Comparison of Some Random Regression Models for Racing Performances of the British Racing Horses in Turkey

Yıl 2019, Cilt: 2 Sayı: 1, 6 - 9, 01.01.2019

Samet Hasan Abacı , Ümit Coşkun Hasan Önder

Öz

This study was conducted to compare some random regression models applied to Legendre polynomials (L (2.2), L (2.3), L (3.2), L (3.3)) for that run on racing performance in British horses in Turkey. For this purpose, a total of 146850 race time record up to 15 races at different distances of 13625 horse taken from the Jockey Club of Turkey between the years of 2005 and 2016 was used. In this study, the genetic correlations between covariance components, heritabilities and race days for race completion times were estimated by using the DXMRR option in the DFREML statistical package program. The track type, the year and the horse's age, the fixed effect, the track distance were taken as covariates and the breeding value estimates were made. -2logL, Akaike information criterion (AIC), Bayesian information criterion (BIC), Error Variance (RV) and Log likelihood values were used to compare models. Heritabilities (0.24 to 0.28), additive genetic correlations (0.87 to 0.99) and phenotypic correlations (0.22 to 0.55) were estimated by L (2.3) random regression model which had the lowest -2LogL and BIC values. As a result, the L (2.3) model can be used for genetic evaluation and breeding of British racing horses.

Anahtar Kelimeler

British horse, Race, Heritabilities, Additive genetic correlations

Kaynakça

• Alkan S, Karslı T, Galiç A, Karabağ K, Balcıoğlu MS. 2012. Estimation of Genetic Parameters for Body Weight of Japanese Quails (Coturnix coturnix japonica) Using Random Regression Model. Kafkas Univ. Vet. Fak. Derg, 18 (6): 985-939.
• Anonymous, 2018. https://www.at.gen.tr/ingiliz-ati.html (date of access: 06.08.2018)
• Buxadera AM, da Mota MDS. 2008. Variance component estimations for race performance of thoroughbred horses in Brazil by random regression model. Livestock Science, 117 (2-3): 298-307.
• Cansabuncu Kanman, G. 2006. The relationship between the quantity, counting and measurement of erytrocytes and the performance of horses during competition season. Master Thesis, Ege University, Institute of Science, İzmir.
• Gómez MD, Molina A, Menendez-Buxadera A, Valera M. 2011. Estimation of genetic parameters for the annual earnings at different race distances in young and adult Trotter Horses using a Random Regression Model. Livestock Science, 137 (1-3): 87-94.
• Köseman A, Şeker İ. 2016. Biosecurity and its importance in the Horse Farms, Journal of Bahri Dagdas Animal Research, 5 (1): 33-39.
• Köseman A, Şeker, İ. 2018. Atların yarış ve yarışma performansları üzerine etkili faktörler ve performansı artırma yolları. Uludag University Journal of the Faculty of Veterinary Medicine, 37 (1): 38-41.
• Meyer K. 1997. DFREML 3.0α program package and user notes. Genetics and Breeding Unit, Univ. New England, Armidale, New South Wales, Australia.
• Onder H, Sen U, Takma C, Ocak S, Abaci SH. 2015. Genetic parameter estimates for growth traits in Saanen kids. Kafkas Univ Vet Fak Derg,, 21 (6): 799-804.
• Takma C, Akbas Y. 2009. Comparison of fitting performance of random regression models to test day milk yields in Holstein Friesians. Kafkas Univ Vet Fak Derg, 15 (2): 261-266.