Analysis of a hierarchial Bayesian method for quantitative trait loci

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

Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo

This thesis consists ideas of two new population Markov chain Monte Carlo algorithms and an automatic proposal mechanism for the Reversible jump Markov chain Monte Carlo algorithm