Integrating Performance Records and Genetic Evaluations in Spanish Horse Populations Competing in Olympic Disciplines

This study evaluates performance data and genetic merit of the main horse populations competing in Olympic disciplines in Spain and examines their implications for the optimization of official Breeding Programs. Performance records from 2004-2023 were analyzed, including 101,093 participations in Dressage, 319,000 in Show Jumping, and 17,535 in Eventing. These records were combined with pedigree information from 35,589 horses in Dressage, 33,935 in Show Jumping, and 12,102 in Eventing and evaluated using BLUP animal models to obtain standardized Estimated Breeding Values (EBV; mean 100 ± 20) and a Genetic Global Index (GGI). A single unified evaluation model was implemented for all studbooks, enabling a direct comparison of genetic quality across different breeds. Results revealed marked differences in genetic merit and genetic progress among breeds. Similar mean EBVs were obtained for the three analyzed breeds in Dressage in both the complete and the top 10% populations, with positive genetic trends in Caballo de Deporte Español (CDE) and Pura Raza Española (PRE), while the slope of EBV on birth year was not significantly different from zero in Spanish Anglo-Árabe (AA). CDE showed the highest mean EBVs and accuracies in Show Jumping (EBV up to 109.27; R up to 0.72), with a clear positive genetic trend. In Eventing, CDE and AA showed similar EBVs, while PRE consistently exhibited lower ones, although with a comparatively more favorable genetic trend. Analysis of selection intensity indicated that PRE breeders applied the most consistent genetic criteria, preferentially using animals with GGI > 100, whereas CDE and AA showed discrepancies between genetic merit and reproductive use. Overall, the unified Spanish genetic evaluation system provides reliable comparative information across breeds and has enabled measurable genetic progress, although improvements in breeders' decision-making and in the use of genetic information are needed to maximize selection response.