Evaluating Genomic Selection in beef cattle: Insights from computer simulations using real SNP data

Genomic Selection (GS) is a method that employs genomic data to estimate breeding values and rank candidates for selection. Despite its numerous advantages, its application in cattle breeding programs remains in the early stages in many livestock systems developed in tropical and subtropical environments, such as those in Paraguay. Computational simulations are powerful tools that enhance our understanding of GS applications in different scenarios and are invaluable as an initial step before implementing this technique in "real" genetic improvement programs. In this study, real data from single nucleotide polymorphisms (SNPs) of the Indicus and Taurus breeds were employed to simulate three crossing schemes: F1 crosses, grading up, and rotational crosses. Phenotypes were selected for traits related to shear force, growth, and tolerance. The predictive accuracy of three 50k SNP chips, differing in their SNP selection methodologies, was compared: random selection, selection based on minimum allele frequency differences between breeds, and selection based on minimum allele frequency differences between breeds with a threshold of 0.09 in Taurus. The findings indicate that rotational crossing demonstrates optimal predictive accuracy (0.38), while marker selection based on allele frequency differences between breeds (0.18 and 0.17, respectively) does not benefit predictions significantly.