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Predicting direct and indirect breeding values for survival time in laying hens using repeated measures

机译：使用重复测量预测蛋鸡生存时间的直接和间接繁殖值

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Background Minimizing bird losses is important in the commercial layer industry. Selection against mortality is challenging because heritability is low, censoring is high, and individual survival depends on social interactions among cage members. With cannibalism, mortality depends not only on an individual’s own genes (direct genetic effects; DGE) but also on genes of its cage mates (indirect genetic effects; IGE). To date, studies using DGE–IGE models have focussed on survival time but their shortcomings are that censored records were considered as exact lengths of life and models assumed that IGE were continuously expressed by all cage members even after death. However, since dead animals no longer express IGE, IGE should ideally be time-dependent in the model. Neglecting censoring and timing of IGE expression may reduce accuracy of estimated breeding values (EBV). Thus, our aim was to improve prediction of breeding values for survival time in layers that present cannibalism. Methods We considered four DGE–IGE models to predict survival time in layers. One model was an analysis of survival time and the three others treated survival in consecutive months as a repeated binomial trait (repeated measures models). We also tested whether EBV were improved by including timing of IGE expression in the analyses. Approximate EBV accuracies were calculated by cross-validation. The models were fitted to survival data on two purebred White Leghorn layer lines W1 and WB, each having monthly survival records over 13?months. Results Including the timing of IGE expression in the DGE–IGE model reduced EBV accuracy compared to analysing survival time. EBV accuracy was higher when repeated measures models were used. However, there was no universal best model. Using repeated measures instead of analysing survival time increased EBV accuracy by 10 to 21 and 2 to 12?% for W1 and WB, respectively. We showed how EBV and variance components estimated with repeated measures models can be translated into survival time. Conclusions Our results suggest that prediction of breeding values for survival time in laying hens can be improved using repeated measures models. This is an important result since more accurate EBV contribute to higher rates of genetic gain.
机译：背景技术在商业蛋鸡产业中，使禽类损失最小化是重要的。针对死亡率的选择具有挑战性，因为遗传力低，审查率高，并且个人生存取决于笼子成员之间的社会互动。食人症的死亡率不仅取决于个人自身的基因（直接遗传效应; DGE），而且还取决于其笼伴侣的基因（间接遗传效应; IGE）。迄今为止，使用DGE-IGE模型进行的研究都集中在生存时间上，但其缺点是审查记录被认为是确切的寿命，并且模型假设IGE在所有笼子成员死亡后仍在持续表达。但是，由于死动物不再表达IGE，因此理想情况下IGE在模型中应与时间有关。忽略IGE表达的审查和时间安排可能会降低估计育种值（EBV）的准确性。因此，我们的目的是改进呈现食人性的层中存活时间的繁殖值的预测。方法我们考虑了四个DGE-IGE模型来预测各层的生存时间。一种模型是对生存时间的分析，而其他三种模型则将连续几个月的生存视为重复的二项式特征（重复测量模型）。我们还测试了在分析中纳入IGE表达时间是否可以改善EBV。通过交叉验证计算大约的EBV准确性。这些模型适用于两个纯种的白色来克霍恩蛋鸡行W1和WB的生存数据，每个月都有13个月以上的生存记录。结果与分析生存时间相比，在DGE–IGE模型中包括IGE表达的时间安排会降低EBV准确性。使用重复测量模型时，EBV准确性较高。但是，没有通用的最佳模型。使用重复测量代替分析生存时间，对于W1和WB，EBV准确性分别提高了10％至21％和2％至12％。我们展示了如何将用重复测量模型估算的EBV和方差成分转化为生存时间。结论我们的结果表明，使用重复测量模型可以改善蛋鸡存活时间的育种值预测。这是一个重要的结果，因为更准确的EBV有助于更高的遗传增益率。

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