4.7 Article

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

Journal

JOURNAL OF DAIRY SCIENCE
Volume 105, Issue 4, Pages 3355-3366

Publisher

ELSEVIER SCIENCE INC
DOI: 10.3168/jds.2021-21360

Keywords

low-coverage sequencing; genotype imputation method; Holstein cattle

Funding

  1. Yangzhou Univer-sity Interdisciplinary Research Foundation for Animal Science Discipline of Targeted Support (Yangzhou, China) [yzuxk202016]
  2. Project of Genetic Improvement for Agricultural Species (Dairy Cattle) of Shandong Province (Jinan, China) [2019LZGC011]
  3. Shandong Provincial Natural Science Foundation (Jinan, China) [ZR2020QC175, ZR2020QC176]
  4. National Natural Science Foundation of China (Beijing) [32002172]

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This study evaluated the performance of several imputation methods in Holstein cattle using LCS data and found that GLIMPSE, QUILT, and STITCH performed better than other methods, with an imputation accuracy over 0.9.
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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