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, <doi:10.1371/journal.pgen.1008766>.
|Depends:||R (≥ 3.4.0)|
|Imports:||glmnet, methods, stats, MASS, Matrix|
|Suggests:||RSpectra, popkin, bnpsd, testthat, covr, knitr, rmarkdown|
|Author:||Sahir Bhatnagar [aut, cre] (https://sahirbhatnagar.com/), Karim Oualkacha [aut] (http://karimoualkacha.uqam.ca/), Yi Yang [aut] (http://www.math.mcgill.ca/yyang/), Celia Greenwood [aut] (http://www.mcgill.ca/statisticalgenetics/)|
|Maintainer:||Sahir Bhatnagar <sahir.bhatnagar at gmail.com>|
|License:||MIT + file LICENSE|
|CRAN checks:||ggmix results|
Alternative Inputs for Population Structure
Introduction to the ggmix package
Incorporating Prior Annotation Weights
|Windows binaries:||r-devel: ggmix_0.0.2.zip, r-release: ggmix_0.0.2.zip, r-oldrel: ggmix_0.0.2.zip|
|macOS binaries:||r-release (arm64): ggmix_0.0.2.tgz, r-release (x86_64): ggmix_0.0.2.tgz, r-oldrel: ggmix_0.0.2.tgz|
|Old sources:||ggmix archive|
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