Shape and joint angle data for seven European horse breeds and their repeatability.

The research provides a comprehensive set of data on the physical traits or landmarks of 1241 horses across seven European breeds. This data could help in the objective comparison between breeds for selection purposes and may further be used for breed comparison studies, heritability, and genome-wide association studies.

Background

• The paper emphasizes the importance of conformation traits in horses as these are fundamental in discerning and selecting between breeds. However, currently, these traits are often subjectively evaluated within a breed, making cross-breed comparisons difficult.
• The subjective method of evaluation has led to a need for objective methods to measure and compare horses of different breeds.

Methodology

• The researchers made use of a horse shape space model, which seizes the shape of a horse from a side photograph using 253 landmarks and semi-landmarks. This model serves as a tool to provide objective information on the form of a horse that can be used for comparing different breeds.
• The methodology involved gathering a full set of 253 landmark data for 1241 horses from seven different breeds. This comprehensive data is intended to offer an objective platform for comparison among these breeds while eliminating any potential subjectivity that can skew the results.
• The research also includes an R code file used to extract joint-angle information and transform the raw data into csv files. This transformed data can then be used for further analysis, such as breed comparisons, heritability, or genome-wide association studies, including either single or multi-breed studies.

Results and Conclusion

• The results were comprehensive, capturing a significant amount of data on horse physical traits, and were supported by a report on the repeatability of the joint angles.
• The dataset provided by the research serves as a valuable resource for understanding, comparing, and exploring horse breeds on a genomic level, with an emphasis on objectivity, repeatability, and scientific rigor.