| Cotton is an important cash crop in the world. A long-term challenge facing cotton breeder is the simultaneous improvement of yield and fiber quality to meet the demands of the cotton producer as well as the textile industry. In the recent years, improvement of cotton fiber quality has been extremely important because of changes in spinning technology. However, a negative association between lint yield and fiber quality are still presented after over dozens years of exhausting breeding for improved fiber properties due to the genetic complexity of fiber quality properties. Conventional breeding procedures exist difficulty in further improving fiber quality because of its high costs, long duration, and low selective efficiency. The development of DNA markers linked to the fiber quality QTL would allow cotton breeders to trace this very important trait in early plant-growing stage or early segregating generations. The use of these DNA markers is increasing the prospect for streamlining the cotton breeding programs for improving fiber quality while maintaining fiber yield.However, QTL analysis for fiber properties is problematic:(1) QTLs obtained from interspecies population of Sealand cotton and Upland cotton showed less valuable in improving fiber quality of Upland cotton; (2) In previous reports, population constructed were all F2 population. Replicated experiment couldn't be carried out and common QTLs couldn't be got. Considering of these problems above, the objective of this research is to develop three F2 and F2:3 populations, by using 7TR-133,7TR-132,7TR-214, which derived from a cross between 7235 and TM-1 using a bulk-selfing technique, as female parent, backcrossed with TM-1 to identify fine located of common QTL associated with fiber quality and yield component traits across different background and different generations by PCR to screen SSR markers. Second, in order to provide theory for employing super quality and high yield, replicated experiment of two years and two locations in the present were used to identify stable major QTLs of fiber quality and yield component traits. The present result as following:1. Correlation analysis for fiber quality and yield traitsPositive correlation was observed between fiber length and fiber strength, between fineness and elongation, between bolls per plant and lint percentage, between fiber length and boll size, and negative correlations between strength and fineness, strength and fiber elongation, between bolls per plant and boll size, boll size and lint percentage, between fiber length and bolls per plant and lint percentage, between fiber strength and bolls per plant, fiber strength and boll size, fiber strength and lint percentage, between fiber fineness and bolls per plant, fineness and boll size, between fiber elongation and bolls per plant, elongation and boll size.2. Genetic analysis for fiber quality and yield componentsFiber quality (fiber length, fiber strength, fiber fineness and fiber elongation) and yield components genetic model was controlled by more than one major gene. Additive effect was stably exhibited in F2 and F2:3 generations and different locations (Xinjiang and Jiangpu). In the present research, the herelicity for fiber quality and yield component was different in different generations and locations. It was the reason that the genes for these traits were influenced by themselves and surroundings.3. Construction of genetic linkage mapOf the 6123 SSR markers employed in this study, polymorphic SSR markers were detected in three populations (between 7TR-133 and TM-1,7TR-132 and TM-1,7TR-214 and TM-1). The three genetic maps for (7TR-133×TM-1) F2, (7TR-132×TM-1) F2, and (7TR-214×TM-1) F2 were generated. The SSR genetic map was constructed using 907 individuals in (7TR-133×TM-1)F2 and included 22 loci covering 13.7 cM, which represented approximately 12.3% of the total 111.7 cM recombinational length of cotton Chro.24. The map constructed from 670 individuals in (7TR-132×TM-1) F2 included 11 loci covering 19.1 cM, approximately 17.1% of the recombinational length of cotton Chro. 24, and the map constructed using 940 individuals in (214-RIL×TM-1) F2 included 18 loci covering 10.1 cM, approximately 9.4% of the recombinational length of cotton Chro.24. Sixteen,7 and 10 distorted SSR loci were detected in (7TR-133×TM-1) F2, (7TR-132×TM-1) F2 and (7TR-214×TM-1) F2. The percent skewed segregation ratios were 72.7%, 63.6%, and 55.6%, respectively, a great deal of decrease from 100% in (7235×TM-1)RIL.4. QTL tagging for fiber quality17 QTLs of fiber quality were identified in Pop A (7TR-133×TM-1), Pop B(7TR-132 ×TM-1) and Pop C (7TR-214×TM-1) three populations. Of these QTLs, there were 5 QTLs for fiber length, which exhibited a total phenotypic variance (PV) of 20.1%-32.7%,5 QTLs for fiber strength, which exhibited a total phenotypic variance (PV) of 28.8%-59.6%, 4 QTLs for fiber fineness, which exhibited a total phenotypic variance (PV) of 17.9%-41.5%, and 3 QTLs for fiber elongation, which exhibited a total phenotypic variance (PV) of 18.0%-26.2%. The QTLs for fiber length, fiber strength and fiber fineness were conferred by female parents 7TR-133,7TR-132 and 7TR-214 (increased fiber length and strength, decrease fiber fineness), and the QTL for elongation was conferred by male parent TM-1 (increased fiber elongation).5. QTL tagging for yield traits6 QTLs of yield trait were identified in (7TR-133×TM-1) Pop A, (7TR-132×TM-1) Pop B and (7TR-214×TM-1) Pop C populations. Of these QTLs, there were 1 QTLs for bolls per plant, which exhibited a phenotypic variance (PV) of 5.6%-9.4%,3 QTLs for boll size, which exhibited a total phenotypic variance (PV) of 15.0%-35.5%, and 2 QTLs for lint percentage, which exhibited a total phenotypic variance (PV) of 10.9%-19.3%. The QTLs for bolls per plant and boll size were conferred by male parent TM-1 (increased bolls per plant and boll size), and the QTL for lint percentage was conferred by female parent 7TR-133,7TR-132 and 7TR-214(increased lint percentage).In the present,5 clustered QTL for fiber strength were detected in the three backcross populations among the confidence interval of q-FS-D08 identified by Shen et al. The q-BN-D08 was identified in the three backcross populations and (7235×TM-1) RIL population. It showed that the QTL for bolls per plant was stable and common. There was consistent with the result of genetic analysis and QTL mapping. Fiber quality and yield components were controlled by more than one main gene, and additive effect was mainly. It was difficult to breed and improve fiber quality and yield at same time because there was negative correlation between fiber quality and yield components. |
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