Title page for ETD etd-1110103-143019


Type of Document Dissertation
Author Aluko, Gabriel Kayode
Author's Email Address galuko1@lsu.edu
URN etd-1110103-143019
Title Genetic Mapping of Agronomic Traits from the Interspecific Cross of Oryza sativa (L.) and Oryza glaberrima (Steud.)
Degree Doctor of Philosophy (Ph.D.)
Department Agronomy & Environmental Management
Advisory Committee
Advisor Name Title
James Oard Committee Chair
Charles Johnson Committee Member
Don Labonte Committee Member
Gerald Myers Committee Member
James Griffin Dean's Representative
Keywords
  • microsatellite
  • discriminant analysis
  • mapping
  • grain quality
  • oryza glaberrima
Date of Defense 2003-10-24
Availability unrestricted
Abstract
Wild relatives of cultivated rice varieties offer new genetic sources for enhancing economic value, but traditional interval mapping techniques have not gained widespread support among applied researchers for marker assisted selection. The objectives of this study were to detect quantitative trait loci (QTL) for agronomic traits in a hybrid mapping population and compare the non-parametric Discriminant Analysis (DA) procedure with traditional approaches for accuracy and precision. In addition, the effects of population structure on marker-assisted classification were explored. A molecular linkage map comprising 100 SSR markers that spanned the rice genome at intervals of 10.5 cM on the average was constructed based on 312 doubled haploid lines derived from the cross interspecific Oryza sativa x O. glaberrima.

The mapping population was evaluated in replicated field plots in Colombia and Louisiana in 2001 and 2002, respectively. QTLs were identified for grain, milling and eating qualities and important agronomic traits such as heading date, plant height, number of tillers per plant, panicle length, grain yield and 1000-grain weight. A total of 28 QTLs were detected for 10 grain quality traits, and 22 QTLs for six agronomic traits were detected that were significant in at least one environment, but only seven were significant in both environments. SSR markers that best discriminated between pre-defined groups of high and low trait values were selected by stepwise DA. Using a k-nearest neighbor algorithm, the largest phenotypic differentiation (3 standard deviations) between two contrasting phenotypic groups resulted in 100% correct classification. Adjustments for population structure resulted in a 5-fold decrease in number of markers needed to achieve the same level of accuracy. These results demonstrated that procedures such as DA and consideration of population structure can be used for efficient marker-based allocation of the doubled haploid lines into pre-defined groups for yield and other agronomic traits. Finally, DA-selected markers pointed to the same or closely linked regions on the linkage map that in turn underscored the validity of the DA approach for genetic mapping.

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