gemma 0.98.1+dfsg-1 source package in Ubuntu

Changelog

gemma (0.98.1+dfsg-1) unstable; urgency=medium

  * Team upload.
  * Simplify watch file

 -- Andreas Tille <email address hidden>  Tue, 18 Dec 2018 16:16:05 +0100

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Uploaded by:
Debian Med on 2018-12-18
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Disco release on 2019-01-30 universe misc

Downloads

File Size SHA-256 Checksum
gemma_0.98.1+dfsg-1.dsc 2.1 KiB 1f9508964f50c33b686611a5fd62ed6d53a1c228dc479d56f3247720e7d632b2
gemma_0.98.1+dfsg.orig.tar.xz 40.6 MiB da205d973dc0254a6a8d89eb47531d7ce93d2bd2c86c957e1d54ec0b711755d5
gemma_0.98.1+dfsg-1.debian.tar.xz 4.8 KiB 0b367ad85cddd5a5cae82caa9c122a0c95997ffb7fd16d6e411bf5ed7ef41e22

Available diffs

No changes file available.

Binary packages built by this source

gemma: 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.

gemma-dbgsym: debug symbols for gemma
gemma-doc: Example folder for GEMMA

 This package ships example data for the Genome-wide Efficient Mixed
 Model Association.