Canario L, Cantoni E, Le Bihan E, Caritez J C, Billon Y, Bidanel J P, Foulley J L
Unit of Applied and Quantitative Genetics, INRA, 78352 Jouy-en-Josas, France.
J Anim Sci. 2006 Dec;84(12):3185-96. doi: 10.2527/jas.2005-775.
Litter characteristics at birth were recorded in 4 genetic types of sows with differing maternal abilities. Eighty-two litters from F(1) Duroc x Large White sows, 651 litters from Large White sows, 63 litters from Meishan sows, and 173 litters from Laconie sows were considered. Statistical models included random effects of sow, litter, or both; fixed effects of sow genetic type, parity, birth assistance, and piglet sex, as well as gestation length, farrowing duration, piglet birth weight, and litter size as linear covariates. The quadratic components of the last 2 factors were also considered. For statistical analyses, GLM were first considered, assuming a binomial distribution of stillbirth. Hierarchical models were also fitted to the data to take into account correlations among piglets from the same litter. Model selection was performed based on deviance and deviance information criterion. Finally, standard and robust generalized estimating equations (GEE) procedures were applied to quantify the importance of each effect on a piglet's probability of stillbirth. The 5 most important factors involved were, in decreasing order (contribution of each effect to variance reduction): difference between piglet birth weight and the litter mean (2.36%), individual birth weight (2.25%), piglet sex (1.01%), farrowing duration (0.99%), and sow genetic type (0.94%). Probability of stillbirth was greater for lighter piglets, for male piglets, and for piglets from small or very large litters. Probability of stillbirth increased with sow parity number and with farrowing duration. Piglets born from Meishan sows had a lower risk of stillbirth (P < 0.0001) and were little affected by the sources of variation mentioned above compared with the 3 other sow genetic types. Standard and robust GEE approaches gave similar results despite some disequilibrium in the data set structure highlighted with the robust GEE approach.
记录了具有不同母性能力的4种遗传类型母猪的出生窝仔特征。研究对象包括82窝F(1) 杜洛克×大白母猪、651窝大白母猪、63窝梅山母猪和173窝拉康尼母猪的仔猪。统计模型包括母猪、窝仔或两者的随机效应；母猪遗传类型、胎次、助产情况、仔猪性别等固定效应，以及妊娠期长度、产仔持续时间、仔猪出生体重和窝仔数作为线性协变量。还考虑了后两个因素的二次成分。对于统计分析，首先考虑广义线性模型（GLM），假设死胎呈二项分布。还对数据拟合了分层模型，以考虑同一窝仔猪之间的相关性。基于偏差和偏差信息准则进行模型选择。最后，应用标准和稳健的广义估计方程（GEE）程序来量化每种效应对仔猪死胎概率的重要性。所涉及的5个最重要因素按降序排列（每种效应对方差减少的贡献）：仔猪出生体重与窝仔平均体重之差（2.36%）、个体出生体重（2.25%）、仔猪性别（1.01%）、产仔持续时间（0.99%）和母猪遗传类型（0.94%）。体重较轻的仔猪、雄性仔猪以及来自小窝或大窝的仔猪死胎概率更高。死胎概率随着母猪胎次和产仔持续时间的增加而增加。与其他3种母猪遗传类型相比，梅山母猪所产仔猪的死胎风险较低（P < 0.0001），且受上述变异来源的影响较小。尽管稳健GEE方法突出显示了数据集中存在一些不平衡，但标准和稳健的GEE方法给出了相似的结果。
