How to Install and Uninstall gemma Package on Ubuntu 21.04 (Hirsute Hippo)

Last updated: November 25,2024

1. Install "gemma" package

This tutorial shows how to install gemma on Ubuntu 21.04 (Hirsute Hippo)


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                gemma
            
                    
    
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2. Uninstall "gemma" package

Please follow the steps below to uninstall gemma on Ubuntu 21.04 (Hirsute Hippo):


    
        
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                sudo apt remove
            
                        
                gemma
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the gemma package on Ubuntu 21.04 (Hirsute Hippo)

Package: gemma
Architecture: amd64
Version: 0.98.4+dfsg-1
Priority: optional
Section: universe/science
Origin: Ubuntu
Maintainer: Ubuntu Developers
Original-Maintainer: Debian Med Packaging Team
Bugs: https://bugs.launchpad.net/ubuntu/+filebug
Installed-Size: 1193
Depends: libc6 (>= 2.29), libgcc-s1 (>= 3.0), libgsl25 (>= 2.6), libopenblas0, libstdc++6 (>= 9), zlib1g (>= 1:1.1.4)
Filename: pool/universe/g/gemma/gemma_0.98.4+dfsg-1_amd64.deb
Size: 405968
MD5sum: f75f653b102a417c6588b47150199394
SHA1: 66f9f06f2cec47c013d8f03cae505e1b9f0a87a0
SHA256: 3bc44888d143367be1a7e72b9c9a94571f70870804d4760bd118487075633f39
SHA512: e63234c1b5c9de548fe783a317e0c2b863256ee4da8dd3e2c78960bd27b4ad0f020f8db2de27bd490ad3439489b95cb13a176d1edc073578bad1327265b11278
Homepage: https://www.xzlab.org/software.html
Description-en: Genome-wide Efficient Mixed Model Association
GEMMA is the software implementing the Genome-wide Efficient Mixed
Model Association algorithm for a standard linear mixed model and some
of its close relatives for genome-wide association studies (GWAS):
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* It fits a univariate linear mixed model (LMM) for marker association
tests with a single phenotype to account for population stratification
and sample structure, and for estimating the proportion of variance in
phenotypes explained (PVE) by typed genotypes (i.e. "chip heritability").
* It fits a multivariate linear mixed model (mvLMM) for testing marker
associations with multiple phenotypes simultaneously while controlling
for population stratification, and for estimating genetic correlations
among complex phenotypes.
* It fits a Bayesian sparse linear mixed model (BSLMM) using Markov
chain Monte Carlo (MCMC) for estimating PVE by typed genotypes,
predicting phenotypes, and identifying associated markers by jointly
modeling all markers while controlling for population structure.
* It estimates variance component/chip heritability, and partitions
it by different SNP functional categories. In particular, it uses HE
regression or REML AI algorithm to estimate variance components when
individual-level data are available. It uses MQS to estimate variance
components when only summary statisics are available.
.
GEMMA is computationally efficient for large scale GWAS and uses freely
available open-source numerical libraries.
Description-md5: b7ae9da559267220e691d02459f93998