ggmix
Variable Selection in Linear Mixed Models for SNP Data
Fit penalized multivariable linear mixed models with a single
random effect to control for population structure in genetic association
studies. The goal is to simultaneously fit many genetic variants at the
same time, in order to select markers that are independently associated
with the response. Can also handle prior annotation information,
for example, rare variants, in the form of variable weights. For more
information, see the website below and the accompanying paper:
Bhatnagar et al., "Simultaneous SNP selection and adjustment for
population structure in high dimensional prediction models", 2020,
ggmix: Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models
Installation
# stable version from CRAN
install.packages("ggmix")
# development version from GitHub
if (!requireNamespace("pacman")) install.packages("pacman")
pacman::p_load_gh('sahirbhatnagar/ggmix')
Methodological Details
The companion paper is available at https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008766
Documentation
Please visit https://sahirbhatnagar.com/ggmix/ for details on how to use this package.
Please note that the 'ggmix' project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
References
If you use ggmix in your work, I would highly appreciate if you cite the paper and the package:
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Bhatnagar SR, Yang Y, Lu T, Schurr E, Loredo-Osti JC, Forest M, Oualkacha K, Greenwood CMT (2020). Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. PLoS Genet 16(5): e1008766. https://doi.org/10.1371/journal.pgen.1008766.
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Bhatnagar SR, Oualkacha K, Yang Y, Greenwood CMT (2020). ggmix: Variable Selection in Linear Mixed Models for SNP Data. R package version 0.0.2. https://github.com/sahirbhatnagar/ggmix.