Random regression model estimation of genetic parameters for show-jumping results of Hungarian Sporthorses.
Abstract: The aim of this study was to estimate the genetic parameters for show-jumping competition performance of Hungarian Sporthorses using a random regression model. There were 21,210 records from 739 horses collected in Hungary between 1996 and 2004. Performance was expressed as shifted Blom normalized ranks and as the difference between fence height and fault points. The random regression model (RRM) included fixed effects for sex, year, location, and obstacle height and random effects for animal, rider and permanent environment. Regressions for the random effects in the RRM were modelled with Legendre polynomials from first to fifth order of fit. The model focused on performance of horses from 4 to 11 years of age, with heterogeneous residual variances considered. The heritabilities were low to moderate for both variables. Genetic and phenotypic correlations between different ages decreased with increasing distance between the ages.
Publication Date: 2010-07-22 PubMed ID: 20646115DOI: 10.1111/j.1439-0388.2009.00848.xGoogle Scholar: Lookup
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- Journal Article
Summary
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The research article focuses on the analysis of the genetic factors impacting the show-jumping performance of Hungarian Sporthorses using a statistical tool known as a random regression model.
Objective of the Study
- The research was primarily undertaken to estimate and analyze the genetic parameters that affect the show-jumping results of Hungarian Sporthorses. The study utilised a statistical method called a random regression model (RRM), with the selected sample consisting of 21,210 records gathered from 739 horses in Hungary during 1996 to 2004.
Methodology Used
- Performance was quantified in two ways – one was through shifted Blom normalized ranks and the other was by considering the difference between the fence height and the fault points.
- The RRM constituted fixed effects covering aspects such as the gender of the horse, the year, location, and the height of the obstacle. Furthermore, random effects included in the model were the animal, rider, and the permanent environment.
- Regressions for these random effects within the RRM were modelled using Legendre polynomials from first to fifth order of fit.
- The research concentrated on analysing the performance of horses in the age range of 4 to 11 years. It was found that residual variances were heterogeneous in nature.
Findings of the Study
- The outcome of the study suggested that the heritabilities, i.e., the ability of a trait to be inherited from a parent to its offspring, were low to moderate for both variables – shifted Blom normalized ranks and the difference between fence height and fault points.
- The research also revealed that both genetic and phenotypic correlations between different ages decreased with an increasing gap in ages.
This implies that the inherited traits and observed characteristics of sporthorses varied more as the age difference increased, indicating a change in these genetic and phenotypic factors with the advancement in the age of a horse.
Cite This Article
APA
Posta J, Malovhr S, Mihók S, Komlósi I.
(2010).
Random regression model estimation of genetic parameters for show-jumping results of Hungarian Sporthorses.
J Anim Breed Genet, 127(4), 280-288.
https://doi.org/10.1111/j.1439-0388.2009.00848.x Publication
Researcher Affiliations
- Institute of Animal Science, University of Debrecen, Debrecen, Hungary. postaj@agr.unideb.hu
MeSH Terms
- Analysis of Variance
- Animals
- Female
- Horses / genetics
- Horses / physiology
- Hungary
- Male
- Models, Genetic
- Motor Activity / genetics
- Motor Activity / physiology
- Phenotype
- Physical Conditioning, Animal / physiology
- Regression Analysis
- Stochastic Processes
Citations
This article has been cited 1 times.- Chapard L, Van Thillo A, Meyermans R, Gorssen W, Buys N, Janssens S. Adjusted fence height: an improved phenotype for the genetic evaluation of show jumping performance in Warmblood horses. Genet Sel Evol 2023 Feb 23;55(1):12.
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