Identification of candidate markers associated with agronomic traits in rice using discriminant analysis |
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Authors: | N. Zhang Y. Xu M. Akash S. McCouch J. H. Oard |
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Affiliation: | (1) Department of Agronomy and Environmental Management, LSU AgCenter, Louisiana State University, Baton Rouge, LA 70803, USA;(2) Department of Plant Breeding, Cornell University, Ithaca, NY 14853, USA;(3) Department of Agronomy, Iowa State University, 2101 Agronomy Hall, Ames, IA 50011, USA |
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Abstract: | Plant genetic mapping strategies routinely utilize marker genotype frequencies obtained from progeny of controlled crosses to declare presence of a quantitative trait locus (QTL) on previously constructed linkage maps. We have evaluated the potential of discriminant analysis (DA), a multivariate statistical procedure, to detect candidate markers associated with agronomic traits among inbred lines of rice (Oryza sativa L.). A total of 218 lines originating from the US and Asia were planted in field plots near Alvin, Texas, in 1996 and 1997. Agronomic data were collected for 12 economically important traits, and DNA profiles of each inbred line were produced using 60 SSR and 114 RFLP markers. Model-based methods revealed population structure among the lines. Marker alleles associated with all traits were identified by DA at high levels of correct percent classification within subpopulations and across all lines. Associated marker alleles pointed to the same and different regions on the rice genetic map when compared to previous QTL mapping experiments. Results from this study suggest that candidate markers associated with agronomic traits can be readily detected among inbred lines of rice using DA combined with other methods described in this report.N. Zhang and Y. Xu contributed equally to work and considered as first authors.Approved for publication by the Director of the Louisiana Agricultural Experiment Station as paper no. 04-14-0335. |
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Keywords: | Association genetic mapping Discriminant analysis Marker-assisted selection Oryza sativa Quantitative trait locus |
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