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Source: gemma
Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
Uploaders: Steffen Moeller <moeller@debian.org>
Section: science
Priority: optional
Build-Depends: debhelper-compat (= 13),
               gfortran,
               libopenblas-dev,
               libeigen3-dev,
               libgsl-dev,
               zlib1g-dev,
               catch,
               shunit2,
               ruby <!nocheck>
Standards-Version: 4.6.0
Vcs-Browser: https://salsa.debian.org/med-team/gemma
Vcs-Git: https://salsa.debian.org/med-team/gemma.git
Homepage: https://www.xzlab.org/software.html
Rules-Requires-Root: no

Package: gemma
Architecture: any
Depends: ${shlibs:Depends},
         ${misc:Depends}
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):
 .
  * 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.

Package: gemma-doc
Architecture: all
Multi-Arch: foreign
Section: doc
Depends: ${misc:Depends}
Recommends: gemma
Description: Example folder for GEMMA
 This package ships example data for the Genome-wide Efficient Mixed
 Model Association.