bolt-lmm - 2.4.1+dfsg-2 main

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).
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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.
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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.