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首页> 外文期刊>Journal of Animal Science >How to improve breeding value prediction for feed conversion ratio in the case of incomplete longitudinal body weights
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How to improve breeding value prediction for feed conversion ratio in the case of incomplete longitudinal body weights

机译：如何改善对纵向体重不完全的饲料转换率的育种值预测

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With the development of automatic self-feeders, repeated measurements of feed intake are becoming easier in an increasing number of species. However, the corresponding BW are not always recorded, and these missing values complicate the longitudinal analysis of the feed conversion ratio (FCR). Our aim was to evaluate the impact of missing BW data on estimations of the genetic parameters of FCR and ways to improve the estimations. On the basis of the missing BW profile in French Large White pigs (male pigs weighed weekly, females and castrated males weighed monthly), we compared 2 different ways of predicting missing BW, 1 using a Gompertz model and 1 using a linear interpolation. For the first part of the study, we used 17,398 weekly records of BW and feed intake recorded over 16 consecutive weeks in 1,222 growing male pigs. We performed a simulation study on this data set to mimic missing BW values according to the pattern of weekly proportions of incomplete BW data in females and castrated males. The FCR was then computed for each week using observed data (obser_FCR), data with missing BW (miss_FCR), data with BW predicted using a Gompertz model (Gomp_FCR), and data with BW predicted by linear interpolation (interp_FCR). Heritability (h(2)) was estimated, and the EBV was predicted for each repeated FCR using a random regression model. In the second part of the study, the full data set (males with their complete BW records, castrated males and females with missing BW) was analyzed using the same methods (miss_FCR, Gomp_FCR, and interp_FCR). Results of the simulation study showed that h2 were overestimated in the case of missing BW and that predicting BW using a linear interpolation provided a more accurate estimation of h2 and of EBV than a Gompertz model. Over 100 simulations, the correlation between obser_EBV and interp_EBV, Gomp_EBV, and miss_EBV was 0.93 +/- 0.02, 0.91 +/- 0.01, and 0.79 +/- 0.04, respectively. The heritabilities obtained with the full data set were quite similar for miss_FCR, Gomp_FCR, and interp_FCR. In conclusion, when the proportion of missing BW is high, genetic parameters of FCR are not well estimated. In French Large White pigs, in the growing period extending from d 65 to 168, prediction of missing BW using a Gompertz growth model slightly improved the estimations, but the linear interpolation improved the estimation to a greater extent. This result is due to the linear rather than sigmoidal increase in BW over the study period.
机译：随着自动自馈器的发展，在越来越多的物种中，反复测量的饲料摄入量变得越来越容易。然而，并不总是记录相应的BW，并且这些缺失值使饲料转换比（FCR）的纵向分析复杂化。我们的目标是评估缺少BW数据对FCR遗传参数的影响及改进估计的方法。在法国大白猪的缺失的BW型材的基础上（每周称重雄性猪，女性和月球阉割的男性），我们将使用Gompertz模型和1使用线性插值来比较2种不同的方式来预测缺失的BW，1。对于该研究的第一部分，我们在1,222个生长的雄性猪中连续16周使用17,398次每周记录BW和Feed Intrake。我们对该数据进行了模拟研究，根据女性和阉割的男性的不完整BW数据的每周比例的模式，对模拟缺失的BW值进行模拟。然后使用观察到的数据（Obser_FCR），使用缺失BW（Miss_FCR）的数据来计算FCR，使用Gompertz模型（GOMP_FCR）预测BW的数据，以及用线性插值预测的BW（Interp_fcr）预测的数据。估计遗传性（H（2）），使用随机回归模型预测每个重复的FCR的EBV。在研究的第二部分中，使用相同的方法分析了完整的数据集（具有其完整的BW记录，阉割的男性和缺失BW的女性和缺失BW的女性）（miss_fcr，gomp_fcr和Interp_fcr）。模拟研究的结果表明，在缺失BW的情况下，H2高估，并且使用线性插值预测BW提供了更准确的H2和EBV的估计而不是Gompertz模型。超过100种模拟，观察者_EBV和Interp_ebv之间的相关性分别为0.93 +/- 0.02,0.91 +/- 0.02,0.91 +/- 0.01和0.79 +/- 0.04。使用完整数据集获得的遗产对于Miss_FCR，GOMP_FCR和InterP_FCR非常相似。总之，当缺失BW的比例很高时，FCR的遗传参数并不估计。在法国大白猪中，在从D 65到168延伸的越来越多的时期，使用Gompertz生长模型预测缺失的BW略微提高了估计，但线性插值在更大程度上提高了估计。这一结果是由于研究期间BW的线性而不是Sigmoidal增加。

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