Tags

Type your tag names separated by a space and hit enter

Stochastic search variable selection for identifying multiple quantitative trait loci.
Genetics. 2003 Jul; 164(3):1129-38.G

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.

Authors+Show Affiliations

Department of Biostatistics, University of Alabama, Birmingham 35294-0022, USA. nyi@ms.soph.uab.eduNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, U.S. Gov't, P.H.S.

Language

eng

PubMed ID

12871920

Citation

Yi, Nengjun, et al. "Stochastic Search Variable Selection for Identifying Multiple Quantitative Trait Loci." Genetics, vol. 164, no. 3, 2003, pp. 1129-38.
Yi N, George V, Allison DB. Stochastic search variable selection for identifying multiple quantitative trait loci. Genetics. 2003;164(3):1129-38.
Yi, N., George, V., & Allison, D. B. (2003). Stochastic search variable selection for identifying multiple quantitative trait loci. Genetics, 164(3), 1129-38.
Yi N, George V, Allison DB. Stochastic Search Variable Selection for Identifying Multiple Quantitative Trait Loci. Genetics. 2003;164(3):1129-38. PubMed PMID: 12871920.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Stochastic search variable selection for identifying multiple quantitative trait loci. AU - Yi,Nengjun, AU - George,Varghese, AU - Allison,David B, PY - 2003/7/23/pubmed PY - 2004/3/18/medline PY - 2003/7/23/entrez SP - 1129 EP - 38 JF - Genetics JO - Genetics VL - 164 IS - 3 N2 - 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. SN - 0016-6731 UR - https://www.unboundmedicine.com/medline/citation/12871920/Stochastic_search_variable_selection_for_identifying_multiple_quantitative_trait_loci_ L2 - http://www.genetics.org/cgi/pmidlookup?view=long&pmid=12871920 DB - PRIME DP - Unbound Medicine ER -