1Department of Animal and Dairy Science,The University of Georgia,Athens,30602 GA,USA.
3Institute of Bioinformatics,The University of Georgia,Athens,30602 GA,USA.
Animal. 2016 Jul;10(7):1077-85. doi: 10.1017/S1751731115002906. Epub 2016 Jan 6.
Availability of high-density single nucleotide polymorphism (SNP) genotyping platforms provided unprecedented opportunities to enhance breeding programmes in livestock, poultry and plant species, and to better understand the genetic basis of complex traits. Using this genomic information, genomic breeding values (GEBVs), which are more accurate than conventional breeding values. The superiority of genomic selection is possible only when high-density SNP panels are used to track genes and QTLs affecting the trait. Unfortunately, even with the continuous decrease in genotyping costs, only a small fraction of the population has been genotyped with these high-density panels. It is often the case that a larger portion of the population is genotyped with low-density and low-cost SNP panels and then imputed to a higher density. Accuracy of SNP genotype imputation tends to be high when minimum requirements are met. Nevertheless, a certain rate of genotype imputation errors is unavoidable. Thus, it is reasonable to assume that the accuracy of GEBVs will be affected by imputation errors; especially, their cumulative effects over time. To evaluate the impact of multi-generational selection on the accuracy of SNP genotypes imputation and the reliability of resulting GEBVs, a simulation was carried out under varying updating of the reference population, distance between the reference and testing sets, and the approach used for the estimation of GEBVs. Using fixed reference populations, imputation accuracy decayed by about 0.5% per generation. In fact, after 25 generations, the accuracy was only 7% lower than the first generation. When the reference population was updated by either 1% or 5% of the top animals in the previous generations, decay of imputation accuracy was substantially reduced. These results indicate that low-density panels are useful, especially when the generational interval between reference and testing population is small. As the generational interval increases, the imputation accuracies decay, although not at an alarming rate. In absence of updating of the reference population, accuracy of GEBVs decays substantially in one or two generations at the rate of 20% to 25% per generation. When the reference population is updated by 1% or 5% every generation, the decay in accuracy was 8% to 11% after seven generations using true and imputed genotypes. These results indicate that imputed genotypes provide a viable alternative, even after several generations, as long the reference and training populations are appropriately updated to reflect the genetic change in the population.
高密度单核苷酸多态性 (SNP) 基因分型平台的出现为增强家畜、家禽和植物物种的育种计划以及更好地理解复杂性状的遗传基础提供了前所未有的机会。利用这些基因组信息，可以获得比传统育种值更准确的基因组育种值 (GEBV)。只有使用高密度 SNP 面板跟踪影响性状的基因和 QTL，基因组选择的优越性才成为可能。不幸的是，即使基因分型成本不断降低，也只有一小部分人群使用这些高密度面板进行了基因分型。通常情况下，更大一部分人群使用低密度和低成本的 SNP 面板进行基因分型，然后推断为更高的密度。只要满足最低要求，SNP 基因型推断的准确性就很高。然而，基因型推断错误的一定比率是不可避免的。因此，可以合理假设 GEBV 的准确性将受到推断错误的影响；特别是，随着时间的推移，它们的累积效应。为了评估多世代选择对 SNP 基因型推断准确性和由此产生的 GEBV 可靠性的影响，在不同的参考群体更新、参考集和测试集之间的距离以及用于估计 GEBV 的方法下进行了模拟。使用固定的参考群体，每代推断准确性下降约 0.5%。实际上，经过 25 代后，准确性仅比第一代低 7%。当参考群体按前几代中前 1%或 5%的最佳动物进行更新时，推断准确性的下降幅度大大降低。这些结果表明，低密度面板是有用的，尤其是当参考群体和测试群体之间的世代间隔较小时。随着世代间隔的增加，推断准确性会下降，尽管速度并不快。在没有更新参考群体的情况下，GEBV 的准确性会在一两代内以每代 20%至 25%的速度大幅下降。当每代参考群体更新 1%或 5%时，使用真实和推断基因型，七代后准确性的下降幅度为 8%至 11%。这些结果表明，即使经过几代，只要适当更新参考和训练群体以反映群体中的遗传变化，推断基因型仍然是一种可行的替代方案。
