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, .


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install.packages("ggmix")

0.0.1 by Sahir Bhatnagar, 7 months ago


https://github.com/sahirbhatnagar/ggmix


Report a bug at https://github.com/sahirbhatnagar/ggmix/issues


Browse source code at https://github.com/cran/ggmix


Authors: 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/)


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports glmnet, methods, stats, MASS, Matrix

Suggests RSpectra, popkin, bnpsd, testthat, covr, knitr, rmarkdown


See at CRAN