Principal components for morphometric traits in Campolina horses.

Area of Science:

• Animal Genetics
• Quantitative Genetics
• Animal Breeding

Background:

• Campolina horses are a significant breed with diverse morphometric traits.
• Understanding genetic variability is crucial for effective breed improvement and selection strategies.
• Previous studies may not have comprehensively analyzed the interplay of multiple morphometric traits using advanced statistical methods.

Purpose of the Study:

• To evaluate genetic variability and relationships among 15 morphometric traits in Campolina horses.
• To develop a selection index based on an aggregate genotype reflecting key genetic variation.
• To propose an efficient tool for identifying superior animals for breeding programs.

Main Methods:

• Application of Principal Component Analysis (PCA) on the genetic (co)variance matrix of 15 morphometric traits.
• Estimation of breeding values and development of a selection index (HPC1) from the principal component explaining the most variance.
• Two-trait analysis to assess the correlation between the proposed index and withers height.

Main Results:

• The first principal component (PC1) explained 97.8% of the total additive genetic variance, primarily contrasting animals by body size.
• PC1 traits exhibited high heritabilities and strong positive genetic correlations.
• The developed index (HPC1) showed moderate to high positive genetic correlations with withers height (0.73-0.86), indicating its effectiveness in selection.

Conclusions:

• PCA is a powerful tool for dissecting genetic variation in complex traits like horse morphometry.
• The proposed index (HPC1) effectively integrates multiple body size traits, offering a robust alternative for selecting Campolina horses.
• HPC1 selection implicitly accounts for important traits like withers height, facilitating breed improvement.