A genomic prediction model for racecourse starts in the Thoroughbred horse. (1st July 2019)

• Record Type: Journal Article
• Authors: McGivney, B. A.; Hernandez, B.; Katz, L. M.; MacHugh, D. E.; McGovern, S. P.; Parnell, A. C.; Wiencko, H. L.; Hill, E. W.
• Is Part Of: Animal genetics. Volume 50:Number 4(2019)
• Page Start: 347
• Page End: 357
• Publication Date: 2019-07-01
• Subjects: behaviour -- durability -- equine -- genome‐wide association study -- performance -- random forest model -- temperament; Animal genetics -- Periodicals; 572.8
• DOI: 10.1111/age.12798
• Languages: English
• ISSNs: 0268-9146

Abstract

Summary: Durability traits in Thoroughbred horses are heritable, economically valuable and may affect horse welfare. The aims of this study were to test the hypotheses that (i) durability traits are heritable and (ii) genetic data may be used to predict a horse's potential to have a racecourse start. Heritability for the phenotype 'number of 2‐ and 3‐year‐old starts' was estimated to be h m 2 = 0.11 ± 0.02 ( n = 4499). A genome‐wide association study identified SNP contributions to the trait. The neurotrimin ( NTM ), opioid‐binding protein/cell adhesion molecule like ( OPCML ) and prolylcarboxypeptidase ( PRCP ) genes were identified as candidate genes associated with the trait. NTM functions in brain development and has been shown to have been selected during the domestication of the horse. PRCP is an established expression quantitative trait locus involved in the interaction between voluntary exercise and body composition in mice. We hypothesise that variation at these loci contributes to the motivation of the horse to exercise, which may influence its response to the demands of the training and racing environment. A random forest with mixed effects (RFME) model identified a set of SNPs that contributed to 24.7% of the heritable variation in the trait. In an independent validation set ( n = 528 horses), the cohort with high genetic potential for a racecourse start had significantly fewer unraced horses (16% unraced) than did low (27% unraced) potential horses and had more favourable race outcomes among those that raced. Therefore, the information from SNPs included in the model may be used to predict horses with a greater chance of a racecourse start.