Cleveland Matthew A, Hickey John M, Kinghorn Brian P
Genus plc,, 100 Bluegrass Commons Blvd,, Suite 2200, Hendersonville, TN, 37075, USA.
BMC Proc. 2011 May 27;5 Suppl 3(Suppl 3):S6. doi: 10.1186/1753-6561-5-S3-S6.
There is wide interest in calculating genomic breeding values (GEBVs) in livestock using dense, genome-wide SNP data. The general framework for genomic selection assumes all individuals are genotyped at high-density, which may not be true in practice. Methods to add additional genotypes for individuals not genotyped at high density have the potential to increase GEBV accuracy with little or no additional cost. In this study a long haplotype library was created using a long range phasing algorithm and used in combination with segregation analysis to impute dense genotypes for non-genotyped dams in the training dataset (S1) and for non-genotyped or low-density genotyped individuals in the prediction dataset (S2), using the 14th QTL-MAS Workshop dataset. Alternative low-density scenarios were evaluated for accuracy of imputed genotypes and prediction of GEBVs.
In S1, females in the training population were not genotyped and prediction individuals were either not genotyped or genotyped at low-density (evenly spaced at 2, 5 or 10 Mb). The proportion of correctly imputed genotypes for training females did not change when genotypes were added for individuals in the prediction set whereas the number of correctly imputed genotypes in the prediction set increased slightly (S1). The S2 scenario assumed the complete training set was genotyped for all SNPs and the prediction set was not genotyped or genotyped at low-density. The number of correctly imputed genotypes increased with genotyping density in the prediction set. Accuracy of genomic breeding values for the prediction set in each scenario were the correlation of GEBVs with true breeding values and were used to evaluate the potential loss in accuracy with reduced genotyping. For both S1 and S2 the GEBV accuracies were similar when the prediction set was not genotyped and increased with the addition of low-density genotypes, with the increase larger for S2 than S1.
Genotype imputation using a long haplotype library and segregation analysis is promising for application in sparsely-genotyped pedigrees. The results of this study suggest that dense genotypes can be imputed for selection candidates with some loss in genomic breeding value accuracy, but with levels of accuracy higher than traditional BLUP estimated breeding values. Accurate genotype imputation would allow for a single low-density SNP panel to be used across traits.
利用密集的全基因组单核苷酸多态性（SNP）数据计算家畜的基因组育种值（GEBVs）受到广泛关注。基因组选择的一般框架假定所有个体都进行了高密度基因分型，但在实际中可能并非如此。为未进行高密度基因分型的个体添加额外基因型的方法有可能在几乎不增加成本或不增加成本的情况下提高GEBV的准确性。在本研究中，使用长程定相算法创建了一个长单倍型文库，并将其与分离分析结合使用，以推算训练数据集（S1）中未进行基因分型的母畜以及预测数据集（S2）中未进行基因分型或进行低密度基因分型的个体的密集基因型，使用的是第14届QTL-MAS研讨会数据集。评估了替代低密度方案对推算基因型准确性和GEBV预测的影响。
在S1中，训练群体中的雌性未进行基因分型，预测个体要么未进行基因分型，要么进行了低密度基因分型（以2、5或10 Mb的间隔均匀分布）。当为预测集中的个体添加基因型时，训练雌性中正确推算基因型的比例没有变化，而预测集中正确推算基因型的数量略有增加（S1）。S2方案假定完整的训练集对所有SNP进行了基因分型，而预测集未进行基因分型或进行了低密度基因分型。预测集中正确推算基因型的数量随着基因分型密度的增加而增加。每个方案中预测集的基因组育种值准确性是GEBV与真实育种值的相关性，并用于评估基因分型减少时准确性的潜在损失。对于S1和S2，当预测集未进行基因分型时，GEBV准确性相似，并且随着低密度基因型的添加而增加，S2的增加幅度大于S1。
使用长单倍型文库和分离分析进行基因型推算在稀疏基因分型的谱系中具有应用前景。本研究结果表明，可以为选择候选个体推算密集基因型，基因组育种值准确性会有一定损失，但准确性水平高于传统的最佳线性无偏预测（BLUP）估计育种值。准确的基因型推算将允许在不同性状上使用单个低密度SNP面板。
