lfmm: Latent Factor Mixed Models

Fast and accurate inference of gene-environment associations (GEA) in genome-wide studies (Caye et al., 2019, <doi:10.1093/molbev/msz008>). We developed a least-squares estimation approach for confounder and effect sizes estimation that provides a unique framework for several categories of genomic data, not restricted to genotypes. The speed of the new algorithm is several times faster than the existing GEA approaches, then our previous version of the 'LFMM' program present in the 'LEA' package (Frichot and Francois, 2015, <doi:10.1111/2041-210X.12382>).

Package details

AuthorBasile Jumentier [aut, cre], Kevin Caye [ctb], Olivier Fran├žois [ctb]
MaintainerBasile Jumentier <basile.jumentier@gmail.com>
Package repositoryView on CRAN
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lfmm documentation built on June 30, 2021, 5:07 p.m.