gemma 0.98+dfsg-3 source package in Ubuntu

Changelog

gemma (0.98+dfsg-3) unstable; urgency=medium

  * Team upload
  * Allow output to stderr in autopkgtests

 -- Graham Inggs <email address hidden>  Tue, 13 Nov 2018 13:33:54 +0000

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Uploaded by:
Debian Med on 2018-11-13
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

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gemma_0.98+dfsg-3.dsc 2.0 KiB 9838e99a44bb9a25e3059a6785a729dc745aa00d6176bd5ec8b104ea37ea0962
gemma_0.98+dfsg.orig.tar.gz 46.9 MiB bf76cdae41192ae9312a08a820c81435a7b1b6655952f3d011ef60cab5be7cc1
gemma_0.98+dfsg-3.debian.tar.xz 4.9 KiB 26ef8df07564b86af6b00f1c9681e1cf7c236ca9f84bc2733566006fa91c1025

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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.