bolt-lmm 2.3.2+dfsg-2build1 source package in Ubuntu

Changelog

bolt-lmm (2.3.2+dfsg-2build1) cosmic; urgency=medium

  * No-change rebuild for boost soname change.

 -- Matthias Klose <email address hidden>  Tue, 17 Jul 2018 12:53:36 +0000

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Uploaded by:
Matthias Klose on 2018-07-17
Uploaded to:
Cosmic
Original maintainer:
Debian Med
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Cosmic release on 2018-07-17 universe misc

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bolt-lmm_2.3.2+dfsg.orig.tar.xz 2.7 MiB b35f5019a80bab01ec7f57798941c024e5052fa8da6c2d8a7204cf9ebc06c86d
bolt-lmm_2.3.2+dfsg-2build1.debian.tar.xz 7.9 KiB 366d31e22f42cc5be384ff825420b23c690e2240a532651cc750337860301363
bolt-lmm_2.3.2+dfsg-2build1.dsc 2.2 KiB 7245eb20a8168a22eaee401fee9e22db31477a7ffeca1801e47d5ddf738c2055

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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: debug symbols for bolt-lmm
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.