Stochastic search variable selection for identifying multiple quantitative trait loci - PubMed (original) (raw)

Stochastic search variable selection for identifying multiple quantitative trait loci

Nengjun Yi et al. Genetics. 2003 Jul.

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.

PubMed Disclaimer

Similar articles

Cited by

References

    1. Genet Res. 1999 Dec;74(3):279-89 - PubMed
    1. Genetics. 2003 Feb;163(2):789-801 - PubMed
    1. Genetics. 1994 Apr;136(4):1457-68 - PubMed
    1. Genetics. 1996 Oct;144(2):805-16 - PubMed
    1. Genetics. 1998 Mar;148(3):1373-88 - PubMed

Publication types

MeSH terms

Substances

LinkOut - more resources