Detection of evaluation bias caused by genomic preselection
Detection of evaluation bias caused by genomic preselection
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DOI：
10.3168/jds.2017-13527
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发表时间：
2018-04-01
影响因子：
3.5
通讯作者：
Lidauer, M. H.
中科院分区：
文献类型：
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作者：
Tyriseva, A. -M.;Mantysaari, E. A.;Lidauer, M. H.
本模拟研究的目的是调查是否有可能检测基因组预选对孟德尔采样（MS）的平均值或方差的影响，通过MS验证测试。公牛犊的基因组预选是国际评价中的另一个潜在偏倚来源，除非在国家评价中充分考虑。如果包括所有动物的数据，选择在传统育种价值评估中不会产生偏差。然而，基因组预选的情况并非如此，因为它排除了被淘汰的公牛。如果使用多步骤程序而不是例如单步方法计算，基因组育种值将变得有偏差。目前，大约60%参与国际公牛评估的国家已经在其育种计划中采用了基因组选择。因此，发送用于多次跨国评估的数据可能非常异质，需要适当的验证方法来确保对国际遗传评估中包含的公牛进行公平比较。为了研究基因组预选的效果，我们使用来自中等规模奶牛群体的真实的数据和系谱的结构，在对照和基因组预选方案下产生了总共50个重复。两种方案的遗传趋势均为遗传标准差的15%。在进行分析时，我们使用了两种不同的遗传力：0.25和0.10。从基因组预选开始，所有公牛都是基因组预选的。它们的MS偏差用对应于选择最佳10%基因组测试的公牛犊的值来放大。对于奶牛，MS偏差未改变。结果表明，基因组预选开始后，公牛的育种值（BV）明显低估，以及在真实和估计的MS平均值都明显偏离零。最近为MS验证测试开发的软件已经产生了每年的MS平均值，并且它们可以用于设计适当的测试。基因组预选公牛的均方真实MS明显被夸大了。校正模拟预选偏倚后，真实遗传方差小于用于模拟BV的参数值，也低于基于估计BV的方差。基于这项研究，该特征的遗传力越低，估计的BV和MS均值和方差的偏差就越强。基因组预选公牛的女儿有更高的真实和估计的BV与对照方案相比，只有轻微升高的MS手段，但没有观察到对遗传方差的影响。
The aim of this simulation study was to investigate whether it is possible to detect the effect of genomic preselection on Mendelian sampling (MS) means or variances obtained by the MS validation test. Genomic preselection of bull calves is 1 additional potential source of bias in international evaluations unless adequately accounted for in national evaluations. Selection creates no bias in traditional breeding value evaluation if the data of all animals are included. However, this is not the case with genomic preselection, as it excludes culled bulls. Genomic breeding values become biased if calculated using a multistep procedure instead of, for example, a single-step method. Currently, about 60% of the countries participating in international bull evaluations have already adopted genomic selection in their breeding schemes. The data sent for multiple across-country evaluation can, therefore, be very heterogeneous, and a proper validation method is needed to ensure a fair comparison of the bulls included in international genetic evaluations. To study the effect of genomic preselection, we generated a total of 50 replicates under control and genomic preselection schemes using the structures of the real data and pedigree from a medium-size cow population. A genetic trend of 15% of the genetic standard deviation was created for both schemes. In carrying out the analyses, we used 2 different heritabilities: 0.25 and 0.10. From the start of genomic preselection, all bulls were genomically preselected. Their MS deviations were inflated with a value corresponding to selection of the best 10% of genomically tested bull calves. For cows, the MS deviations were unaltered. The results revealed a clear underestimation of bulls' breeding values (BV) after genomic preselection started, as well as a notable deviation from zero both in true and estimated MS means. The software developed recently for the MS validation test already produces yearly MS means, and they can be used to devise an appropriate test. Mean squared true MS of genomically preselected bulls was clearly inflated. After correcting for the simulated preselection bias, the true genetic variance was smaller than the parametric value used to simulate BV, and also below the variance based on the estimated BV. Based on this study, the lower the trait's heritability, the stronger the bias in estimated BV and MS means and variances. Daughters of genomically preselected bulls had higher true and estimated BV compared with the control scheme and only slightly elevated MS means, but no effect on genetic variances was observed.