The global climate is changing, resulting in significant economic losses worldwide. It is thus necessary to speed up the plant selection process, especially for complex traits such as biotic and abiotic stresses. Nowadays, genomic selection (GS) is paving new ways to boost plant breeding, facilitating the rapid selection of superior genotypes based on the genomic estimated breeding value (GEBV). GEBVs consider all markers positioned throughout the genome, including those with minor effects. Indeed, although the effect of each marker may be very small, a large number of genome-wide markers retrieved by high-throughput genotyping (HTG) systems (mainly genotyping-by-sequencing, GBS) have the potential to explain all the genetic variance for a particular trait under selection. Although several workflows for GBS and GS data have been described, it is still hard for researchers without a bioinformatics background to carry out these analyses. This chapter has outlined some of the recently available bioinformatics resources that enable researchers to establish GBS applications for GS analysis in laboratories. Moreover, we provide useful scripts that could be used for this purpose and a description of key factors that need to be considered in these approaches.
Keywords: Genomic estimated breeding values (GEBVs); Machine learning; Next-generation breeding; Single-nucleotide polymorphisms (SNPs); Stacks; rrBLUP.