A Novel Model to Explain Extreme Feather Pecking Behavior in Laying Hens

A Novel Model to Explain Extreme Feather Pecking Behavior in Laying Hens
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DOI:
10.1007/s10519-019-09971-w
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发表时间:
2020-01-01
期刊:
影响因子:
2.6
通讯作者:
Bennewitz, J.
Bennewitz, J.
中科院分区:
医学3区
文献类型:
--
作者:
Iffland, H.;Wellmann, R.;Bennewitz, J.

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啄羽是家禽的一个严重的经济和福利问题。最近的研究表明,群体内FP发作的分布是异质的,并包含极端啄羽鸟(EFP)的亚群。本研究提出了一种新的模型来检测EFP母鸡。将两个负二项分布的混合物拟合到约960只母鸡的F-2杂交的FP数据,并且基于结果,计算属于EFP亚组(pEFP)的每只母鸡的后验概率。混合分布的拟合表明,EFP亚群占F-2组合的三分之一。EFP鸟更频繁地进入啄食情绪,并表现出更高的啄食强度相比,其余的鸟类。在母鸡的幼年和成年阶段进行紧张性不动和羽化箱试验,以将恐惧与EFP联系起来。二分后,所有性状在多变量阈值模型中进行分析,并进行全基因组关联研究。新性状pEFP具有中等遗传力0.35,与恐惧性状呈正相关。培育这种新特性可能是一个有趣的选择,以减少极端啄羽鸟的比例。恐惧相关性状的指数可以作为间接繁殖pEFP的代理。GWAS分析表明,所有性状都是典型的数量性状,基因数目多,遗传方差的贡献效应小。
Feather pecking (FP) is a serious economic and welfare problem in the domestic fowl. It has recently been shown that the distribution of FP bouts within groups is heterogeneous and contains a sub-population of extreme feather peckers (EFP). The present study proposed a novel model to detect EFP hens. A mixture of two negative binomial distributions was fitted to FP data of a F-2 cross of about 960 hens, and, based on the results, a calculation of the posterior probability for each hen belonging to the EFP subgroup (pEFP) was done. The fit of the mixture distribution revealed that the EFP subgroup made up a proportion of one third of the F-2 cross. The EFP birds came more frequently into pecking mood and showed higher pecking intensities compared to the remaining birds. Tonic immobility and emerge box tests were conducted at juvenile and adult age of the hens to relate fearfulness to EFP. After dichotomization, all traits were analyzed in a multivariate threshold model and a genomewide association study was performed. The new trait pEFP has a medium heritability of 0.35 and is positively correlated with the fear traits. Breeding for this new trait could be an interesting option to reduce the proportion of extreme feather peckers. An index of fear related traits might serve as a proxy to breed indirectly for pEFP. GWAS revealed that all traits are typical quantitative traits with many genes and small effects contributing to the genetic variance.