Parallelized Genomic Prediction and GWAS

Frequently used methods in genomic applications with emphasis on parallel computing (OpenMP). At its core, the package has a Gibbs Sampler that allows running univariate linear mixed models that have both, sparse and dense design matrices. The parallel sampling method in case of dense design matrices (e.g. Genotypes) allows running Ridge Regression or BayesA for a very large number of individuals. The Gibbs Sampler is capable of running Single Step Genomic Prediction models. In addition, the package offers parallelized functions for common tasks like genome-wide association studies and cross validation in a memory efficient way.


Reference manual

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0.1 by Claas Heuer, 4 years ago

Browse source code at

Authors: Claas Heuer

Documentation:   PDF Manual  

GPL (>= 2) license

Imports methods, stats

Depends on Matrix, pedigreemm

Linking to Rcpp, RcppEigen, RcppProgress

System requirements: C++11

See at CRAN