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交叉验证分析在耐力赛马分级遗传评估模型中的应用。

Cross-validation analysis for genetic evaluation models for ranking in endurance horses.

机构信息

1Departamento de Producción Animal,Universidad Complutense de Madrid,Avda. Puerta de Hierro s/n,E-28040 Madrid,Spain.

2Unidad de Genética Cuantitativa y Mejora Animal,Universidad de Zaragoza,E-50013 Zaragoza,Spain.

出版信息

Animal. 2018 Jan;12(1):20-27. doi: 10.1017/S1751731117001331. Epub 2017 Jun 21.

Abstract

Ranking trait was used as a selection criterion for competition horses to estimate racing performance. In the literature the most common approaches to estimate breeding values are the linear or threshold statistical models. However, recent studies have shown that a Thurstonian approach was able to fix the race effect (competitive level of the horses that participate in the same race), thus suggesting a better prediction accuracy of breeding values for ranking trait. The aim of this study was to compare the predictability of linear, threshold and Thurstonian approaches for genetic evaluation of ranking in endurance horses. For this purpose, eight genetic models were used for each approach with different combinations of random effects: rider, rider-horse interaction and environmental permanent effect. All genetic models included gender, age and race as systematic effects. The database that was used contained 4065 ranking records from 966 horses and that for the pedigree contained 8733 animals (47% Arabian horses), with an estimated heritability around 0.10 for the ranking trait. The prediction ability of the models for racing performance was evaluated using a cross-validation approach. The average correlation between real and predicted performances across genetic models was around 0.25 for threshold, 0.58 for linear and 0.60 for Thurstonian approaches. Although no significant differences were found between models within approaches, the best genetic model included: the rider and rider-horse random effects for threshold, only rider and environmental permanent effects for linear approach and all random effects for Thurstonian approach. The absolute correlations of predicted breeding values among models were higher between threshold and Thurstonian: 0.90, 0.91 and 0.88 for all animals, top 20% and top 5% best animals. For rank correlations these figures were 0.85, 0.84 and 0.86. The lower values were those between linear and threshold approaches (0.65, 0.62 and 0.51). In conclusion, the Thurstonian approach is recommended for the routine genetic evaluations for ranking in endurance horses.

摘要

排名性状被用作赛马的选择标准,以估计比赛表现。在文献中,最常见的估计育种值的方法是线性或阈统计模型。然而,最近的研究表明,Thurstonian 方法能够固定比赛效应(参加同一比赛的马匹的竞争水平),从而表明对排名性状的育种值具有更好的预测准确性。本研究旨在比较线性、阈和 Thurstonian 方法在耐力赛马遗传评估中的预测能力。为此,对于每个方法,使用了八种遗传模型,随机效应的不同组合:骑手、骑手-马互作和环境永久效应。所有遗传模型都包括性别、年龄和比赛作为系统效应。使用的数据库包含 966 匹马的 4065 个排名记录, pedigree 包含 8733 只动物(47%的阿拉伯马),排名性状的估计遗传力约为 0.10。使用交叉验证方法评估模型对比赛表现的预测能力。在遗传模型中,真实表现与预测表现之间的平均相关性约为阈的 0.25,线性的 0.58,Thurstonian 的 0.60。尽管在方法内没有发现模型之间的显著差异,但最佳遗传模型包括:阈的骑手和骑手-马随机效应,线性的仅骑手和环境永久效应,以及 Thurstonian 的所有随机效应。模型之间预测的育种值的绝对相关性在阈和 Thurstonian 之间更高:所有动物的 0.90、0.91 和 0.88,前 20%和前 5%最佳动物。对于排名相关性,这些数字分别为 0.85、0.84 和 0.86。较低的值是线性和阈方法之间的(0.65、0.62 和 0.51)。总之,Thurstonian 方法建议用于耐力赛马的常规遗传评估。

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