ArticleJournal of computational biology : a journal of computational molecular cell biology2005
Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Pharmacogenomic characterization of gemcitabine response--a framework for data integration to enable personalized medicine.Pharmacogenetics and genomics · 2014Trial
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- Synthetic data generation with probabilistic Bayesian Networks.Mathematical biosciences and engineering : MBE · 2021Article
- One-carbon metabolism biomarkers and genetic variants in relation to colorectal cancer risk by KRAS and BRAF mutation status.PloS one · 2018Article
- Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling.Proceedings of the National Academy of Sciences of the United States of America · 2017Article
- New Algorithm and Software (BNOmics) for Inferring and Visualizing Bayesian Networks from Heterogeneous Big Biological and Genetic Data.Journal of computational biology : a journal of computational molecular cell biology · 2017Article
- Untangling the role of one-carbon metabolism in colorectal cancer risk: a comprehensive Bayesian network analysis.Scientific reports · 2017Article
- Personalized cardiovascular medicine: concepts and methodological considerations.Nature reviews. Cardiology · 2013Review
- Data mining approaches for genome-wide association of mood disorders.Psychiatric genetics · 2012Article
- Application of Bayesian network structure learning to identify causal variant SNPs from resequencing data.BMC proceedings · 2011Article
- Systems biology data analysis methodology in pharmacogenomics.Pharmacogenomics · 2011Review
- Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies.Journal of computational biology : a journal of computational molecular cell biology · 2009Article
- Phenotype forecasting with SNPs data through gene-based Bayesian networks.BMC bioinformatics · 2009Article
- The challenges for molecular nutrition research 1: linking genotype to healthy nutrition.Genes & nutrition · 2008Article
- Mining gene networks with application to GAW15 Problem 1.BMC proceedings · 2007Article
- Applications of artificial intelligence systems in the analysis of epidemiological data.European journal of epidemiology · 2006Review
- Pharmacogenetics of response to statins: where do we stand?Current atherosclerosis reports · 2005Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.
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Registered trials
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