Abstract Growth, being a repeatable trait, is found to be more suitable for random regression models (RRM). Especially in a field performance recording system the nature of data is heterogenous and the structure of data causes problems in genetic evaluation through normal univariate models. In this study, RRM was used to estimate variance components for growth in farmers’ flocks of Madras Red sheep, reared under the ICAR-Network Project on Sheep Improvement-Madras Red Field Unit (NWPSI), Government of India. Data on body weight collected over a period of six years in this project was used for the study. General linear model (GLM) with ANOVA for repeated measures was used to understand the effect of non-genetic factors including, center, season, sex and period. For the RRM, along with the significant non-genetic effects, orders of polynomial (k) fit up to 4, including the constant term, were considered for the random effects of sire and individual permanent environment. Error variances were modeled as homogenous and heterogenous classes. The heterogenous classes were modeled as a step function with four and ten different classes of age. The 10 class heterogenous error class model with order of fit 4 for both the random effects was found to have the best fit and the sire variance ratio estimated through this model for weights at 3, 6, 9 and 12 months of age were 0.195 ± 0.022, 0.27 ± 0.027, 0.04 ± 0.007 and 0.39 ± 0.047. The study indicated that RRM was found to be suitable for growth data measured over wide range of ages in farmers’ flocks. Precise estimates of variance components could be estimated for most part of the growth curve and this model avoids the error due to adjustment of data usually done for body weights at specific ages. In order to obtain precise estimates of genetic parameters through RRM, data recorded should be evenly distributed over all the age classes.

中文翻译：

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在田间性能记录系统下随机回归模型对马德拉斯红羊生长的适用性

摘要 增长是一种可重复的特征，被发现更适合随机回归模型 (RRM)。特别是在田间性能记录系统中，数据的性质是异质的，数据的结构会导致通过正常单变量模型进行遗传评估的问题。在这项研究中，RRM 用于估计马德拉斯红羊农民羊群生长的方差分量，这些羊群是在印度政府的 ICAR 绵羊改良网络项目 - 马德拉斯红场单位 (NWPSI) 下饲养的。在该项目中收集的六年期间的体重数据用于研究。使用具有重复测量方差分析的一般线性模型 (GLM) 来了解非遗传因素的影响，包括中心、季节、性别和时期。对于 RRM，连同显着的非遗传效应，多项式 (k) 的阶数最多为 4，包括常数项，考虑了父系和个体永久环境的随机效应。误差方差被建模为同质和异质类。异质类别被建模为具有四个和十个不同年龄类别的阶跃函数。发现随机效应的拟合顺序为 4 的 10 类异质误差类模型具有最佳拟合，并且通过该模型估计的 3、6、9 和 12 个月龄体重的父系方差比为 0.195 ± 0.022 , 0.27 ± 0.027, 0.04 ± 0.007 和 0.39 ± 0.047。该研究表明，发现 RRM 适用于测量农民鸡群不同年龄范围内的生长数据。可以对大部分生长曲线的方差分量进行精确估计，并且该模型避免了由于通常针对特定年龄的体重进行数据调整而导致的误差。为了通过 RRM 获得对遗传参数的精确估计，记录的数据应均匀分布在所有年龄段。