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.

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