| Cotton(Gossypium) is an important economic and strategic crop, which provides the most natural textile fiber throughout the world. Among the four cultivated species of cotton, upland cotton(G. hirsutum) is widely cultivated in the world because of the high yield and high fiber quality. Upland cotton is an allotetraploid, and the genome of which is very complex, these reasons resulted in that it is difficult to improve the cotton yield and fiber quality using conventional cultivar breeding programs. With the widespread application of the molecular marker technology and high-throughout sequencing, which will be used as a more effective mean to improve the yield and fiber quality of cotton.In this research, we used molecular marker technology and high-throughout sequencing to provide a foundation for the research of cotton genome and the improvement of cotton varieties, and the main work on the following three aspects:(1) Enrichment of an intraspecific F2 genetic map of upland cotton by developing markers using parental RAD sequencing;(2) construction of genetic map and QTL mapping for fiber quality and yield component traits in upland cotton. 1. Enrichment of an intraspecific F2 genetic map of upland cotton by developing markers using parental RAD sequencingRAD sequencing was performed using DH962 and Jimian5 as upland cotton mapping parents. A total of 62.46 million and 61.27 million raw reads were produced from the DH962 and Jimian5 RAD libraries, respectively. After quality filtering, 5.15 and 5.18 Gb of clean reads were obtained from DH962 and Jimian5 libraries, the GC contents were 34.00 and 34.17%, respectively. Initial de novo assembly produced ≈55.27 Mb of DH962 genome sequence distributed over 178157 individual contigs, the average length of contigs was 310 bp. The clean reads of Jimian5 were assembled into a ≈57.06 Mb genome sequence distributed over 181422 individual contigs, the average length of contigs was 314 bp. Analysing genome sequences of the two parents, 1323 SSR, 3838 insertions/deletion(In Del), and 9366 single-nucleotide polymorphism(SNP) primer pairs were developed. The frequencies of SSRs, In Dels, and SNPs were 1/10.58 kb, 1/18.46 kb, 1/6.55 kb, respectively, and the transition/transversion ratio of the SNPs was 1.76. A total of 14433 contigs to BLAST with the sequences of the A2 and D5 genomes, and 14103(97.71%) contigs were matched. Of the 14103 contigs, 6995 were matched on the 13 chromosomes of the A2 genome, and 7108 were matched on the 13 chromosomes of the D5 genome. The hits were found in an essentially uniform distribution on every chromosome. Polymorphism analysis of markers showed that In Dels and SNPs were better markers than SSRs for intraspecific populations; however, SSRs were better markers than In Dels and SNPs for interspecific populations. Results demonstrated that RAD-seq is an effective and economic method for the development of molecular markers in upland cotton.DH962 and Jimian5 served as the parents of an F2 mapping population included 137 individuals. A total of 1869 markers selected from an interspecific BC1 genetic map of our lab, were screening for polymorphism between DH962 and Jimian5, and 187 showed polymorphisms, revealing 192 polymorphic loci. Adding together the 165 polymorphic loci obtained from RAD-seq studies and other 684 polymorphic loci of the previous studies in our lab, a total of 1041 loci were used for linkage analysis. Finally, 1013 loci were mapped on 50 linkage groups with 41 linkage groups assigned to 23 chromosomes, the total length of the linkage map was 3004.71 c M, with a mean distance of 2.97 c M between adjacent markers. The results of QTL mapping for fiber and yield traits revealed that the efficiency of QTL detection was greatly improved by the increase in map density.The sequences of 562 markers(the sequences of SRAPs were not available) mapped on chromosomes of the upland cotton genetic map in this study were used to BLASTN with the diploid A2 genome of G. arboretum and the D5 genome of G. raimondii. After analysis, the homology and collinearity between the AT genome and the A2 genome were high, except for Chr2 and Ga2, Chr5 and Ga10, Chr10 and Ga9. In the current study, 4 unassembled scaffolds of the A2 genome were anchored by our map. HAU-DJ-S078 matched on scaffold7300, NAU2687 on scaffold3678, HAU-DJ-S168 on scaffold1365, and NBRI_HQ527767 on scaffold4507. Results showed that the homology and collinearity between the DT genome and the D5 genome were high on every chromosome. These information provided an important reference for the sequence assembly of upland cotton and helped to better understand the origin and evolution of polyploidization and genomic integration studies in cotton. 2. Construction of genetic map and QTL mapping for fiber quality and yield component traits in upland cottonA population of 178 recombinant inbred lines(RILs) was developed using a single seed descendant from a cross between G. hirsutum. acc DH962 and G. hirsutum. cv Jimian5 to construct a genetic map of the QTL for fiber and yield traits. A total of 644 polymorphic loci selected from an intraspecific F2 genetic map were used to construct a final genetic map, containing 616 loci, 59 linkage groups and spanning 2016.44 c M, with an average of 3.27 c M between adjacent markers. The map included 538 SSR loci, 32 In Del loci and 46 SNP loci. Fifty-three linkage groups were assigned to 24 chromosomes and 4 linkage groups were unanchored, and most of the loci from two larger linkage groups(LG1-Chr1/15 and LG3-Chr9/23) were mapped on two pairs of homologous chromosomes(Chr1 and Chr15, Chr9 and Chr23, respectively). Among the 59 linkage groups, 2-58 loci were observed per 1.88-104.57 c M in each linkage group. All the 644 loci were tested for the expected Mendelian 1:1 segregation ratio, and 144(22.36%) loci showed a distorted segregation ratio(P < 0.05). Statistical analysis revealed that segregation distortion in the intraspecific population was more serious than that in the interspecific population.The RIL population and the two parents of plants from eight environments(two locations, six years) were phenotyped. Overall, the trait values of DH962 were higher than those of Jimian5 in fiber qualities, and Jimian5 demonstrated higher trait values than DH962 in yield components, except no significant difference was observed for SI and LI. The RIL population performed transgressive segregation on all traits, and each trait in the RIL population showed a normal distribution, the results revealed that this population is suitable for QTL mapping. The results of ANOVA revealed that most of fiber quality and yield component traits presented significant genetic and environmental effects(P < 0.01), except that SI showed no significantly environmental effect. The results of broad-sense heritabilities of the fiber quality and yield component traits showed that the Genetic stabilities of these traits were low. Genetic correlation analysis between fiber quality and yield component traits were was calculated based on covariance. FL was significantly and positively correlated with FS, FU, and significantly and negatively correlated with MIC, FE, LW, LP and BN. FS was significantly and positively correlated with FU and SI, and significantly and negatively correlated with MIC, FE, LW, LP, BN and LI. Among the yield component traits, most traits were positively correlated between two traits, except that SI was significantly and negatively correlated with LP, SCW was negatively correlated with BN, SI was negatively correlated with LI.Based on composite interval mapping(CIM), a total of 134 QTL were detected on 21 chromosomes and 4 linkage groups, explaining 4.40-15.28% of the PV, with LOD scores ranging from 2.50 to 6.66. In seven environments, 64 QTL for five fiber quality traits and 70 QTL for six yield components were identified. And 10, 18, 13, 16, 7, 19, 20, 13, 14, 2 and 2 QTL were related to FL, FS, MIC, FE, FU, SCW, LW, LP, BN, SI and LI, respectively. Among these QTL, 9 common QTL were detected in more than one environment. Among these 9 QTL, 5 QTL were associated with fiber qualities, and 4 QTL were associated with yield components. In addition, 22 of the 134 QTL and 19 new QTL were identified through combined analysis(E9). A total of 26 QTL hotspot regions were observed on 13 chromosomes and 2 larger linkage groups. And several QTL clusters were distributed on 15 chromosomes and 2 linkage groups. The detection of these QTL lays a solid foundation for the QTL fine mapping and map-base cloning in the future. |
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