Bayesian hierarchical regression model to detect quantitative trait loci
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Haikun Bao (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
- Advisor
- Susan Simmons
Abstract: Detecting genetic loci responsible for variation in quantitative traits is a problem
of great importance to biologists. The location on a genetic map responsible for
a quantitative trait is referred to as Quantitative Trait Loci, or QTL. This thesis
uses a Bayesian Hierarchical Regression model which incorporates variability both
within and between lines to detect the QTL. This method is applied to a simulated
data set using the line information from Bay-0 × Shahdara population to find the
activation probability of each genetic segment via the Gibbs sampler and Monte
Carlo integration techniques. Using the activation probability, which indicates the
influence of each segment within all the models, the QTL is detected. The results
show that it is an effective way to detect QTL.
Bayesian hierarchical regression model to detect 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 Arts
- Language: English
- Date: 2009
- Keywords
- Bayesian statistical decision theory, Genetics--Statistical methods, Mathematical statistics
- Subjects
- Bayesian statistical decision theory
- Mathematical statistics
- Genetics -- Statistical methods