lme4GS: An R-Package for Genomic Selection - Caamal-Pat, Diana; Pérez-Rodríguez, Paulino; Crossa, José; Velasco-Cruz, Ciro; Pérez-Elizalde, Sergio; Vázquez-Peña, Mario (Frontiers in Genetics, 2021)
Genomic selection (GS) is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in genomic selection, for example, linear mixed models (LMM). An active area of research is the development of software for fitting linear mixed models mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized linear mixed models in the R package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. This article describes the new lme4GS package for R, which is focused on fitting linear mixed models with covariance structures defined by the user, bandwidth selection and genomic prediction. The new package is focused on genomic prediction of the models used in genomic selection and can fit linear mixed models using different variance-covariance matrices. Several examples of genomic selection models are presented using this package as well as the analysis using real data.