ArticleBMC genetics2005
A Bayesian approach for applying Haseman-Elston methods.
Article in BMC genetics, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- An efficient method to handle the 'large p, small n' problem for genomewide association studies using Haseman-Elston regression.Journal of genetics · 2016Article
- Mixture modeling of microarray gene expression data.BMC proceedings · 2007Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main goal of this paper is to couple the Haseman-Elston method with a simple yet effective Bayesian factor-screening approach. This approach selects markers by considering a set of multigenic models that include epistasis effects. The markers are ranked based on their marginal posterior probability. A significant improvement over our previously proposed Bayesian variable selection methodology is a simple Metropolis-Hasting algorithm that requires minimum tuning on the prior settings. The algorithm, however, is also flexible enough for us to easily incorporate our hypotheses and avoid computational pitfalls. We apply our approach to the microsatellite data of Collaborative Studies on Genetics of Alcoholism using the coded values for the ALDX1 variable as our response.
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Registered trials
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