Experiences from international sire evaluation indicate that the multiple-trait across-country evaluation method is sensitive to changes in genetic variance over time. Top bulls from birth year classes with inflated genetic variance will benefit, hampering reliable ranking of bulls. However, none of the methods available today enable countries to validate their national evaluation models for heterogeneity of genetic variance. We describe a new validation method to fill this gap comprising the following steps: estimating within-year genetic variances using Mendelian sampling and its prediction error variance, fitting a weighted linear regression between the estimates and the years under study, identifying possible outliers, and defining a 95% empirical confidence interval for a possible trend in the estimates. We tested the specificity and sensitivity of the proposed validation method with simulated data using a real data structure. Moderate (M) and small (S) size populations were simulated under 3 scenarios: a control with homogeneous variance and 2 scenarios with yearly increases in phenotypic variance of 2 and 10%, respectively. Results showed that the new method was able to estimate genetic variance accurately enough to detect bias in genetic variance. Under the control scenario, the trend in genetic variance was practically zero in setting M. Testing cows with an average birth year class size of more than 43,000 in setting M showed that tolerance values are needed for both the trend and the outlier tests to detect only cases with a practical effect in larger data sets. Regardless of the magnitude (yearly increases in phenotypic variance of 2 or 10%) of the generated trend, it deviated statistically significantly from zero in all data replicates for both cows and bulls in setting M. In setting S with a mean of 27 bulls in a year class, the sampling error and thus the probability of a false-positive result clearly increased. Still, overall estimated genetic variance was close to the parametric value. Only rather strong trends in genetic variance deviated statistically significantly from zero in setting S. Results also showed that the new method was sensitive to the quality of the approximated reliabilities of breeding values used in calculating the prediction error variance. Thus, we recommend that only animals with a reliability of Mendelian sampling higher than 0.1 be included in the test and that low heritability traits be analyzed using bull data sets only.

国际父亲评估的经验表明，跨国家多特征评估方法对遗传变异随时间的变化敏感。遗传变异偏高的出生年份的顶级公牛将受益，从而影响公牛的可靠排名。但是，当今没有可用的方法使各国能够验证其关于遗传变异异质性的国家评估模型。我们描述了一种填补这一空白的新验证方法，包括以下步骤：使用孟德尔采样及其预测误差方差估算年内遗传方差，拟合估算值与研究年份之间的加权线性回归，确定可能的离群值，并定义估计中可能趋势的95％经验置信区间。我们使用真实的数据结构通过模拟数据测试了所提出的验证方法的特异性和敏感性。在3种情况下模拟了中型（M）和小型（S）人群：具有均质方差的对照和具有2％和10％的表型方差逐年增加的2种情况。结果表明，该新方法能够足够准确地估计遗传变异，以检测遗传变异中的偏倚。在控制情景下，设置M时遗传变异的趋势几乎为零。在设置M中测试平均出生年胎龄超过43,000的奶牛表明，趋势和离群测试都需要公差值才能仅检测在较大数据集中具有实际效果的案例。不论生成趋势的大小（表型方差每年增加2或10％），在M设置的母牛和公牛的所有数据重复样本中，它在统计学上均显着偏离零。在S设置的情况下，S的平均为27头一年级时，抽样误差以及由此产生假阳性结果的可能性明显增加。尽管如此，总体估计的遗传方差仍接近参数值。在设置S中，只有相当大的遗传方差趋势在统计上与零显着偏离。结果还表明，该新方法对用于计算预测误差方差的育种值的近似可靠性的质量敏感。因此，我们建议仅孟德尔采样的可靠性高于0的动物。