How to Install and Uninstall bolt-lmm Package on Kali Linux

Last updated: November 25,2024

1. Install "bolt-lmm" package

This guide let you learn how to install bolt-lmm on Kali Linux


    
        
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                sudo apt update
            
                    
    
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            $
        

        
            
                sudo apt install
            
                        
                bolt-lmm
            
                    
    
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2. Uninstall "bolt-lmm" package

Learn how to uninstall bolt-lmm on Kali Linux:


    
        
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                sudo apt remove
            
                        
                bolt-lmm
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
    Copied

3. Information about the bolt-lmm package on Kali Linux

Package: bolt-lmm
Version: 2.4.1+dfsg-2
Installed-Size: 968
Maintainer: Debian Med Packaging Team
Architecture: amd64
Depends: libboost-iostreams1.83.0 (>= 1.83.0), libboost-program-options1.83.0 (>= 1.83.0), libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libnlopt0 (>= 2.6.1), libopenblas0, libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), zlib1g (>= 1:1.1.4)
Suggests: bolt-lmm-doc
Size: 351272
SHA256: 3ffe09bb2cede106e835162aec72033bf27cf0660e158f86f5058709d848e6a3
SHA1: 636ebb6219b9c7a74e48741255be3e0bb7e85625
MD5sum: b95807c93abac268f0dc2745181a732c
Description: Efficient large cohorts genome-wide Bayesian mixed-model association testing
The BOLT-LMM software package currently consists of two main algorithms, the
BOLT-LMM algorithm for mixed model association testing, and the BOLT-REML
algorithm for variance components analysis (i.e., partitioning of
SNP-heritability and estimation of genetic correlations).
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The BOLT-LMM algorithm computes statistics for testing association between
phenotype and genotypes using a linear mixed model. By default, BOLT-LMM
assumes a Bayesian mixture-of-normals prior for the random effect attributed
to SNPs other than the one being tested. This model generalizes the standard
infinitesimal mixed model used by previous mixed model association methods,
providing an opportunity for increased power to detect associations while
controlling false positives. Additionally, BOLT-LMM applies algorithmic
advances to compute mixed model association statistics much faster than
eigendecomposition-based methods, both when using the Bayesian mixture model
and when specialized to standard mixed model association.
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The BOLT-REML algorithm estimates heritability explained by genotyped SNPs and
genetic correlations among multiple traits measured on the same set of
individuals. BOLT-REML applies variance components analysis to perform these
tasks, supporting both multi-component modeling to partition SNP-heritability
and multi-trait modeling to estimate correlations. BOLT-REML applies a Monte
Carlo algorithm that is much faster than eigendecomposition-based methods for
variance components analysis at large sample sizes.
Description-md5:
Homepage: https://data.broadinstitute.org/alkesgroup/BOLT-LMM/
Section: science
Priority: optional
Filename: pool/main/b/bolt-lmm/bolt-lmm_2.4.1+dfsg-2_amd64.deb