ArticleBMC genetics2003
Locating disease genes using Bayesian variable selection with the Haseman-Elston method.
Article in BMC genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Family-based analysis of a myocardial infarction endophenotype: comparison of sampling designs.BMC proceedings · 2009Article
- Identification of significant genes in genomics using Bayesian variable selection methods.Advances and applications in bioinformatics and chemistry : AABC · 2008Article
- A Bayesian genome-wide linkage analysis of quantitative traits for rheumatoid arthritis via perfect sampling.BMC proceedings · 2007Article
- A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method.BMC genetics · 2005Article
- A Bayesian approach for applying Haseman-Elston methods.BMC genetics · 2005Article
- Screening large-scale association study data: exploiting interactions using random forests.BMC genetics · 2004Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWe applied stochastic search variable selection (SSVS), a Bayesian model selection method, to the simulated data of Genetic Analysis Workshop 13. We used SSVS with the revisited Haseman-Elston method to find the markers linked to the loci determining change in cholesterol over time. To study gene-gene interaction (epistasis) and gene-environment interaction, we adopted prior structures, which incorporate the relationship among the predictors. This allows SSVS to search in the model space more efficiently and avoid the less likely models.
resultsIn applying SSVS, instead of looking at the posterior distribution of each of the candidate models, which is sensitive to the setting of the prior, we ranked the candidate variables (markers) according to their marginal posterior probability, which was shown to be more robust to the prior. Compared with traditional methods that consider one marker at a time, our method considers all markers simultaneously and obtains more favorable results.
conclusionsWe showed that SSVS is a powerful method for identifying linked markers using the Haseman-Elston method, even for weak effects. SSVS is very effective because it does a smart search over the entire model space.
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