{"authors":[{"id":"4b4937a2-c8d9-4d2e-982e-c2eea3d119fb","name":"Loh Po-Ru","role":[]}],"components":[{"id":"root","name":"root","payload":{"cid":"bafybeievc3kjymvxg4ns3jlhdy3souzyxd4cc65edelaikhbtbn7kwgamu","path":"root"},"type":{".pdf":"pdf"}},{"id":"cb538873-4682-4a7f-9db1-5265c6bbad7b","name":"BOLT-LMM_v1.0_manual.pdf","type":"pdf","payload":{"cid":"bafkreiheemo45c4seomw42fbzf4t3h6ty76gohyrrhlxewlyc6ck3ftpde","path":"root/BOLT-LMM_v1.0_manual.pdf","title":"Manuscript"},"starred":true,"subtype":"manuscript"}],"defaultLicense":"CC BY","researchFields":[],"title":"BOLT-LMM v1.0 User Manual","version":"desci-nodes-0.2.0","references":[{"id":"https://doi.org/10.1101/007799","type":"doi","url":"https://doi.org/10.1101/007799","authors":[{"name":"P.-R Loh"}],"title":"Efficient bayesian mixed model analysis increases association power in large cohorts","journal":"bioRxiv","volume":"","pages":"","issue":"","year":"2014"},{"id":"https://doi.org/10.1038/nmeth.1681","type":"doi","url":"https://doi.org/10.1038/nmeth.1681","authors":[{"name":"C Lippert"}],"title":"FaST linear mixed models for genome-wide association studies","journal":"Nature Methods","volume":"8","pages":"","issue":"","year":"2011"},{"id":"https://doi.org/10.1038/nmeth.2037","type":"doi","url":"https://doi.org/10.1038/nmeth.2037","authors":[{"name":"J Listgarten"}],"title":"Improved linear mixed models for genome-wide association studies","journal":"Nature Methods","volume":"9","pages":"","issue":"","year":"2012"},{"id":"https://doi.org/10.1038/ng.2620","type":"doi","url":"https://doi.org/10.1038/ng.2620","authors":[{"name":"J Listgarten"},{"name":"C Lippert"},{"name":"D Heckerman"}],"title":"FaST-LMM-Select for addressing confounding from spatial structure and rare variants","journal":"Nature Genetics","volume":"45","pages":"","issue":"","year":"2013"},{"id":"https://doi.org/10.1038/srep01815","type":"doi","url":"https://doi.org/10.1038/srep01815","authors":[{"name":"C Lippert"}],"title":"The benefits of selecting phenotype-specific variants for applications of mixed models in genomics","journal":"Scientific Reports","volume":"3","pages":"","issue":"","year":"2013"},{"id":"https://doi.org/10.1038/ng.2310","type":"doi","url":"https://doi.org/10.1038/ng.2310","authors":[{"name":"X Zhou"},{"name":"M Stephens"}],"title":"Genome-wide efficient mixed-model analysis for association studies","journal":"Nature Genetics","volume":"44","pages":"","issue":"","year":"2012"},{"id":"https://doi.org/10.1038/ng.2410","type":"doi","url":"https://doi.org/10.1038/ng.2410","authors":[{"name":"G R Svishcheva"},{"name":"T I Axenovich"},{"name":"N M Belonogova"},{"name":"C M Van Duijn"},{"name":"Y S Aulchenko"}],"title":"Rapid variance components-based method for whole-genome association analysis","journal":"Nature Genetics","volume":"","pages":"","issue":"","year":"2012"},{"id":"https://doi.org/10.1038/ng.2876","type":"doi","url":"https://doi.org/10.1038/ng.2876","authors":[{"name":"J Yang"},{"name":"N A Zaitlen"},{"name":"M E Goddard"},{"name":"P M Visscher"},{"name":"A L Price"}],"title":"Advantages and pitfalls in the application of mixed-model association methods","journal":"Nature Genetics","volume":"46","pages":"","issue":"","year":"2014"},{"id":"https://doi.org/10.1086/519795","type":"doi","url":"https://doi.org/10.1086/519795","authors":[{"name":"S Purcell"}],"title":"PLINK: a tool set for whole-genome association and population-based linkage analyses","journal":"The American Journal of Human Genetics","volume":"81","pages":"","issue":"","year":"2007"}],"description":"The BOLT-LMM software package computes statistics for 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 existing mixed model association methods (e.g., EMMAX [2], FaST-LMM [3-6], GEMMA [7], GRAMMAR-Gamma [8], GCTA-LOCO [9]), providing an opportunity for increased power to detect associations while controlling false positives. Additionally, BOLT-LMM applies algorithmic advances to compute association statistics much faster than existing methods, both when using the Bayesian mixture model and when specialized to standard mixed model association."}