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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0.0.2 by Sahir Bhatnagar, 10 months ago

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Authors: Sahir Bhatnagar [aut, cre] ( , Karim Oualkacha [aut] ( , Yi Yang [aut] ( , Celia Greenwood [aut] (

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports glmnet, methods, stats, MASS, Matrix

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

See at CRAN