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比较统计模型分析猪窝内变异的遗传效应。

Comparison of statistical models to analyse the genetic effect on within-litter variance in pigs.

机构信息

Forschungsinstitut für die Biologie landwirtschaftlicher Nutztiere, FB Genetik und Biometrie, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, Germany.

出版信息

Animal. 2008 Nov;2(11):1559-68. doi: 10.1017/S1751731108002851.

Abstract

Genetics affects not only the weight of piglets at birth but also the variability of birth weight within litter. Previous studies on this topic assigned the sample standard deviation of piglet birth weights within litter as an observation to the sow. However, the contribution of the difference in mean birth weight per sex on the within-litter variance has been neglected so far. This work deals with the genetic effect on within-litter variance when different statistical models with different distributional assumptions are used and considers the sex effect and appropriate weights per trait. Traits were formed from the pooled sample variance of male and female birth weights within litter. A linear model approach was fitted to the logarithmized within-litter variance and the sample standard deviation. A generalized linear model with gamma-distributed residuals and log-link function was applied to the untransformed sample variance. Models were compared by analysing data from 9439 litters from Landrace and Large White of a commercial breeding programme. The estimates of heritability for different traits ranged from 7% to 11%. Although the generalized linear mixed model is preferred from a mathematical view, the rank correlations between breeding values of the linear mixed models and the generalized linear mixed model were relatively high, i.e. 94% to 98%. By residual diagnostics, a linear mixed model using the weighted and pooled within-litter standard deviation was identified as most suitable.

摘要

遗传学不仅影响仔猪出生时的体重,还影响窝内出生体重的变异性。以前关于这个主题的研究将窝内仔猪出生体重的样本标准差作为观察值分配给母猪。然而,迄今为止,一直忽略了每个性别之间平均出生体重差异对窝内变异性的贡献。这项工作涉及在使用不同分布假设的不同统计模型中,遗传对窝内变异性的影响,并考虑性别效应和每个性状的适当权重。性状是由窝内雄性和雌性出生体重的样本方差组合而成的。对数化的窝内方差和样本标准差采用线性模型方法进行拟合。未转换的样本方差采用具有伽马分布残差和对数链接函数的广义线性模型进行拟合。通过分析来自商业繁殖计划的长白猪和大白猪 9439 窝的数据来比较模型。不同性状的遗传力估计值范围从 7%到 11%。虽然从数学角度来看,广义线性混合模型是首选,但线性混合模型和广义线性混合模型的育种值之间的秩相关度相对较高,即 94%到 98%。通过残差诊断,使用加权和组合窝内标准差的线性混合模型被确定为最合适的模型。

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