gemma 0.98.5+dfsg-1build1 source package in Ubuntu

Changelog

gemma (0.98.5+dfsg-1build1) jammy; urgency=medium

  * No-change rebuild against libgsl27

 -- Steve Langasek <email address hidden>  Tue, 07 Dec 2021 17:30:54 +0000

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Uploaded by:
Steve Langasek
Uploaded to:
Jammy
Original maintainer:
Ubuntu Developers
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Jammy release universe misc

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File Size SHA-256 Checksum
gemma_0.98.5+dfsg.orig.tar.xz 42.1 MiB 6a9741ad53d2c581d0fb850de5807ca61fb3449f1d198bbab78291487187619a
gemma_0.98.5+dfsg-1build1.debian.tar.xz 6.5 KiB d3894a35f97dda4fdaa3c239b62db22eb33b94747b697c9ef14232cf35551dda
gemma_0.98.5+dfsg-1build1.dsc 2.2 KiB ac3f390c3a85d8fd35fd1a39d83f365fbd81048543417ab9689d26b89e3f4f2a

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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: No summary available for gemma-dbgsym in ubuntu kinetic.

No description available for gemma-dbgsym in ubuntu kinetic.

gemma-doc: Example folder for GEMMA

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