Zhao Changheng, Teng Jun, Zhang Xinhao, Wang Dan, Zhang Xinyi, Li Shiyin, Jiang Xin, Li Haijing, Ning Chao, Zhang Qin
Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China.
National Engineering Research Center for Gelatin-based TCM, Dong-E E-Jiao Co., Ltd., Dong'e County, China.
Front Genet. 2021 Nov 3;12:728764. doi: 10.3389/fgene.2021.728764. eCollection 2021.
Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.
低覆盖度全基因组测序是一种低成本的基因分型技术。结合基因型填充方法，它很可能成为农业家畜中具有成本效益的基因组选择计划的关键组成部分。在此，我们使用617头德州驴的低覆盖度序列数据，研究低覆盖度全基因组序列数据的基因型填充性能以及基于填充后的基因型数据的基因组预测。具体目标如下：1）测量不同测序深度、样本量、次要等位基因频率（MAF）和填充流程下基因型填充的准确性；2）评估在来自填充序列数据的不同标记密度、构建基因组关系矩阵的不同策略以及单性状与多性状模型下基因组预测的准确性。我们发现，对于测序深度低至1x且测序个体数量≥400的序列数据，可以实现较高的填充准确性（>0.95）。对于基因组预测，使用410K的标记密度和基于预期标记剂量构建的G矩阵可获得最佳性能。多性状基因组最佳线性无偏预测（GBLUP）比单性状GBLUP表现更好。我们的研究表明，低覆盖度全基因组测序将是德州驴基因组预测的一种具有成本效益的方法。
