ArticleAmerican journal of human genetics2006
Bayesian graphical models for genomewide association studies.
Article in American journal of human genetics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Superior haplotypes for transforming crop breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- A method combining a random forest-based technique with the modeling of linkage disequilibrium through latent variables, to run multilocus genome-wide association studies.BMC bioinformatics · 2018Article
- A BAYESIAN GRAPHICAL MODEL FOR GENOME-WIDE ASSOCIATION STUDIES (GWAS).The annals of applied statistics · 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
- A comparative analysis of methods for predicting clinical outcomes using high-dimensional genomic datasets.Journal of the American Medical Informatics Association : JAMIA · 2014Article
- CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.PLoS computational biology · 2014Article
- Brain galanin system genes interact with life stresses in depression-related phenotypes.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Analysis of gene-gene interactions using gene-trait similarity regression.Human heredity · 2014Article
- Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.PloS one · 2012Article
- Learning genetic epistasis using Bayesian network scoring criteria.BMC bioinformatics · 2011Article
- A hierarchical Bayesian network approach for linkage disequilibrium modeling and data-dimensionality reduction prior to genome-wide association studies.BMC bioinformatics · 2011Article
- Visualization of pairwise and multilocus linkage disequilibrium structure using latent forests.PloS one · 2011Article
- Efficient Bayesian approach for multilocus association mapping including gene-gene interactions.BMC bioinformatics · 2010Article
- The conditional independences between variables derived from two independent identically distributed Markov random fields when pairwise order is ignored.Mathematical medicine and biology : a journal of the IMA · 2010Article
- Selection of important variables by statistical learning in genome-wide association analysis.BMC proceedings · 2009Article
- Systematic haplotype analysis resolves a complex plasma plant sterol locus on the Micronesian Island of Kosrae.Proceedings of the National Academy of Sciences of the United States of America · 2009Article
- A method and program for estimating graphical models for linkage disequilibrium that scale linearly with the number of loci, and their application to gene drop simulation.Bioinformatics (Oxford, England) · 2009Article
- HaploBuild: an algorithm to construct non-contiguous associated haplotypes in family based genetic studies.Bioinformatics (Oxford, England) · 2007Article
- Genetic association mapping via evolution-based clustering of haplotypes.PLoS genetics · 2007Article
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3 authors.
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Abstract
As the extent of human genetic variation becomes more fully characterized, the research community is faced with the challenging task of using this information to dissect the heritable components of complex traits. Genomewide association studies offer great promise in this respect, but their analysis poses formidable difficulties. In this article, we describe a computationally efficient approach to mining genotype-phenotype associations that scales to the size of the data sets currently being collected in such studies. We use discrete graphical models as a data-mining tool, searching for single- or multilocus patterns of association around a causative site. The approach is fully Bayesian, allowing us to incorporate prior knowledge on the spatial dependencies around each marker due to linkage disequilibrium, which reduces considerably the number of possible graphical structures. A Markov chain-Monte Carlo scheme is developed that yields samples from the posterior distribution of graphs conditional on the data from which probabilistic statements about the strength of any genotype-phenotype association can be made. Using data simulated under scenarios that vary in marker density, genotype relative risk of a causative allele, and mode of inheritance, we show that the proposed approach has better localization properties and leads to lower false-positive rates than do single-locus analyses. Finally, we present an application of our method to a quasi-synthetic data set in which data from the CYP2D6 region are embedded within simulated data on 100K single-nucleotide polymorphisms. Analysis is quick (<5 min), and we are able to localize the causative site to a very short interval.
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