Ubuntu
bolt-lmm package

bolt-lmm 2.3.6+dfsg-1 source package in Ubuntu

Changelog

bolt-lmm (2.3.6+dfsg-1) unstable; urgency=medium

  * New upstream version

 -- Dylan Aïssi <email address hidden>  Fri, 19 Nov 2021 16:44:39 +0100

Upload details

Uploaded by:
Debian Med
Uploaded to:
Sid
Original maintainer:
Debian Med
Architectures:
amd64 i386 ppc64el all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Jammy release universe misc

Builds

Jammy: [FULLYBUILT] amd64 [FULLYBUILT] ppc64el

Downloads

File Size SHA-256 Checksum
bolt-lmm_2.3.6+dfsg-1.dsc 2.2 KiB f1c054f30d224f925d53b183fa215a05742b76f9b8ea469abccf1dfc8f33f6d1
bolt-lmm_2.3.6+dfsg.orig.tar.xz 2.7 MiB 689f841b0b72bc593c0eb812e39d2866576d1018486639a8a9ec2128527f7a19
bolt-lmm_2.3.6+dfsg-1.debian.tar.xz 8.3 KiB fc2e4e2c3c58af714da775ad9e3435d8951c31b8b9a0d201e1acbca65cf432ac

Available diffs

No changes file available.

Binary packages built by this source

bolt-lmm: Efficient large cohorts genome-wide Bayesian mixed-model association testing

 The BOLT-LMM software package currently consists of two main algorithms, the
 BOLT-LMM algorithm for mixed model association testing, and the BOLT-REML
 algorithm for variance components analysis (i.e., partitioning of
 SNP-heritability and estimation of genetic correlations).
 .
 The BOLT-LMM algorithm computes statistics for testing association between
 phenotype and genotypes using a linear mixed model. By default, BOLT-LMM
 assumes a Bayesian mixture-of-normals prior for the random effect attributed
 to SNPs other than the one being tested. This model generalizes the standard
 infinitesimal mixed model used by previous mixed model association methods,
 providing an opportunity for increased power to detect associations while
 controlling false positives. Additionally, BOLT-LMM applies algorithmic
 advances to compute mixed model association statistics much faster than
 eigendecomposition-based methods, both when using the Bayesian mixture model
 and when specialized to standard mixed model association.
 .
 The BOLT-REML algorithm estimates heritability explained by genotyped SNPs and
 genetic correlations among multiple traits measured on the same set of
 individuals. BOLT-REML applies variance components analysis to perform these
 tasks, supporting both multi-component modeling to partition SNP-heritability
 and multi-trait modeling to estimate correlations. BOLT-REML applies a Monte
 Carlo algorithm that is much faster than eigendecomposition-based methods for
 variance components analysis at large sample sizes.

bolt-lmm-dbgsym: No summary available for bolt-lmm-dbgsym in ubuntu kinetic.

No description available for bolt-lmm-dbgsym in ubuntu kinetic.

bolt-lmm-example: Examples for bolt-lmm

 The BOLT-LMM software package currently consists of two main algorithms, the
 BOLT-LMM algorithm for mixed model association testing, and the BOLT-REML
 algorithm for variance components analysis (i.e., partitioning of
 SNP-heritability and estimation of genetic correlations).
 .
 This package provides some example data for bolt-lmm.