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. 2021 Jun 18:12:680569.
doi: 10.3389/fgene.2021.680569. eCollection 2021.

lme4GS: An R-Package for Genomic Selection

Diana Caamal-Pat  1 , Paulino Pérez-Rodríguez  1 , José Crossa  1   2 , Ciro Velasco-Cruz  1 , Sergio Pérez-Elizalde  1 , Mario Vázquez-Peña  3
Affiliations

lme4GS: An R-Package for Genomic Selection

Diana Caamal-Pat et al. Front Genet. .

Abstract

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 GS, such as in linear mixed models (LMMs). An active area of research is the development of software for fitting LMMs mainly used to make genome-based predictions. The lme4 is the standard package for fitting linear and generalized LMMs 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 LMMs 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 GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data.

Keywords: genomic prediction; genomic selection; kernel; linear mixed model; lme4.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Observed vs. predicted phenotypic values in the training and testing sets.
FIGURE 2
FIGURE 2
The values of the bandwidth parameter vs. the log-likelihood. (A) Gaussian kernel. (B) Exponential kernel.
See this image and copyright information in PMC

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