Title :
Quantitative Trait Loci Analysis Using a Bayesian Framework
Author :
Boone, Edward L. ; Ricanek, Karl, Jr. ; Simmons, Susan J.
Author_Institution :
Virginia Commonwealth Univ., Richmond
Abstract :
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.
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; biology computing; genetics; statistical analysis; Bayesian framework; Markov chain Monte Carlo method composition; chromosome setting; classification rate; genetic statistics; quantitative trait loci analysis; quantitative trait loci mapping; statistical perspective; Analytical models; Bayesian methods; Bioinformatics; Biological cells; Biological system modeling; Genetics; Genomics; Least squares approximation; Neural networks; Statistical analysis;
Conference_Titel :
Neural Networks, 2007. IJCNN 2007. International Joint Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
978-1-4244-1379-9
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2007.4371053