Abstract:
: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.
journal_name
Behav Genetjournal_title
Behavior geneticsauthors
Fang M,Liu S,Jiang Ddoi
10.1007/s10519-009-9259-ysubject
Has Abstractpub_date
2009-05-01 00:00:00pages
337-46issue
3eissn
0001-8244issn
1573-3297journal_volume
39pub_type
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