BOLT-LMM

The BOLT-LMM algorithm computes statistics for testing association between phenotype and genotypes using a linear mixed model (LMM) [1]. 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 (e.g., EMMAX, FaST-LMM, GEMMA, GRAMMAR-Gamma, GCTA-LOCO), 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.

homepage: https://storage.googleapis.com/broad-alkesgroup-public/BOLT-LMM/BOLT-LMM_manual.html

version	toolchain
2.4.1	iimkl/2024a

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