Abstract：
As optimization methods to identify the best animals for dense genotyping to construct a reference population for genotype imputation, the MCA and MCG methods, which use the pedigree-based additive genetic relationship matrix (A matrix) and the genomic relationship matrix (G matrix), respectively, have been proposed. We assessed the performance of MCA and MCG methods using 575 Japanese Black cows. Pedigree data were provided to trace back up to five generations to construct the A matrix with changing the pedigree depth from 1 to 5 (five MCA methods). Genotype information on 36,426 single-nucleotide polymorphisms was used to calculate the G matrix based on VanRaden\'s methods 1 and 2 (two MCG methods). The MCG always selected one cow per iteration, while MCA sometimes selected multiple cows. The number of commonly selected cows between the MCA and MCG methods was generally lower than that between different MCA methods or between different MCG methods. For the studied population, MCG appeared to be more reasonable than MCA in selecting cows as a reference population for higher-density genotype imputation to perform genomic prediction and a genome-wide association study.
摘要：
作为优化方法，以确定用于密集基因分型的最佳动物，以构建用于基因型填补的参考种群，MCA和MCG方法，使用基于谱系的加性遗传关系矩阵(A矩阵)和基因组关系矩阵(G矩阵)，分别,已被提议。我们使用575头日本黑牛评估了MCA和MCG方法的性能。提供谱系数据以追溯到五代以构建A矩阵,其中谱系深度从1改变为5(五种MCA方法)。基于VanRaden方法1和2（两种MCG方法），使用36,426个单核苷酸多态性的基因型信息来计算G矩阵。MCG每次迭代总是选择一头牛，而MCA有时会选择多头牛。MCA和MCG方法之间通常选择的母牛的数量通常低于不同MCA方法之间或不同MCG方法之间的数量。对于被研究的人群，MCG似乎比MCA更合理，可以选择奶牛作为参考群体，进行高密度基因型填补，以进行基因组预测和全基因组关联研究。
