Ramirez-Valverde R, Misztal I, Bertrand J K
Animal and Dairy Science Department, University of Georgia, Athens 30602, USA.
J Anim Sci. 2001 Feb;79(2):333-8. doi: 10.2527/2001.792333x.
This study compared the accuracy of several models for obtaining genetic evaluations of calving difficulty. The models were univariate threshold animal (TAM), threshold sire-maternal grandsire (TSM), linear animal (LAM), and linear sire-maternal grandsire (LSM) models and bivariate threshold-linear animal (TLAM), threshold-linear sire-maternal grandsire (TLSM), linear-linear animal (LLAM), and linear-linear sire-maternal grandsire (LLSM) models for calving difficulty and birth weight. Data were obtained from the American Gelbvieh Association and included 84,420 first-parity records of both calving difficulty and birth weight. Calving difficulty scores were distributed as 73.4% in the first category (no assistance), 18.7% in the second, 6.3% in the third, and 1.6% in the fourth. Included in the animal models were fixed sex of calf by age of dam subclasses, random herd-year-season effects, and random animal direct and maternal breeding values. Sire-maternal grandsire models were similar to the animal models, with animal and maternal effects replaced by sire and maternal grandsire effects. Models were compared using a data splitting technique based on the correlation of estimated breeding values from two samples, with one-half of the calving difficulty records discarded randomly in the first sample and the remaining calving difficulty records discarded in the second sample. Reported correlations are averages of 10 replicates. The results obtained using animal models confirmed the slight advantage of TAM over LAM (0.69 vs 0.63) and TLAM over LLAM (0.90 vs 0.86). Bivariate analyses greatly improved the accuracy of genetic prediction of direct effects on calving difficulty relative to univariate analyses. Similar ranking of the models was found for maternal effects, but smaller correlations were obtained for bivariate models. For sire-maternal grandsire models, no differences between sire or maternal grandsire correlations were observed for TLSM compared to LLSM, and small differences were observed between TSM and LSM. The threshold model offered advantages over the linear model in animal models but not in sire-maternal grandsire models. For genetic evaluation of calving difficulty in beef cattle, the threshold-linear animal model seems to be the best choice for predicting both direct and maternal effects.
本研究比较了几种用于获得产犊难易度遗传评估模型的准确性。这些模型包括单变量阈值动物模型（TAM）、阈值父系-母系祖父模型（TSM）、线性动物模型（LAM）和线性父系-母系祖父模型（LSM），以及用于产犊难易度和出生体重的双变量阈值-线性动物模型（TLAM）、阈值-线性父系-母系祖父模型（TLSM）、线性-线性动物模型（LLAM）和线性-线性父系-母系祖父模型（LLSM）。数据取自美国盖尔维肉牛协会，包括84420条产犊难易度和出生体重的头胎记录。产犊难易度得分分布如下：第一类（无需助产）占73.4%，第二类占18.7%，第三类占6.3%，第四类占1.6%。动物模型中包含按母牛年龄划分的犊牛固定性别亚类、随机的畜群-年份-季节效应，以及随机的动物直接育种值和母系育种值。父系-母系祖父模型与动物模型类似，只是用父系和母系祖父效应取代了动物和母系效应。使用基于两个样本估计育种值相关性的数据拆分技术对模型进行比较，在第一个样本中随机丢弃一半产犊难易度记录，在第二个样本中丢弃其余的产犊难易度记录。报告的相关性是10次重复的平均值。使用动物模型得到的结果证实了TAM相对于LAM的微弱优势（0.69对0.63）以及TLAM相对于LLAM的微弱优势（0.90对0.86）。与单变量分析相比，双变量分析大大提高了对产犊难易度直接效应的遗传预测准确性。对于母系效应，模型的排序相似，但双变量模型的相关性较小。对于父系-母系祖父模型，与LLSM相比，TLSM在父系或母系祖父相关性方面未观察到差异，TSM和LSM之间观察到的差异较小。在动物模型中，阈值模型比线性模型具有优势，但在父系-母系祖父模型中并非如此。对于肉牛产犊难易度的遗传评估，阈值-线性动物模型似乎是预测直接效应和母系效应的最佳选择。
