Type of Document Dissertation Author Badigannavar, Ashok Author's Email Address firstname.lastname@example.org, email@example.com URN etd-01042010-165217 Title Characterization of Quantitative Traits Using Association Genetics in Tetraploid and Genetic Linkage Mapping in Diploid Cotton (Gossypium spp.) Degree Doctor of Philosophy (Ph.D.) Department Agronomy & Environmental Management Advisory Committee
Advisor Name Title Myers, Gerald Committee Chair Kimbeng, Collins Committee Member Knott, Carrie Committee Member Rajasekaran, K Committee Member Huang, Fangneng Dean's Representative Keywords
- Genetic linkage map
- Molecular diversity
- Association mapping
Date of Defense 2009-12-08 Availability unrestricted AbstractCotton (Gossypium spp.) is the most extensively used natural fiber in the textile industry. Understanding the genetic diversity, population structure and marker trait associations are of great importance in marker assisted selection.
Microsatellite, AFLP and TRAP markers were used to construct a linkage map with 94 F2 diploid individuals derived from a cross between G. arboreum x G. herbaceum. A total of 606 polymorphic markers gave rise to 37 linkage groups covering a total of 1109cM with an average distance of 7.92cM between each loci. Discriminant analysis identified three markers each for petal color and seed fuzziness, and four markers for petal spot. For quantitative traits, a total of 19 QTL’s were identified and linked with five fiber traits using composite interval mapping. Markers such as qFL4-1, qFS4-2, qELO1-1 and qSI2-1 were found to be significantly linked with fiber length, strength, elongation and seed index respectively.
Association mapping principles were applied to upland cotton genotypes in order to examine population structure and marker trait associations. A set of 232 genotypes were genotyped using AFLP markers. The molecular diversity was in the range of 0.48-0.574 with molecular variance found to be 10% among the groups. Bayesian and MCMC based population structure analysis, there existed six subpopulations, in accordance with their geographical origin. The mixed and mixed-multiple regression (MMR) models identified significant markers for lint yield and fiber traits, showing low AICC, BIC and SBC values and high adj. R2. Two way epistatic interaction analyses further confirmed their strong association.
In the similar study, a set of 75 upland cotton genotypes were analyzed for seed quality traits such as seed protein, oil and fiber content. Population structure based mixed models showed 32 significant markers, associated with these seed quality traits. MMR models identified several markers, notably E4M3_440, E4M3_200 and E5M7_195 for seed protein, oil and fiber content respectively.
Finally, 60 upland genotypes from RBTN program were screened with AFLP markers. The pairwise kinship estimates were ranging between 0.1-0.88 accounting for most of the shared ancestral alleles. The MMR models improved the efficiency of marker selection with 38 markers associated with eight traits.
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