb and Cx were all identified in the same molecular marker region Xcfd101-Xbarc345, indicating that these traits had relationship on heredity and molecular levels. QTLs for some identified traits were detected on 5B. Some QTLs were detected in not only one environment. |
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