ArticlePloS one2011
A bayesian method for evaluating and discovering disease loci associations.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- Discovering causal interactions using Bayesian network scoring and information gain.BMC bioinformatics · 2016Article
- Computational methods for ubiquitination site prediction using physicochemical properties of protein sequences.BMC bioinformatics · 2016Article
- Evaluation of a two-stage framework for prediction using big genomic data.Briefings in bioinformatics · 2015Article
- LEAP: biomarker inference through learning and evaluating association patterns.Genetic epidemiology · 2015Article
- Learning Predictive Interactions Using Information Gain and Bayesian Network Scoring.PloS one · 2015Article
- A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.Journal of the American Medical Informatics Association : JAMIA · 2014Article
- Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models.Circulation. Cardiovascular genetics · 2014Article
- A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.Journal of biomedical informatics · 2014Article
- Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data.Cancer informatics · 2014Review
- Modeling the altered expression levels of genes on signaling pathways in tumors as causal bayesian networks.Cancer informatics · 2014Article
- Evaluation of an ensemble of genetic models for prediction of a quantitative trait.Frontiers in genetics · 2014Article
- An omnibus permutation test on ensembles of two-locus analyses can detect pure epistasis and genetic heterogeneity in genome-wide association studies.SpringerPlus · 2013Article
- GWIS--model-free, fast and exhaustive search for epistatic interactions in case-control GWAS.BMC genomics · 2013Article
- Naïve Bayesian Classifier and Genetic Risk Score for Genetic Risk Prediction of a Categorical Trait: Not so Different after all!Frontiers in genetics · 2012Article
- Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.PloS one · 2012Article
- Bayesian methods for multivariate modeling of pleiotropic SNP associations and genetic risk prediction.Frontiers in genetics · 2012Article
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4 authors.
Funding
Abstract
backgroundA genome-wide association study (GWAS) typically involves examining representative SNPs in individuals from some population. A GWAS data set can concern a million SNPs and may soon concern billions. Researchers investigate the association of each SNP individually with a disease, and it is becoming increasingly commonplace to also analyze multi-SNP associations. Techniques for handling so many hypotheses include the Bonferroni correction and recently developed bayesian methods. These methods can encounter problems. Most importantly, they are not applicable to a complex multi-locus hypothesis which has several competing hypotheses rather than only a null hypothesis. A method that computes the posterior probability of complex hypotheses is a pressing need. METHODOLOGY/
findingsWe introduce the bayesian network posterior probability (BNPP) method which addresses the difficulties. The method represents the relationship between a disease and SNPs using a directed acyclic graph (DAG) model, and computes the likelihood of such models using a bayesian network scoring criterion. The posterior probability of a hypothesis is computed based on the likelihoods of all competing hypotheses. The BNPP can not only be used to evaluate a hypothesis that has previously been discovered or suspected, but also to discover new disease loci associations. The results of experiments using simulated and real data sets are presented. Our results concerning simulated data sets indicate that the BNPP exhibits both better evaluation and discovery performance than does a p-value based method. For the real data sets, previous findings in the literature are confirmed and additional findings are found. CONCLUSIONS/SIGNIFICANCE: We conclude that the BNPP resolves a pressing problem by providing a way to compute the posterior probability of complex multi-locus hypotheses. A researcher can use the BNPP to determine the expected utility of investigating a hypothesis further. Furthermore, we conclude that the BNPP is a promising method for discovering disease loci associations.
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