gemma 0.98.5+dfsg-2 source package in Ubuntu
Changelog
gemma (0.98.5+dfsg-2) unstable; urgency=medium * Team upload. * Fix watch file * Standards-Version: 4.6.1 (routine-update) -- Andreas Tille <email address hidden> Fri, 18 Nov 2022 12:07:39 +0100
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 | Published | Component | Section | |
---|---|---|---|---|
Oracular | release | universe | misc | |
Noble | release | universe | misc | |
Mantic | release | universe | misc | |
Lunar | release | universe | misc |
Downloads
File | Size | SHA-256 Checksum |
---|---|---|
gemma_0.98.5+dfsg-2.dsc | 2.1 KiB | 2924f2e8f02cf891e74cf1d22c0509da3948e50180230450a3d51cba29cb90c3 |
gemma_0.98.5+dfsg.orig.tar.xz | 42.1 MiB | 6a9741ad53d2c581d0fb850de5807ca61fb3449f1d198bbab78291487187619a |
gemma_0.98.5+dfsg-2.debian.tar.xz | 6.5 KiB | a8eaa04514945343f5e6416a178379036a12c43b8ee1ab46c780bc0f114b68a5 |
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.