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.

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