Abstract：
:In this article, we utilize stochastic search variable selection methodology to develop a Bayesian method for identifying multiple quantitative trait loci (QTL) for complex traits in experimental designs. The proposed procedure entails embedding multiple regression in a hierarchical normal mixture model, where latent indicators for all markers are used to identify the multiple markers. The markers with significant effects can be identified as those with higher posterior probability included in the model. A simple and easy-to-use Gibbs sampler is employed to generate samples from the joint posterior distribution of all unknowns including the latent indicators, genetic effects for all markers, and other model parameters. The proposed method was evaluated using simulated data and illustrated using a real data set. The results demonstrate that the proposed method works well under typical situations of most QTL studies in terms of number of markers and marker density.
journal_name
Geneticsjournal_title
Geneticsauthors
Yi N,George V,Allison DBkeywords:
subject
Has Abstractpub_date
2003-07-01 00:00:00pages
1129-38issue
3eissn
0016-6731issn
1943-2631journal_volume
164pub_type
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