How to Install and Uninstall gemma Package on Ubuntu 21.10 (Impish Indri)

Last updated: May 10,2024

1. Install "gemma" package

This guide covers the steps necessary to install gemma on Ubuntu 21.10 (Impish Indri)


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

Learn how to uninstall gemma on Ubuntu 21.10 (Impish Indri):


    
        
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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 21.10 (Impish Indri)

Package: gemma
Architecture: amd64
Version: 0.98.4+dfsg-4
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: 950
Depends: libc6 (>= 2.33), libgcc-s1 (>= 3.3.1), libgsl25 (>= 2.6), libopenblas0, libstdc++6 (>= 9), zlib1g (>= 1:1.1.4)
Filename: pool/universe/g/gemma/gemma_0.98.4+dfsg-4_amd64.deb
Size: 365486
MD5sum: 5df79c5f07c731d4483192db85fa90c3
SHA1: f92e9d4a414c1588e95785b41ae618a2502d9d19
SHA256: 123e27cf1a9858dbdcc224997b7c39ad74e0a3c1e3183dcf2cf6b40e3a1dca59
SHA512: 2c2965dcfc30f18587d0ce97e9b6d36474ae1c40d5366151514f1daa082c47d35109209cf644b5f1017e798a1b22f8f6e5d9c4fead02671cc9331565e39563c2
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