The bmass R package provides accessible functions for running the
algorithms described in Stephens 2013 PLOS ONE and
applied to multiple large, publicly available GWAS datasets in
Turchin and Stephens 2019 PLOS Genetics. bmass conducts a
Bayesian multivariate analysis of GWAS data using univariate
association summary statistics. Output inclues whether any new SNPs
are found as multivariate genome-wide significant as well as posterior
probabilities of each significant SNP's assignment to different
multivariate models.
For more details on the results of applying bmass to publicly available
GWAS datasets, please see our paper in PLOS Genetics. For
more details regarding the underlying algorithms of bmass, please see
Stephens 2013 PLOS ONE.
If you find a bug, or you have a question or feedback on our work, please post an issue.
If you find the bmass package or any of the source code in this
repository useful for your work, please cite:
Turchin MC and Stephens M (2019) "Bayesian multivariate reanalysis of large genetic studies identifies many new associations." PLOS Genetics 15(10): e1008431. doi.org/10.1371/journal.pgen.1008431
Copyright (c) 2016-2019, Michael Turchin and Matthew Stephens.
All source code and software in this repository are made available under the terms of the MIT license. See file LICENSE for the full text of the license.
To install bmass from CRAN:
install.packages("bmass")
To install the most recent dev version of bmass from github:
install.packages("devtools")
devtools::install_github("mturchin20/bmass@v1.0.3", build_vignettes=TRUE)
Once you have installed the package, load the package in R:
library("bmass")
Next, view and run the example code provided in the first introductory vignette using simulated data. A second, more advanced introductory vignette is also available that involves downloading, processing, and analyzing the GlobalLipids 2013 data.
The bmass R package was developed by Michael Turchin at
the University of Chicago, with contributions from
Peter Carbonetto and Matthew Stephens, and based
on the R code provided in Stephens 2013 PLOS ONE.