Worms and ticks are important parasites in beef cattle, especially in tropical areas, causing significant economic and production losses. Understanding animal-to-animal variation on infestation for these parasites might guide genetic selection and improvement of management practices to attenuate its detrimental effects. Statistical models used to analyze such traits usually assume a Gaussian distribution for the observed data. However, this assumption is quite often inappropriate for counting data. Therefore, the objectives of this study were: 1) Estimate genetic parameters for worms and tick infestations in Nellore cattle, and 2) To compare the overall performance of six data analysis approaches for worm and tick infestation in Nellore cattle, using different specifications of generalized linear mixed models (GLMM) and response variables. Data consisted of presence/absence of parasites as well as counting observations for both worms and ticks in a Nellore herd in Brazil. The binary data were analysed with both Gaussian and Threshold models, whereas the counting data were studied using Gaussian models on the original and logarithmic scales, as well as Poisson and Zero-Inflated Poisson (ZIP) models. All models included the systematic effects of contemporary group and age, as well as the random additive genetic and residual effects. Models were compared using four criteria: Deviance Information Criterion (DIC), Spearman's correlation between predicted breeding values from different models, the agreement on the 5 and 50% top-ranked animals across models, and the Mean Squared Error of Prediction (MSEP) assessed via Monte Carlo Cross-Validation (MCCV). The MCCV was performed using parallel computing through the Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison. The estimates of heritability ranged from 0.15 to 0.40 for worms and from 0.08 to 0.25 for ticks. According to the DIC, non-Gaussian models displayed the best goodness of fit compared to Gaussian models. DIC's results excluded Gaussian models on the logarithmic scale because fairer comparisons involve phenotypes on the same scale. Spearman's correlation and the percentage of agreement on the 5% and 50% top-ranked animals suggested some re-ranking of animals depending upon the model used. Monte Carlos Cross-Validation showed that all models presented similar MSEP with average values of 0.20 (binary data; worms), 0.18 (binary data; ticks), 15.69 (count data; worms), and 14.19 (count data; ticks). Moreover, performing MCCV in parallel has the benefit of delivery results for all models in about 2 days. Heritability estimates indicate that the selection of high merit animals for worms and tick resistance is possible feasible and can potentially contribute to the genetic progress. Furthermore, genetic selection should be performed concomitantly with traditional parasite control approaches. Overall, non-Gaussian models seem to be better suitable for genetic analysis of worm and tick infestation in beef cattle, because such models have lower DIC values with similar predictive performance compared to Gaussian models.

蠕虫和tick虫是肉牛的重要寄生虫，尤其是在热带地区，会造成重大的经济和生产损失。了解这些寄生虫在动物身上的侵染变化可能会指导遗传选择和管理方法的改进，以减轻其有害影响。用于分析此类特征的统计模型通常假定观测数据的高斯分布。但是，此假设通常不适用于计数数据。因此，本研究的目的是：1）估算内洛尔牛蠕虫和壁虱侵袭的遗传参数，以及2）使用不同的广义规范，比较六种数据方法对内洛尔牛蠕虫和壁虱侵袭的总体性能线性混合模型（GLMM）和响应变量。数据包括寄生虫的有无，以及计数巴西内洛尔牛群中蠕虫和tick虫的观察值。使用高斯模型和阈值模型分析了二进制数据，而使用高斯模型在原始和对数尺度上以及泊松模型和零膨胀泊松（ZIP）模型中研究了计数数据。所有模型都包括当代群体和年龄的系统影响，以及随机加性遗传和残留影响。使用四个标准对模型进行比较：偏差信息标准（DIC），不同模型的预测育种值之间的Spearman相关性，模型中5％和50％排名最高的动物的一致性以及预测的均方误差（MSEP）通过蒙特卡洛交叉验证（MCCV）。MCCV是通过威斯康星大学麦迪逊分校的高吞吐量计算中心（CHTC）使用并行计算执行的。蠕虫的遗传力估计值范围从0.15到0.40，壁虱的遗传力估计值从0.08到0.25。根据DIC，与高斯模型相比，非高斯模型显示出最佳的拟合优度。DIC的结果排除了对数规模的高斯模型，因为更公平的比较涉及相同规模的表型。Spearman的相关性以及排名最高的5％和50％的动物的一致性百分比表明，根据所使用的模型，对动物进行了一些重新排名。蒙特卡洛斯交叉验证显示，所有模型均呈现相似的MSEP，平均值分别为0.20（二进制数据；蠕虫），0.18（二进制数据；滴答），15.69（计数数据；蠕虫）和14.19（计数数据；滴答）。此外，并行执行MCCV的好处是，所有模型在约2天内都能获得交付结果。遗传力估计表明，为蠕虫和tick虫抗性选择高品质动物是可行的，并且可能有助于遗传进程。此外，遗传选择应与传统的寄生虫控制方法同时进行。总体而言，非高斯模型似乎更适合于肉牛蠕虫和壁虱侵袭的遗传分析，因为与高斯模型相比，此类模型具有更低的DIC值和相似的预测性能。遗传力估计表明，为蠕虫和tick虫抗性选择高品质动物是可行的，并且可能有助于遗传进程。此外，遗传选择应与传统的寄生虫控制方法同时进行。总体而言，非高斯模型似乎更适合于肉牛蠕虫和壁虱侵袭的遗传分析，因为与高斯模型相比，此类模型具有更低的DIC值和相似的预测性能。遗传力估计表明，为蠕虫和tick虫抗性选择高品质动物是可行的，并且可能有助于遗传进程。此外，遗传选择应与传统的寄生虫控制方法同时进行。总体而言，非高斯模型似乎更适合于肉牛蠕虫和壁虱侵袭的遗传分析，因为与高斯模型相比，此类模型具有更低的DIC值和相似的预测性能。