| Xinjiang is one of the largest cotton production bases in China. In recent years, cotton has become the leading industry in autonomic regional economic development and rural areas. Adverse environments, such as dehydration and salinity, restrict cotton yield greatly. Mapping markers of QTL on dehydration resistance and salinity tolerance presents an important theoretical basis and material basis for the future conduct of cotton drought and salt-tolerant molecular marker-assisted breeding. In addition, the study also enforces QTL mapping on the cotton yield and morphological properties under four different ecological environments, the main findings are as follows:1 76 cotton varieties were studied. Selection of salt-tolerant varieties using seed germinating test and seedling growth under salinity indicated that 9812-85,9811-36,Shigan1,Xinzhi3 and Jinong441 are salt-tolerant varieties while Xinzhi1,KD51028 and Lu22 are sensitive. Using seedling growth test to select drought tolerance of cotton variety,the result showed that 1193B and Shandong8 are drought-tolerant varieties and 9811-36,9810-3,21025-2,Zhong45 and 21021-8 are sensitive.2 Using F2 populations of 184 individuals originated from the hybridization between Lm97-8 and Sm12, genetic mapping with a chain of eight groups was constructed, including a total of 25 tags, the average interval between 13.7 cM and full length of 342.7 cM.3 Drought tolerance QTL:The study performed statistical analysis and QTL mapping on cotton yield and plant height of the growth period using stress index and coefficient and composite interval mapping method, respectively, a total contribution rate of two larger sites with contribution rate of 10.87% and 13.73% was detected. Among that, seven sites related to dehydration resistance were determined with the contribution rate ranging from 4.19% to 19.66%, 4 QTLs of plant height in squaring stage, one in flowering stage and 2 in the opening of a boll were positioned. Sites of plant height in different stages dispersed in different recombinant groups, which indicated the same traits controlled by different gene loci and plant height at different growth stages determined by different genes.4 Salt resistance QTL:The composite interval mapping theory was applied to QTL position on the biological yield and plant height of the growth period. Two sites with large contribution rate on biological yield were detected. They were qSI-C2-1 and qSC-C2-1 with contribution rate of 10.73% and 13.08%, respectively. 4 stable sites on plant height, one in seedling and 3 in squaring, were detected with the contribution rate of 5.47% -17.20%.As the four sites in plant height stress coefficient and index can both be detected, they may be the same four sites. According to it, we conclude that the four sites may control salt tolerance in different growth stages.5 Yield QTL mapping: In boll size, a total of four larger contribution rate sites, qBS-C6-1. qBS-C2-1, qBS-C3-2 and qBS-C2-1, respectively were detected, which can explain 18.54%, 13.07%, 17.50% and 13.48% of the phenotypic variability. Referring to SCW, seven QTLs were detected. Three of them, which are located in the 2nd and 5th linkage groups, may explain 18.90%, 13.41% and 16.30% of the phenotypic variability. A total of six detected QTLs were in boll number, including four main sites, explaining 12.51%, 12.62%, 13.82% and 12.92%. In lint percentage, a total of 10 traits were detected, including the main 3 sites, which were able to explain 11.97%, 10.48% and 10.15% phenotypic variation.6 Characters QTL: In flowering stage, a total of three larger contribution rate QTLs, loci qFS-C1-1 (D0) qFS-C2-2 (D1) and qFS-C3-1 (D0), respectively, can explain 17.75%, 16.49% and 17.24% of the phenotypic variability. Using reversing four-leaf area index method, two larger contribution rate QTLs, loci qLAI-C1-1 (S0) and qLAI - C5-1 (S0) with the contribution rate of 10 .48% and 14.87% were detected. Two QTLs, qFHWLR-C1-1 (D1) and qFHWLRC2-1 (S0) with contribution rate of 10.67% and 14.51%, were detected, according to water loss rate in 5 hours. In the meaningwhile, the same number of QTLs was detected in 10-hour water loss rate, that is, qTHWLR-C3-1 and qTHWLR-C2-1 with the corresponding contribution rate of 15.33% and 15.92%.
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