This study aimed to evaluate the changes in variance components over time to identify a subset of data from the Italian Simmental (IS) population that would yield the most appropriate estimates of genetic parameters and breeding values for beef traits to select young bulls. Data from bulls raised between 1986 and 2017 were used to estimate genetic parameters and breeding values for four beef traits (average daily gain [ADG], body size [BS], muscularity [MUS], and feet and legs [FL]). The phenotypic mean increased during the years of the study for ADG, but it decreased for BS, MUS, and FL. The complete dataset (ALL) was divided into four generational subsets (Gen1, Gen2, Gen3, and Gen4). Additionally, ALL was divided into two larger subsets: the first one (OLD) combined data from Gen1 and Gen2 to represent the starting population, and the second one (CUR) combined data from Gen3 and Gen4 to represent a subpopulation with stronger ties to the current population. Genetic parameters were estimated with a four-trait genomic animal model using a single-step genomic average information restricted maximum likelihood algorithm. Heritability estimates from ALL were 0.26 ± 0.03 for ADG, 0.33 ± 0.04 for BS, 0.55 ± 0.03 for MUS, and 0.23 ± 0.03 for FL. Higher heritability estimates were obtained with OLD and ALL than with CUR. Considerable changes in heritability existed between Gen1 and Gen4 due to fluctuations in both additive genetic and residual variances. Genetic correlations also changed over time, with some values moving from positive to negative or even to zero. Genetic correlations from OLD were stronger than those from CUR. Changes in genetic parameters over time indicated that they should be updated regularly to avoid biases in genomic estimated breeding values (GEBV) and low selection accuracies. GEBV estimated using CUR variance components were less biased and more consistent than those estimated with OLD and ALL variance components. Validation results indicated that data from recent generations produced genetic parameters that more appropriately represent the structure of the current population, yielding accurate GEBV to select young animals and increasing the likelihood of higher genetic gains.

中文翻译：

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基于旧数据和近期数据的牛肉性状遗传参数及其对意大利西门塔尔牛基因组预测的影响。

本研究旨在评估方差分量随时间的变化，以确定意大利西门塔尔 ( IS ) 种群的数据子集，这些数据将产生最合适的牛肉性状遗传参数和育种值估计值，以选择年轻公牛。来自 1986 年至 2017 年间饲养的公牛的数据用于估计四种牛肉性状（平均日增重 [ ADG ]、体型 [ BS ]、肌肉发达 [ MUS ] 和脚和腿 [ FL ]）的遗传参数和育种值。在 ADG 的研究期间，表型平均值增加，但 BS、MUS 和 FL 的表型平均值下降。完整的数据集 ( ALL ) 被分为四个世代子集(Gen1，第2代，3代和GEN4）。此外，ALL 被分为两个更大的子集：第一个子集 ( OLD ) 组合来自 Gen1 和 Gen2 的数据来表示起始种群，第二个子集( CUR) 结合来自 Gen3 和 Gen4 的数据来表示与当前种群有更强联系的亚种群。使用单步基因组平均信息限制最大似然算法，通过四性状基因组动物模型估计遗传参数。ALL 的遗传力估计值对于 ADG 为 0.26 ± 0.03，对于 BS 为 0.33 ± 0.04，对于 MUS 为 0.55 ± 0.03，对于 FL 为 0.23 ± 0.03。OLD 和 ALL 比 CUR 获得更高的遗传力估计值。由于加性遗传和残差方差的波动，Gen1 和 Gen4 之间的遗传力存在相当大的变化。遗传相关性也随着时间发生变化，一些值从正变为负甚至为零。OLD 的遗传相关性强于 CUR 的遗传相关性。GEBV）和低选择精度。使用 CUR 方差分量估计的 GEBV 比使用 OLD 和 ALL 方差分量估计的那些偏差更小，更一致。验证结果表明，来自最近几代的数据产生的遗传参数更能代表当前种群的结构，产生准确的 GEBV 以选择年轻动物并增加更高遗传收益的可能性。