Artifact bolt-lmm_2.4.1+dfsg-2_amd64

Metadata
deb_control_files:
- control
- md5sums
deb_fields:
  Architecture: amd64
  Depends: libboost-iostreams1.83.0 (>= 1.83.0), libboost-program-options1.83.0 (>=
    1.83.0), libc6 (>= 2.34), libgcc-s1 (>= 3.0), libgomp1 (>= 4.9), libnlopt0 (>=
    2.6.1), libopenblas0, libstdc++6 (>= 13.1), libzstd1 (>= 1.5.5), zlib1g (>= 1:1.1.4)
  Description: |-
    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.
  Homepage: https://data.broadinstitute.org/alkesgroup/BOLT-LMM/
  Installed-Size: '968'
  Maintainer: Debian Med Packaging Team <debian-med-packaging@lists.alioth.debian.org>
  Package: bolt-lmm
  Priority: optional
  Section: science
  Suggests: bolt-lmm-doc
  Version: 2.4.1+dfsg-2
srcpkg_name: bolt-lmm
srcpkg_version: 2.4.1+dfsg-2

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