The measurement of morphometric traits in horses is important for determining breed qualification and is one of the main selection criteria for the species. The development of an index (HPC) that consists of principal components weighted by additive genetic values allows to explore the most relevant relationships using a reduced number of variables that explain the greatest amount of variation in the data. Genome-wide association studies (GWAS) using HPC are a relatively new approach that permits to identify regions related to a set of traits. The aim of this study was to perform GWAS using HPC for 15 linear measurements as the explanatory variable in order to identify associated genomic regions and to elucidate the biological mechanisms linked to this index in Campolina horses. For GWAS, weighted single-step GBLUP was applied to HPC. The eight genomic windows that explained the highest proportion of additive genetic variance were identified. The sum of the additive variance explained by the eight windows was 95.89%. Genes involved in bone and cartilage development were identified (SPRY2, COL9A2, MIR30C, HEYL, BMP8B, LTBP1, FAM98A, and CRIM1). They represent potential positional candidates for the HPC of the linear measurements evaluated. The HPC is an efficient alternative to reduce the 15 usually measured traits in Campolina horses. Moreover, candidate genes inserted in region that explained high additive variance of the HPC were identified and might be fine-mapped for searching putative mutation/markers.
Keywords: Campolina; Conformation; SNP; linear measurements; morphometry; wither height.