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Asessing statistical significance in predictive GWA studies

Bioconductor version: Release (3.18)

Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers.

Author: Laura Buzdugan

Maintainer: Laura Buzdugan <buzdugan at stat.math.ethz.ch>

Citation (from within R, enter citation("hierGWAS")):


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if (!require("BiocManager", quietly = TRUE))


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User manual for R-Package hierGWAS PDF R Script
Reference Manual PDF


biocViews Clustering, LinkageDisequilibrium, SNP, Software
Version 1.32.0
In Bioconductor since BioC 3.2 (R-3.2) (8.5 years)
License GPL-3
Depends R (>= 3.2.0)
Imports fastcluster, glmnet, fmsb
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Suggests BiocGenerics, RUnit, MASS
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Follow Installation instructions to use this package in your R session.

Source Package hierGWAS_1.32.0.tar.gz
Windows Binary hierGWAS_1.32.0.zip
macOS Binary (x86_64) hierGWAS_1.32.0.tgz
macOS Binary (arm64) hierGWAS_1.32.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/hierGWAS
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/hierGWAS
Bioc Package Browser https://code.bioconductor.org/browse/hierGWAS/
Package Short Url https://bioconductor.org/packages/hierGWAS/
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