QTL detection from stochastic process by Bayesian hierarchial regression model
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Yi Chen (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
- Advisor
- Susan Simmons
Abstract: The problem of identifying the genetic loci contributing to variation in a quantitative
trait (called QTL) has been researched for a number of years, and is a growing
eld in statistical genetics[10]. Most research focuses on the problem with only
one observation per genotype. For years, plant biologists have condensed replicates
within lines to one genotype to use these conventional methods. In this paper we
extend and apply one of the most widely used Markov Chain Monte Carlo Model
Comparison(MC3) algorithms, incorporated in a Bayesian hierarchical regression
setting. This algorithm is then applied to simulation data in order to validate the
model. Use of Posterior Model Probability and Activation Probability will be used
for model comparison. Furthermore, based on Acceptance Probability, we perform
stochastic search through the model space to identify potential QTL.
QTL detection from stochastic process by Bayesian hierarchial regression model
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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, Random walks (Mathematics), Stochastic processes
- Subjects
- Random walks (Mathematics)
- Stochastic processes
- Bayesian statistical decision theory