Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle

文献类型： 外文期刊

第一作者： Teng, Jun

作者： Teng, Jun;Zhao, Changheng;Wang, Dan;Tang, Hui;Ning, Chao;Zhang, Qin;Chen, Zhi;Yang, Zhangping;Li, Jianbin;Mei, Cheng

作者机构：

关键词： low-coverage sequencing; genotype imputation method; Holstein cattle

期刊名称：JOURNAL OF DAIRY SCIENCE （ 影响因子：4.225； 五年影响因子：4.987 ）

ISSN： 0022-0302

年卷期： 2022 年 105 卷 4 期

页码：

收录情况： SCI

摘要： Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

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