Stochastic search variable selection for identifying multiple quantitative trait loci

Nengjun Yi, Varghese George, David B. Allison

Research output: Contribution to journalArticlepeer-review

104 Scopus citations

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, generic 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.

Original languageEnglish (US)
Pages (from-to)1129-1138
Number of pages10
JournalGenetics
Volume164
Issue number3
StatePublished - Jul 1 2003
Externally publishedYes

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'Stochastic search variable selection for identifying multiple quantitative trait loci'. Together they form a unique fingerprint.

Cite this