Center for Quantitative Genetics and Genomics, Aarhus University, 8000, Aarhus, Denmark.
Faculty of Agricultural Sciences, Agricultural University of Iceland, 311, Borgarbyggð, Iceland.
Genet Sel Evol. 2023 Jul 17;55(1):48. doi: 10.1186/s12711-023-00826-x.
Genomic selection has increased genetic gain in dairy cattle, but in some cases it has resulted in higher inbreeding rates. Therefore, there is need for research on efficient management of inbreeding in genomically-selected dairy cattle populations, especially for local breeds with a small population size. Optimum contribution selection (OCS) minimizes the increase in average kinship while it maximizes genetic gain. However, there is no consensus on how to construct the kinship matrix used for OCS and whether it should be based on pedigree or genomic information. VanRaden's method 1 (VR1) is a genomic relationship matrix in which centered genotype scores are scaled with the sum of 2p(1-p) where p is the reference allele frequency at each locus, and VanRaden's method 2 (VR2) scales each locus with 2p(1-p), thereby giving greater weight to loci with a low minor allele frequency. We compared the effects of nine kinship matrices on genetic gain, kinship, inbreeding, genetic diversity, and minor allele frequency when applying OCS in a simulated small dairy cattle population. We used VR1 and VR2, each using base animals, all genotyped animals, and the current generation of animals to compute reference allele frequencies. We also set the reference allele frequencies to 0.5 for VR1 and the pedigree-based relationship matrix. We constrained OCS to select a fixed number of sires per generation for all scenarios. Efficiency of the different matrices were compared by calculating the rate of genetic gain for a given rate of increase in average kinship.
We found that: (i) genomic relationships were more efficient than pedigree-based relationships at managing inbreeding, (ii) reference allele frequencies computed from base animals were more efficient compared to reference allele frequencies computed from recent animals, and (iii) VR1 was slightly more efficient than VR2, but the difference was not statistically significant.
Using genomic relationships for OCS realizes more genetic gain for a given amount of kinship and inbreeding than using pedigree relationships when the number of sires is fixed. For a small genomic dairy cattle breeding program, we recommend that the implementation of OCS uses VR1 with reference allele frequencies estimated either from base animals or old genotyped animals.
基因组选择已提高了奶牛的遗传增益，但在某些情况下，它导致了更高的近交率。因此，需要研究如何有效地管理基因组选择的奶牛群体中的近交，特别是对于种群规模较小的本地品种。最佳贡献选择（OCS）在最大限度地提高遗传增益的同时，最大限度地减少平均亲缘关系的增加。然而，对于如何构建用于 OCS 的亲缘关系矩阵，以及它是否应该基于谱系或基因组信息，尚无共识。VanRaden 的方法 1（VR1）是一种基因组关系矩阵，其中中心化的基因型得分按 2p(1-p)的总和缩放，其中 p 是每个位点的参考等位基因频率，VanRaden 的方法 2（VR2）按 2p(1-p)缩放每个位点，从而赋予低频等位基因更多的权重。我们比较了在模拟小奶牛群体中应用 OCS 时，九种亲缘关系矩阵对遗传增益、亲缘关系、近交率、遗传多样性和低频等位基因频率的影响。我们使用 VR1 和 VR2，分别使用基础动物、所有基因型动物和当前代动物来计算参考等位基因频率。我们还将 VR1 的参考等位基因频率设置为 0.5，并将基于系谱的关系矩阵设置为 0.5。我们为所有场景都将 OCS 约束为选择每代固定数量的父本。通过计算给定平均亲缘关系增加率的遗传增益率来比较不同矩阵的效率。
我们发现：（i）基因组关系在管理近交方面比基于系谱的关系更有效，（ii）从基础动物计算的参考等位基因频率比从近期动物计算的参考等位基因频率更有效，（iii）VR1 比 VR2 稍微有效，但差异不具有统计学意义。
在固定父本数量的情况下，使用基因组关系进行 OCS 比使用系谱关系可以实现更多的遗传增益和近交。对于一个小的基因组奶牛育种计划，我们建议 OCS 的实施使用 VR1，并使用从基础动物或旧基因型动物估计的参考等位基因频率。
