lme4GS: An R-Package for Genomic Selection

Frontiers in Genetics

José Crossa¹  Mario Vázquez-Peña²  Paulino Pérez-Rodríguez³  Sergio Pérez-Elizalde³  Diana Caamal-Pat³  Ciro Velasco-Cruz³ 

[1] Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico;Department of Irrigation, Universidad Autónoma Chapingo, Texcoco, Mexico;Department of Socioeconomics, Statistics, and Informatics, Colegio de Postgraduados, Texcoco, Mexico;

关键词: genomic selection;    genomic prediction;    linear mixed model;    lme4;    kernel;   

DOI  :  10.3389/fgene.2021.680569

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown