Genomic association using principal components of morphometric traits in horses: identification of genes related to bone growth.

This research article investigates the genetic factors influencing bone growth in Campolina horses. The study employed genome-wide association studies (GWAS) using principal components derived from linear measurements to identify relevant genomic regions and understand the related biological mechanisms.

Study Methodology

• The research involved determining the morphometric traits in horses – an important factor for breed qualification. An index (HPC) was developed which consists of principal components weighted by additive genetic values. This was done to pinpoint the most crucial relationships using a minimal set of variables that account for the most data variation.
• The study used the method of genome-wide association studies (GWAS) using the HPC for 15 linear measurements as the explanatory variable.
• The study aimed to identify the genomic regions associated with the morphometric traits and reveal the biological mechanisms linked to this index in Campolina horses, a specific breed.
• The GWAS study involved a method referred to as the weighted single-step GBLUP (Genomic Best Linear Unbiased Predictor) applied to the HPC.

Study Findings

• Eight genomic windows were found that shared the highest percentage of additive genetic variance.
• The sum of the additive variance explained by these eight identified windows was 95.89%.
• Genes associated with bone and cartilage development, which significantly contribute to the horses’ morphometric traits, were identified.
• The HPC is an effective alternative to reduce the commonly measured 15 traits in Campolina horses.
• The study also found candidate genes in a region that explained a high additive variance of the HPC. These identified genes could be fine-mapped for searching possible mutation/markers that could be crucial in understanding the development of these horses.