It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

中文翻译：

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包括缺失记录的原因对商品猪生长基因评估的影响

评估杂交猪的热胴体重 (HCW) 和出生体重 (BW) 是有意义的；但是，获得 HCW 记录取决于宜居性 (LIV) 和保留标签 (RT)。本研究的目的是分析当模型中包含群体清除和缺失识别时，HCW 评估如何受到影响，并检查考虑缺失性状的原因是否提高了预测育种值的准确性。1,965,077 只纯种和杂交动物的谱系信息可用。Smithfield Premium Genetics 提供了 2014 年至 2019 年 503,716 只商业三向杂交终端动物的记录。比较了两个基于谱系的模型；模型 1 (M1) 是具有所有四个特征（BW、HCW、RT 和 LIV）的阈值线性模型，模型 2 (M2) 是仅包括 BW 和 HCW 的线性模型。模型中使用的固定效应是当代群体、性别、收获年龄（仅适用于 HCW）和母系胎次。随机效应包括直接加性遗传和随机垃圾效应。使用线性回归方法估计准确性、离散度、偏差和 Pearson 相关性。BW、HCW、RT和LIV的遗传力分别为0.11、0.07、0.02和0.04，标准误小于0.01。M1 和 M2 之间的 BW 和 HCW 的遗传力或准确性没有观察到差异。BW、HCW、RT 和 LIV 的准确度分别为 0.33、0.37、0.19 和 0.23。BW与RT之间的遗传相关性为0.34±0.03，BW与LIV之间的遗传相关性为0.56±0.03。同样，HCW 和 RT 之间的遗传相关性为 0.26 ± 0.04，HCW 和 LIV 之间的遗传相关性分别为 0.09 ± 0.05。体重与其他性状之间的正相关和中等遗传相关性意味着体重越重，存活收获的概率越高。由于大量缺失记录，医护人员与其他性状之间的遗传相关性较低。尽管 RT 和 LIV 具有可遗传和相关的方面，但结果表明 M1 和 M2 之间没有重大差异；因此，没有必要将这些特征包含在 BW 和 HCW 的经典模型中。