Novel molecular and statistical approaches are needed for identification of DNA markers associated with complex traits in rice. The first research objective was to evaluate mixed-model and multiple regression approaches for their ability to identify molecular markers associated with complex traits in rice. A combined mixed model and multiple regression approach was optimal for selecting the smallest number of DNA markers associated with relatively high R2 values and for consistency with previous mapping studies.
Support Vector Regression (SVR) was evaluated in the second research objective for the ability to generate high levels of accuracy and power for markers associated with complex traits. High levels of prediction accuracy and power were observed for the selected markers. SVR produced greater model accuracy and ability to explain trait variation than multiple linear regression.
Single nucleotide polymorphic (SNP) markers for aroma, amylose content and gelatinization temperature were evaluated in the third research objective for marker-assisted improvement of breeding lines. This strategy increased frequency of desired alleles by an average of 26 percent in only two generations. Genetic analysis of pollen sterility was conducted in the fourth research objective for an F2 population derived from an outcross between a weedy biotype and a commercial variety. Segregation analyses revealed that seed fertility was governed by two dominant genes, a result similar to the cytoplasmic male sterile (CMS)-WA system used to develop commercial hybrids. Pollen sterility was controlled by two recessive genes. The pollen sterility trait could be exploited as a new source of CMS for hybrid rice breeding. Additional research is needed to confirm if lines developed from this natural outcross represent a new source of CMS. Overall results show that both standard and new data mining approaches can be used to successfully identify candidate genes and DNA markers associated with complex agronomic traits. In addition, the SNP markers were shown to rapidly enrich frequency of desired alleles associated with rice grain and cooking quality traits. All results demonstrated that a combination of molecular, statistical, and genetic approaches created an effective strategy to advance our understanding of factors that govern complex traits in rice.
| Identifer | oai:union.ndltd.org:LSU/oai:etd.lsu.edu:etd-07012009-193032 |
| Date | 03 July 2009 |
| Creators | Ordonez Jr., Samuel Agbayani |
| Contributors | James H. Oard, James P. Geaghan, Don R. LaBonte, Xueyan Sha, Jing Wang, Michael J. Stout |
| Publisher | LSU |
| Source Sets | Louisiana State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lsu.edu/docs/available/etd-07012009-193032/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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