Yu Haipeng, Spangler Matthew L, Lewis Ronald M, Morota Gota
Department of Animal Science, University of Nebraska-Lincoln, Nebraska 68583.
Department of Animal Science, University of Nebraska-Lincoln, Nebraska 68583
G3 (Bethesda). 2017 Oct 5;7(10):3543-3556. doi: 10.1534/g3.117.300151.
Genetic connectedness refers to a measure of genetic relatedness across management units (, herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation r to a combination of computer simulation and real data (mice and cattle). We found that genomic information ([Formula: see text]) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree ([Formula: see text]). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using [Formula: see text]- rather than [Formula: see text]-based relatedness, suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling [Formula: see text] to values between 0 and 2, which is intrinsic to [Formula: see text] connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of [Formula: see text] instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.
遗传连通性是指衡量各管理单元（如畜群和禽群）之间的遗传相关性。已知在管理单元中存在高遗传连通性时，最佳线性无偏预测（BLUP）能够在估计的遗传值之间提供可靠的比较。遗传连通性已针对基于系谱的BLUP进行了研究；然而，利用基因组信息来衡量连通性的研究相对较少。在本研究中，我们通过将差异预测误差方差（PEVD）、决定系数（CD）和预测误差相关性r应用于计算机模拟和实际数据（小鼠和牛）的组合，评估了各管理单元之间基于基因组的连通性。我们发现，与基于系谱的情况相比，基因组信息（[公式：见原文]）提高了不同管理单元个体之间连通性的估计值。不连通的设计受益最大。在两个数据集中，当使用基于[公式：见原文]而非基于[公式：见原文]的亲缘关系时，PEVD和CD统计量推断各单元之间的连通性增加，表明连通性更强。对于r，一旦使用等于二分之一的等位基因频率或将[公式：见原文]缩放到0到2之间的值（这是[公式：见原文]所固有的），连通性也会随着基因组信息而增加。然而，当使用[公式：见原文]的替代形式获得时，PEVD偶尔会增加，而r会降低，这表明连通性较低。CD未发现此类不一致情况。我们认为，基因组亲缘关系加强了各单元之间遗传连通性的度量，并且有潜力辅助家畜物种的基因组评估。
