Quantitative Trait Loci Analysis Using a Bayesian Framework

Quantitative trait loci mapping is a growing field in genetic statistics. However, dealing with this type of data from a statistical perspective is often perilous. This work considers a model selection approach to quantitative trait loci analysis of the Arabidopsis Bay-O by Shahdara using a Bayesian framework, Markov Chain Monte Carlo method composition. The efficacy of this technique is demonstrated on simulated data of 158 Arabidopsis Bay-O by Shahdara recombinant inbred lines. A simulation study is employed to study the correct classification, false positive and false negative rates of the method. This was performed for a single chromosome and a three chromosome setting. The results show a high correct classification rate with a low false negative rate and a moderate false positive rate. The results of this work demonstrate the robustness of this algorithm for quantitative trait loci analysis.