Ubuntu
gemma package

gemma 0.98.5+dfsg-1 source package in Ubuntu

Changelog

gemma (0.98.5+dfsg-1) unstable; urgency=medium

  * Team upload.
  * Enable hardening
  * Rename autopkgtest script to use a unique name for all our packages
  * Standards-Version: 4.6.0 (routine-update)
  * Build time test is now using ruby

 -- Andreas Tille <email address hidden>  Wed, 06 Oct 2021 07:45:55 +0200

Upload details

Uploaded by:
Debian Med
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

Downloads

File Size SHA-256 Checksum
gemma_0.98.5+dfsg-1.dsc 2.1 KiB cd17c6599f8c601c23f9df106f4dbd3052b37f9c16e7abe4dca42e7d2df7c18e
gemma_0.98.5+dfsg.orig.tar.xz 42.1 MiB 6a9741ad53d2c581d0fb850de5807ca61fb3449f1d198bbab78291487187619a
gemma_0.98.5+dfsg-1.debian.tar.xz 6.4 KiB 1815a48dbb71e5543cb0ce0eff0c36dd7f3c254c1435aba2cb18069fb3126f5a

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.