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

TitleMining genetic epidemiology data with Bayesian networks I: Bayesian networks and example application (plasma apoE levels).
Publication TypeJournal Article
Year of Publication2005
AuthorsRodin, AS, Boerwinkle, E
JournalBioinformatics
Volume21
Issue15
Pagination3273-8
Date Published2005 Aug 01
ISSN1367-4803
KeywordsApolipoproteins E, Bayes Theorem, Chromosome Mapping, Computer Simulation, Database Management Systems, Databases, Genetic, DNA Mutational Analysis, Epidemiologic Methods, Genotype, Humans, Information Storage and Retrieval, Models, Genetic, Models, Statistical, Polymorphism, Single Nucleotide
Abstract

MOTIVATION: The wealth of single nucleotide polymorphism (SNP) data within candidate genes and anticipated across the genome poses enormous analytical 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 paper, 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 20 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 the well-known epsilon2 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 given in the text) as well as source code extensions, are available from the authors.SUPPLEMENTARY INFORMATION: http://bioinformatics.oxfordjournals.org.

DOI10.1093/bioinformatics/bti505
Alternate JournalBioinformatics
PubMed ID15914545
PubMed Central IDPMC1201438
Grant ListP50 GM065509 / GM / NIGMS NIH HHS / United States
R01 HL072905 / HL / NHLBI NIH HHS / United States