Skip Navigation



Bioinformatics Advance Access published online on May 24, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti505
This Article
Right arrow Advance Access manuscript (PDF) Freely available
Right arrow Supplementary Data
Right arrow Editorial Note
Right arrow All Versions of this Article:
21/15/3273    most recent
bti505v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Rodin, A. S.
Right arrow Articles by Boerwinkle, E.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Rodin, A. S.
Right arrow Articles by Boerwinkle, E.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received September 24, 2004
Revised May 3, 2005
Accepted May 17, 2005

Article

Mining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels)

Andrei S. Rodin 1* and Eric Boerwinkle 2

1 Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, Texas 77030
2 Human Genetics Center, School of Public Health, University of Texas Health Science Center, Houston, Texas 77030; Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030

* To whom correspondence should be addressed.
Andrei S. Rodin, E-mail: arodin{at}uth.tmc.edu


  Abstract

Motivation: The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytic problems for studies of genotype-to-phenotype relationships, and modern data mining methods may be particularly well suited to meet the swelling challenges. In this manuscript we introduce the method of Belief (Bayesian) networks to the domain of genotype-to-phenotype analyses and provide an example application.

Results: A Belief network is a graphical model of a probabilistic nature that represents a joint multivariate probability distribution and reflects conditional independences between variables. Given the data, optimal network topology can be estimated with the assistance of heuristic search algorithms and scoring criteria. Statistical significance of edge strengths can be evaluated using Bayesian methods and bootstrapping. As an example application, the method of Belief networks was applied to twenty SNPs in the apolipoprotein (apo) E gene and plasma apoE levels in a sample of 702 individuals from Jackson, MS. Plasma apoE level was the primary target variable. These analyses indicate that the edge between SNP 4075, coding for well-known {varepsilon}2 allele, and plasma apoE level was strong. Belief networks can effectively describe complex uncertain processes and can both learn from data and incorporate prior knowledge.

Availability: Various alternative and supplemental networks (not shown in text), as well as source code extensions, are available from the authors.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
W. Li, M. Wang, P. Irigoyen, and P. K. Gregersen
Inferring causal relationships among intermediate phenotypes and biomarkers: a case study of rheumatoid arthritis
Bioinformatics, June 15, 2006; 22(12): 1503 - 1507.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.