Factor analysis of evaluated and linearly scored traits in Swedish Warmblood horses

Assessment protocols to describe the various aspects of conformation, gait and jumping traits on a linear scale were introduced at young horse tests for Swedish Warmblood horses in 2013. The traits scored on a linear scale are assumed to be less subjective and more easily compared across populations than the traditional evaluated traits that are scored relative to the breeding goal. However, the resulting number of traits is considerable, and several of the traits are correlated. The aim of this study was to investigate the interrelationship between the different evaluated and linearly scored traits in Swedish Warmbloods using factor analysis. In total, 20,935 horses born 1996-2017 had information on evaluated traits, and 5450 of these also had linearly scored trait records assessed since 2014 when the protocol was updated. A factor analysis with varimax rotation was performed separately for evaluated and linearly scored traits using the Psych package in R. Height at withers was included in both analyses. A total of four factors for evaluated traits and 14 factors for linearly scored traits were kept for further analysis. Missing values for individual traits in horses with linearly scored trait records were imputed based on correlated traits before factor scores were calculated using factor loadings. Genetic parameters for, and correlations between, the resulting underlying factors were estimated using multiple-trait animal models in the BLUPF90 package. Heritability estimates were on a similar level as for the traits currently used in the genetic evaluation, ranging from 0.05 for the factor for linearly scored traits named L.behaviour (dominated by traits related to behaviour) to 0.59 for the factor for evaluated traits named E.size (dominated by height at withers and conformation). For both types of traits, separate factors were formed for jumping and gait traits, as well as for body size. High genetic correlations were estimated between such corresponding factors for evaluated traits and factors for linearly scored traits. In conclusion, factor analysis could be used to reduce the number of traits to be included in multiple-trait genetic evaluation or in genomic analysis for warmblood horses. It can also contribute to a better understanding of the interrelationships among the assessed traits and be useful to decide on subgroups of traits to be used in several multiple-trait evaluations on groups of original traits.