The use of a random regression model to account for change in racing speed of German trotters with increasing age.
Abstract: 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.
Publication Date: 2006-08-03 PubMed ID: 16882090DOI: 10.1111/j.1439-0388.2006.00596.xGoogle Scholar: Lookup
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- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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The researchers conducted a study to understand changes in racing speed of German trotters as they age using a random regression model. They analyzed the trait of racing time per kilometer across six age classes for trotters and found the model with the highest fit included several fixed variables including the drive’s effects and the racetrack condition. Overall, their findings suggest that using a random regression model can improve the accuracy of trotter selection across age classes.
Objective of the Research
- The study aimed to examine the change in racing speed of German trotters as they grow older.
- The researchers chose a genetic analysis approach to study the performance trait of racing time per kilometer.
- Another goal was to determine if the use of a random regression model could yield a more precise selection of trotters over different age classes.
Methodology
- The researchers classified German trotters into six separate age classes: 2-, 3-, 4-, 5-, 6-year-old, and older.
- The category of 3-year-old trotters was further divided into two seasons based on their birth months.
- The team used a random regression model where sex, race track, the condition of the race track, race distance, and drivers were treated as fixed effects.
- The model considered a first-order polynomial for each driver and a second-order polynomial for the condition of the racetrack.
- Different elements like the year and season were treated independently of age.
- The database used included performance data from 2,373 trotters and pedigree data from 9,952 horses across four generations.
Findings
- The study found that with increasing age classes from 2- to 4-year-old trotters, heritabilities for racing time per km increased from 0.01 to 0.18, they then slightly decreased for 5-year-old trotters and dropped significantly for 6-year-old trotters.
- The genetic correlations of racing time per kilometer among the six age classes were very high (rg = 0.82-0.99).
- The use of a repeatability animal model for racing times considering the six age classes as a fixed effect showed a heritability of h2 = 0.13.
- When ranking trotters using a repeatability analysis within age classes, the rankings differed slightly from those obtained using random regression analysis.
- However, using random regression analysis, almost no reranking of trotters over time took place.
Conclusion
- The research concluded that the use of a random regression model could potentially enhance the precision of selecting trotters over various age classes.
- This method can be beneficial in identifying which trotters will maintain their performance levels as they age.
Cite This Article
APA
Bugislaus AE, Roehe R, Willms F, Kalm E.
(2006).
The use of a random regression model to account for change in racing speed of German trotters with increasing age.
J Anim Breed Genet, 123(4), 239-246.
https://doi.org/10.1111/j.1439-0388.2006.00596.x Publication
Researcher Affiliations
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University of Kiel, Kiel, Germany. antke-elsabe.bugislaus@uni-rostock.de
MeSH Terms
- Aging / physiology
- Animals
- Breeding / economics
- Breeding / methods
- Female
- Gait / physiology
- Horses / genetics
- Horses / physiology
- Logistic Models
- Male
- Time Factors
Citations
This article has been cited 1 times.- Takahashi T. The effect of age on the racing speed of Thoroughbred racehorses.. J Equine Sci 2015;26(2):43-8.
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