Genomic Selection (GS) is currently a vere potential tool to increase genetic gains from breeding programs. In this workshop, statistical methodology to implement analysis for GS will be presented, this will include aspects such as: 1) pre-processing of phenotypic field data, 2) pre-precessing of molecular (i.e. SNP) data, 3) preparind data (e.g. GenoMatrix) and fitting GS models in R using packages such as BGLR and ASReml-R, 4) performing validation and cross-validation of fitted models to compare and evaluate the performance of GS for a given trait, 5) report and implement GS outcomes for scientists and managers. In addition, in this workshop further discussion of the use of GS methods and its implementation in plant breeding programs will de discussed in order to facilitate its operational use. This will include: assesing where in a program genetic gains are achieved, and re-defining testing and model fitting to maximize benefits of genomic selection. Some of the GS methods presented will be: BayesB, RKHS, and GBLUP. In this workshop you will learn the basics od data and software commads to fit GS models, and in interpretation and implementation of GS models.