| China is the largest cotton producer, consumer, importer, and textile exporter in the world. Cotton exportation earns huge foreign exchange and plays an irreplaceable role in the nation economy. Since the last few decades because of the food security concern, cotton planting areas shifted from central provinces to eastern coastal, northwest inland and Inner Mongolia. Cotton belongs to water-consuming of crops. Drought stress suppresses root growth, reproduction and photosynthesis processes of cotton,which leads to yield and quality decline. Thus, to ensure sustainable cotton production and boost productivity in drought prone area, development of drought tolerant cultivars is crucial. It is time consuming and challenging to develop elite cultivars that possess high yielding, good fiber quality and drought tolerant traits through traditional breeding. However, the development and use of molecular markers in cotton breeding programs hold a promise of providing necessary information about the detection of drought tolerance related genes and marker assisted selection. In this study, we developed mapping population from an interspecific cross between G. hirsutum(CRI12) and G. tomentosum(GT).Qualitative trait loci(QTL) for drought tolerance were mapped at seedling bud, and full boll stages.Photosynthetic related QTL were mapped at bud stage.1. QTL mapping for drought tolerance traits at seedling stageThe focus of the present study was to dissect the genetic basis of drought tolerance in 149 F2:3 lines under water-limited(W1) and well-watered(W2) regimes at seedling stage. Forty-one significant QTLs were detected by composite interval mapping. Out of these, 21 were under W1 condition, comprising two QTLs for Width/Height, three for leaf number,two for leaf area,five for plant height, two for chlorophyll content, two for proline content, and five for malonaldehyde(MDA) content. Thirteen QTLs were inherited from GT and eight were inherited from CRI12. Among these, 6 QTLs showed additive effects, explaining 6.93%-16.93% of the phenotypic variation. Whereas the remaining 20 QTLs were observed under W2 condition, including six QTLs for Width/Height, two for plant width,three for leaf area, four for plant height, four for proline content, and one for malonaldehyde(MDA) content.Twelve major QTLs were inherited from CRI12 and eight were inherited from GT. Five clusters were also found. Of which, four were on chromosomes 8, 19, 23, and 24 under W1 condition, while one cluster related to various traits was found on chromosomes 26 under W2 condition. Five QTLs showed additive effects, explaining 6.61%-15.19% of the phenotypic variation. Moreover, 16 QTLs for drought resistant coefficient were detected at seedling stage: five for plant height, one for number of leaves,three for chlorophyll content, three for proline content, and four for malonaldehyde(MDA) content. Ten major QTLs were inherited from GT and six were inherited from CRI12. Five QTLs showed additive effects, explaining 8.60- 25.80% of the phenotypic variation.2. QTL mapping for drought tolerance traits at bud, flowering, and full-boll stagesA QTL analysis was conducted to investigate the genetic basis of drought tolerance in cotton(Gossypium spp.) using 149 F2:3 lines. A field experiment was conducted in two consecutive years(2014and 2015) and 13 morphological and physiological traits were studied under water-limited(W1) and well-watered(W2) regimes at bud, flowering, and full boll stages. In total, 166 QTLs were detected, of which 71 QTLs were detected under the W1, representing eight for chlorophyll content, five for leaf area,ten for number of leaves, and seven for plant height at bud stage; five for chlorophyll content, seven for leaf fresh weight, seven for leaf dry weight, and eight for plant height at the flowering stage; and seven for boll weight, eight for number of fruiting branches, and four for plant height at full-boll stage.Thirty-one major QTLs were inherited from GT and 40 were inherited from CRI12. Thirteen QTLs showed additive effects, explaining 6.07%-13.34% of the phenotypic variation. Thirty-five QTLs were detected under the W2 conditions. Of these, three for plant height, one for leaf number, two for leaf area,and four for chlorophyll content were detected at the bud stage. Four for leaf fresh weight, six for leaf dry weight, and two for chlorophyll content were detected at the flowering stage. The other QTLs, four for boll weight, five for the number of fruiting branches, and four for plant height were detected at the full-boll stage. Twenty QTLs were inherited from GT and 15 were inherited from CRI12. Eight QTLs showed additive effects, explaining 9.20%- 19.88% of the phenotypic variation.Twenty-four QTLs for drought resistant coefficient were detected at the bud stage, comprising eight for chlorophyll content, five for leaf area, four for number of leaves, and seven for plant height.Nine QTLs were inherited from GT and 15 were inherited from CRI12. Two QTLs showed additive effects, explaining 10.93%- 15.80% of the phenotypic variation. Nine QTLs for drought resistant coefficient were detected at the flowering stage, comprising two for chlorophyll content, six for plant height, and one for leaf fresh weight. Six QTLs were inherited from GT and three were inherited from CRI12. Two QTLs showed additive effects, explaining 29.73%- 16.18% of the phenotypic variation.Twenty-seven QTLs for drought resistant coefficient were detected at the full-boll stage, comprising five for boll weight, five for number of fruiting branches, 11 for stem diameter, five for first fruit node,and one for plant height. Ten QTLs were inherited from GT and 17 were inherited from CRI12. Four QTLs showed additive effects, explaining 8.41%- 11.97% of the phenotypic variation.Among all the detected QTLs under drought stress condition in 2014 and 2015, q BLA-Chr5-1 for leaf area at the bud stage, explaining 9.00%- 13.30% of the phenotypic variation; q BCC-Chr9-1 and q FCC-Chr8-1 for chlorophyll content at the bud stage and at the flowering stage respectively,explaining 4.10%- 16.00% of the phenotypic variation; q FBBW-Chr16-1 for boll weight at the full-boll stage, explaining 6.40%- 7.00% of the phenotypic variation; q FBSD-Chr21-1 and q FBSD-Chr21-2 for stem diameter at full-boll stage, explaining 0.80%- 2.30% of the phenotypic variation were found to be stable QTLs. Eleven QTL clusters were detected on chromosomes 2, 5, 6, 14, 16 and 21, and four were on chromosome 16.3. QTL mapping for photosynthetic traits at flowering stage.Photosynthetic traits of the parents and F2:3 were evaluated at blooming stage under drought environment(W1) and well-watered(W2) in 2014 and 2015. In total 45 QTLs were identified for photosynthetic traits, of which, 27 were detected under W1, comprising six for photosynthetic rate(Pn), two for transpiration rate(Tr),five for stomatal conductance(GS), five for leaf temperature, and seven for water use efficiency(WUE). Four QTLs showed additive effects with average LOD between3.32-4.96, and explaining 11.00%- 13.76% of the phenotypic variation. Whereas, under well-watered condition, 18 photosynthetic trait QTLs were identified, comprising one for net photosynthetic rate(Pn),three for intercellular CO2 concentration(Ci), one for stomatal conductance, four for leaf temperature,and nine for water use efficiency(WUE). Four QTLs showed additive effects with average LOD between 3.32- 4.96, explaining 11.00%- 13.76% of the phenotypic variation. Among all the detected QTLs, q Pn-Chr16-1 mapped on chromosome 16; explaining 9.44%- 18.61% of the phenotypic variation was found to be a stable QTL under drought stress environment in 2014 and 2015. Whereas,q Gs-Chr5-1 mapped on chromosome 5, explaining 0.66%- 0.67% of the phenotypic variation was found to be common between W1 and W2 environmental conditions in 2015. |
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