How to Install and Uninstall gemma Package on Kali Linux

Last updated: April 29,2024

1. Install "gemma" package

This guide let you learn how to install gemma on Kali Linux


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

This guide covers the steps necessary to uninstall gemma on Kali Linux:


    
        
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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 Kali Linux

Package: gemma
Version: 0.98.5+dfsg-2
Installed-Size: 1177
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgsl27 (>= 2.7.1), libopenblas0, libstdc++6 (>= 11), zlib1g (>= 1:1.1.4)
Size: 406776
SHA256: 917abf973333596f5b80329128d3fc8510774cbd7358a90623a6c79f6860b15f
SHA1: 48927bcf5c9d9af4903d1802af0730db99adddad
MD5sum: 0eb6aa2328f50055fd0eced5f841e60d
Description: 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:
Homepage: https://www.xzlab.org/software.html
Section: science
Priority: optional
Filename: pool/main/g/gemma/gemma_0.98.5+dfsg-2_amd64.deb