Analysis of a hierarchial Bayesian method for quantitative trait loci
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Caroline Pearson (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
- Advisor
- Susan Simmons
Abstract: Simulations were performed to compare two methods that detect quantitative trait loci on
plant data. Karl Broman’s interval mapping algorithm which uses only one observation value per
plant line was compared to a hierarchical Bayesian model that allows replicates into the analysis
and takes into account the variability within each plant line. The simulation study utilized the
genetic map of Bay-0 X Shahdara plant with 38 genetic markers on 5 chromosomes. It is shown
through these simulations that the hierarchical Bayesian model and Broman’s interval mapping
algorithm are able to detect quantitative trait loci (QTL) when only a single location was chosen,
but the hierarchical model was more powerful when two locations were chosen. This work
shows that when analyzing plant replicates the variability within each line has a strong impact on
the success of the overall analyses.
Analysis of a hierarchial Bayesian method for quantitative trait loci
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Created on 1/1/2009
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Additional Information
- Publication
- Thesis
- A Thesis Submitted to the University of North Carolina at Wilmington in Partial Fulfillment of the Requirement for the Degree of Masters of Science
- Language: English
- Date: 2009
- Keywords
- Bayesian statistical decision theory, Genetics--Mathematical models
- Subjects
- Bayesian statistical decision theory
- Genetics -- Mathematical models