Iung L H S, Neves H H R, Mulder H A, Carvalheiro R
J Anim Sci. 2017 Apr;95(4):1425-1433. doi: 10.2527/jas.2016.1326.
There is evidence for genetic variability in residual variance of livestock traits, which offers the potential for selection for increased uniformity of production. Different statistical approaches have been employed to study this topic; however, little is known about the concordance between them. The aim of our study was to investigate the genetic heterogeneity of residual variance on yearling weight (YW; 291.15 ± 46.67) in a Nellore beef cattle population; to compare the results of the statistical approaches, the two-step approach and the double hierarchical generalized linear model (DHGLM); and to evaluate the effectiveness of power transformation to accommodate scale differences. The comparison was based on genetic parameters, accuracy of EBV for residual variance, and cross-validation to assess predictive performance of both approaches. A total of 194,628 yearling weight records from 625 sires were used in the analysis. The results supported the hypothesis of genetic heterogeneity of residual variance on YW in Nellore beef cattle and the opportunity of selection, measured through the genetic coefficient of variation of residual variance (0.10 to 0.12 for the two-step approach and 0.17 for DHGLM, using an untransformed data set). However, low estimates of genetic variance associated with positive genetic correlations between mean and residual variance (about 0.20 for two-step and 0.76 for DHGLM for an untransformed data set) limit the genetic response to selection for uniformity of production while simultaneously increasing YW itself. Moreover, large sire families are needed to obtain accurate estimates of genetic merit for residual variance, as indicated by the low heritability estimates (<0.007). Box-Cox transformation was able to decrease the dependence of the variance on the mean and decreased the estimates of genetic parameters for residual variance. The transformation reduced but did not eliminate all the genetic heterogeneity of residual variance, highlighting its presence beyond the scale effect. The DHGLM showed higher predictive ability of EBV for residual variance and therefore should be preferred over the two-step approach.
有证据表明家畜性状的剩余方差存在遗传变异性，这为选择提高生产均匀度提供了潜力。已采用不同的统计方法来研究这一主题；然而，对于它们之间的一致性了解甚少。我们研究的目的是调查内洛尔肉牛群体一岁龄体重（YW；291.15±46.67）剩余方差的遗传异质性；比较统计方法（两步法和双重分层广义线性模型（DHGLM））的结果；并评估幂变换以适应尺度差异的有效性。比较基于遗传参数、剩余方差的估计育种值（EBV）准确性以及交叉验证以评估两种方法的预测性能。分析中使用了来自625头公牛的总共194,628条一岁龄体重记录。结果支持内洛尔肉牛YW剩余方差遗传异质性的假设以及通过剩余方差的遗传变异系数衡量的选择机会（两步法为0.10至0.12，DHGLM为0.17，使用未变换数据集）。然而，与均值和剩余方差之间的正遗传相关性相关的低遗传方差估计值（未变换数据集两步法约为0.20，DHGLM为0.76）限制了对生产均匀度选择的遗传反应，同时增加YW本身。此外，需要大的父系家系来获得剩余方差遗传价值的准确估计，低遗传力估计值（<0.007）表明了这一点。Box-Cox变换能够降低方差对均值的依赖性，并降低剩余方差遗传参数的估计值。该变换减少但并未消除剩余方差的所有遗传异质性，突出了其在尺度效应之外的存在。DHGLM显示出对剩余方差的EBV具有更高的预测能力，并因此应优先于两步法。
