Browse All

Theses & Dissertations


  • Submissions (Articles, Chapters, and other finished products)

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)
The University of North Carolina Wilmington (UNCW )
Web Site:
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.

Additional Information

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
Bayesian statistical decision theory, Genetics--Statistical methods, Mathematical statistics
Bayesian statistical decision theory
Mathematical statistics
Genetics -- Statistical methods