Package: MISA
Type: Package
Title: Bayesian Model Search and Multilevel Inference for SNP
Association Studies
Author: Melanie Wilson <maw27@stat.duke.edu>
Maintainer: Gary Lipton <gl37@stat.duke.edu>
Description: The functions in this package focus on intermediate
throughput case-control association studies, where the outcome
of interest is often a binary disease state and where the
genetic markers have been chosen to capture variation in a set
of related genes, such as those involved in a specific
biochemical pathway. Given this data, we are interested in
addressing two questions: "To what extent does the data support
an overall association between the pathway and outcome of
interest?" and "Which markers or genes are most likely to be
driving this association?" To address both of these
questions,this package performs a Bayesian model search
technique that utilizes Evolutionary Monte Carlo and searches
over models including main effects of all genetic markers and
marker-specific genetic effects in a computationally efficient
manner.  The package incorporates functions that perform a
marginal screen on the genetic markers, summarize the output of
the model search algorithm, including image plots of the models
with the highest posterior probability, marginal summaries of
SNP and gene inclusion probabilities and Bayes Factors, and
global summaries of the posterior probability and Bayes Factor
giving evidence of an association in the set of SNPs of
interest.
Version: 2.11.1-1.0.1
License: Unlimited
Depends: R (>= 2.10), coda
Suggests: tcltk
Packaged: 2012-07-23 09:31:19 UTC; ripley
Repository: CRAN
Date/Publication: 2012-07-23 10:35:31

Package: MISA
Type: Package
Title: Bayesian Model Search and Multilevel Inference for SNP
Association Studies
Author: Melanie Wilson <maw27@stat.duke.edu>
Maintainer: Gary Lipton <gl37@stat.duke.edu>
Description: The functions in this package focus on intermediate
throughput case-control association studies, where the outcome
of interest is often a binary disease state and where the
genetic markers have been chosen to capture variation in a set
of related genes, such as those involved in a specific
biochemical pathway. Given this data, we are interested in
addressing two questions: "To what extent does the data support
an overall association between the pathway and outcome of
interest?" and "Which markers or genes are most likely to be
driving this association?" To address both of these
questions,this package performs a Bayesian model search
technique that utilizes Evolutionary Monte Carlo and searches
over models including main effects of all genetic markers and
marker-specific genetic effects in a computationally efficient
manner.  The package incorporates functions that perform a
marginal screen on the genetic markers, summarize the output of
the model search algorithm, including image plots of the models
with the highest posterior probability, marginal summaries of
SNP and gene inclusion probabilities and Bayes Factors, and
global summaries of the posterior probability and Bayes Factor
giving evidence of an association in the set of SNPs of
interest.
Version: 2.11.1-1.0
License: Unlimited
Depends: coda
Suggests: tcltk
Packaged: 2011-02-17 14:02:11 UTC; gl37
Repository: CRAN
Date/Publication: 2011-02-17 19:30:23