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Dissertation - University Access Only
Doctor of Philosophy (PhD)
Mathematics and Statistics
Genetic analysis aims at providing useful genetic information that can be used in plant or animal improvement. Genetic data are obtained from measuring traits in field experiments. One of the common problems associated with the field experiments is the field variation due to the heterogeneity of experimental units. Ineffective control of field variation in genetic analysis may increase the residual variance and results in biased estimation of genetic effects. This research addressed this problem by extending the genetic models with augmented experimental designs. Without losing the focus, two extended genetic models were proposed: generalized lattice model and sub-block model. A modeling frame work was provided for these extended genetic models using mixed linear modeling approaches. Data from a genetic mapping study in cotton and early generation trial in spring wheat were used to demonstrate the use of proposed models. With the use of generalized lattice model, the residual variance was reduced approximately by 65% for seed and lint yields in cotton and consequently, the heritability was increased by 38% for these traits. With the use of sub-block model, the residual variance was decreased by 29% for grain yield and 22% for plant height in spring wheat. This suggests that, accounting field variation in genetic models is important and the proposed models can reduce the impact of field variation on genetic data analysis.
Includes bibliographical references.
Number of Pages
South Dakota State University
In Copyright - Educational Use Permitted
Bondalapati, Krishna D., "Improving Genetic Analysis with Augmented Experimental Designs" (2013). Theses and Dissertations. 1390.