Bayesian composite model space approach for mapping quantitative trait Loci in variance component model.
In this article, we successfully apply the novel model selection method, Bayesian composite model space approach which has been used to map quantitative trait loci (QTL) for allelic substitution model, to map QTL for variance component model. The novel model selection approach has two advantages compared to the reversible jump Markov chain Monte Carlo method. Firstly, it mixes well due to the fixedness of the model dimension; secondly, it can map multiple QTL with higher power especially in genome-wide QTL mapping; finally, in the new method, it is also easy to incorporate our prior information about the variance components, which may bring precise estimate for variance components. A series of simulation experiments were conducted to demonstrate the general characters of the proposed method. The computer program is written in FORTRAN language, which is also built into a software "BayesMapQTL", and they also can be used for real data analysis and are available for request.
Fang
Ming
M
Life Science College, Heilongjiang August First Land Reclamation University, 163319 Daqing, People's Republic of China. fangming618@126.com
Liu
ShengCai
S
Jiang
Dan
D
D000465:Algorithms; D000483:Alleles; D000704:Analysis of Variance; D001499:Bayes Theorem; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005819:Genetic Markers / Q000235:genetics; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data; D006801:Humans; D008432:Mathematical Computing; D008957:Models, Genetic; D020412:Multifactorial Inheritance / Q000235:genetics; D010641:Phenotype; D011336:Probability; D040641:Quantitative Trait Loci / Q000235:genetics*; D019655:Quantitative Trait, Heritable; D012984:Software
D005819:Genetic Markers