The use of a random regression model to account for change in racing speed of German trotters with increasing age.
In a genetic analysis of German trotters, the performance trait racing time per km was analysed by using a random regression model on six different age classes (2-, 3-, 4-, 5- and 6-year-old and older trotters; the age class of 3-year-old trotters was additionally divided by birth months of horses into two seasons). The best-fitting random regression model for the trait racing time per km on six age classes included as fixed effects sex, race track, condition of race track (fitted as second-order polynomial on age), distance of race and each driver (fitted as first-order polynomial on age) as well as the year-season (fitted independent of age). The random additive genetic and permanent environmental effects were fitted as second-order polynomials on age. Data consisted of 138,620 performance observations from 2,373 trotters and the pedigree data contained 9,952 horses from a four-generation pedigree. Heritabilities for racing time per km increased from 0.01 to 0.18 at age classes from 2- to 4-year-old trotters, then slightly decreased for 5 year and substantially decreased for 6-year-old horses. Genetic correlations of racing time per km among the six age classes were very high (rg = 0.82-0.99). Heritability was h2 = 0.13 when using a repeatability animal model for racing time per km considering the six age classes as fixed effect. Breeding values using repeatability analysis over all and within age classes resulted in slightly different ranking of trotters than those using random regression analysis. When using random regression analysis almost no reranking of trotters over time took place. Generally, the analyses showed that using a random regression model improved the accuracy of selection of trotters over age classes.
Bugislaus
A-E
AE
Institute of Animal Breeding and Husbandry, Christian-Albrechts-University of Kiel, Kiel, Germany. antke-elsabe.bugislaus@uni-rostock.de
Roehe
R
R
Willms
F
F
Kalm
E
E
D000375:Aging / Q000502:physiology*; D000818:Animals; D001947:Breeding / Q000191:economics / Q000379:methods; D005260:Female; D005684:Gait / Q000502:physiology*; D006736:Horses / Q000235:genetics* / Q000502:physiology*; D016015:Logistic Models; D008297:Male; D013997:Time Factors