Mangalarga Marchador (MM) is a very important Brazilian gaited horse breed. These animals show two gait types, differing in proportion of lateral or diagonal movements. Thus, it is possible to consider the gait type as a binary categorical trait in our statistical models. Threshold models (TM) are strongly recommended to handle binary traits for genetic evaluation purposes. However, TM are susceptible to the extreme categorical problem (ECP). ECP is usually observed due to the absence of variation within subclasses for a given systematic effect and can be avoided after handling this effect as random or by combining different systematic effects in the model. In this context, we aimed to find the most suitable systematic effect (based on goodness-of-fit and predictive ability) to be included in Bayesian threshold model for the genetic evaluation of gait type in MM horses. The dataset consisted of 1,231 gait type records and 3,172 animals in the pedigree file. Phenotypic record associated with gait type was treated as a categorical trait (MP = 0 and MB = 1). In summary, models with small complexity were benefited by smaller bias and average prediction errors. Additionally, these models showed higher heritability estimates. ECP was an important issue, and should always be approached when using threshold models for genetic evaluation.

Mangalarga Marchador（MM）是非常重要的巴西步态马品种。这些动物表现出两种步态类型，横向或对角运动的比例不同。因此，有可能在我们的统计模型中将步态类型视为二元分类特征。强烈建议使用阈值模型（TM）处理二进制特征，以进行遗传评估。但是，TM容易受到极端分类问题（ECP）的影响。通常观察到ECP的原因是对于给定的系统效果子类内没有变异，可以在将这种效果作为随机值处理或在模型中组合不同的系统效果后予以避免。在这种情况下，我们旨在找到最合适的系统效果（基于拟合优度和预测能力），将其包括在贝叶斯阈值模型中以对MM马的步态类型进行遗传评估。该数据集由谱系文件中的1,231个步态类型记录和3,172只动物组成。与步态类型相关的表型记录被视为分类特征（MP = 0和MB = 1）。总而言之，具有较小复杂性的模型将受益于较小的偏差和平均预测误差。此外，这些模型显示出较高的遗传力估计值。ECP是一个重要问题，在使用阈值模型进行遗传评估时应始终采用ECP。与步态类型相关的表型记录被视为分类特征（MP = 0和MB = 1）。总而言之，具有较小复杂度的模型将受益于较小的偏差和平均预测误差。此外，这些模型显示出较高的遗传力估计值。ECP是一个重要问题，在使用阈值模型进行遗传评估时应始终采用ECP。与步态类型相关的表型记录被视为分类特征（MP = 0和MB = 1）。总而言之，具有较小复杂性的模型将受益于较小的偏差和平均预测误差。此外，这些模型显示出较高的遗传力估计值。ECP是一个重要问题，在使用阈值模型进行遗传评估时应始终采用ECP。