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首页> 外文期刊>Livestock Science >Genetic parameters of new comprehensive performance traits for dressage and show jumping competitions performance of German riding horses
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Genetic parameters of new comprehensive performance traits for dressage and show jumping competitions performance of German riding horses

机译：德国骑行马展示展示新综合性能特征的遗传参数

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摘要

The aim of this study was to optimize modelling of the highest performance in dressage and show jumping competitions for enabling its use in the genetic evaluation of German riding horses. Genetic parameters were estimated using 4,582,191 observations from 221,132 horses in dressage and 9,997,439 observations from 253,945 horses in show jumping recorded between 1995 and 2014 in Germany. The highest performance was defined as the highest level achieved in competition and graduated by the level of difficulty. To investigate the effect of alternative modelling on genetic parameters for performance and on distributions and stability of estimated breeding values (EBV), eight different statistical models were set up. In these models, the following fixed effects were considered separately and in combinations in addition to the random additive genetic effect: gender, age at the highest performance or maximum age in the competition data, birth year, and year of the highest performance. Models were compared based on Akaike's information criterion (AIC), genetic parameters, and correlations between EBV from full and reduced data. Heritability estimates ranged from 0.21 to 0.24 for dressage and from 0.28 to 0.36 for show jumping. The two best fitted models included year of the highest performance and age at the highest performance or maximum age. EBV for the highest level achieved in dressage and show jumping competitions appear to be valuable in enhancing genetic progress for sport-related breeding goals of German riding horses.
机译：本研究的目的是优化盛装舞步最高性能的建模，并显示跳跃竞争，以使其在德国骑马的遗传评价中使用。估计遗传参数，使用221,132匹马的观察结果和9,997,439匹马，从253,945匹马的观察结果显示在德国1995年至2014年之间。最高性能被定义为竞争中实现的最高水平，并逐渐逐渐逐渐达到困难程度。为了探讨替代建模对遗传参数对估计育种值（EBV）的分布和稳定性的影响，建立了八种不同的统计模型。在这些模型中，除了随机添加剂遗传效应之外，还在组合中分别进行了以下固定效果：性别，年龄在竞争数据的最高性能或最高年龄，诞生年份和最高绩效的年龄。基于Akaike的信息标准（AIC），遗传参数和EBV之间的相关性比较模型。遗传性估计范围为0.21至0.24，用于跳跃0.28至0.36。两个最佳拟合模型包括最高性能和最高性能或最高年龄的年龄。 EBV在盛装舞步和展示跳跃比赛中获得的最高水平似乎对提高德国骑马的体育相关目标的遗传进展似乎有价值。

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