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

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

doi:10.3168/jds.2021-21360

Abstract

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.

Highlights

Single nucleotide polymorphisms are the most widely used molecular genetic markers for dissecting complex traits and predicting unobserved phenotypes
Among the imputation methods investigated in this study [i.e., Beagle, GeneImp, GLIMPSE, QUILT, Reveel, and STITCH], Reveel resulted in much lower imputation accuracy than the other methods
We compared the performance of 6 methods, Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, for imputation of Low-coverage sequencing (LCS) data with sequencing depth of 1× or lower of Holstein cattle

Summary

Introduction

Single nucleotide polymorphisms are the most widely used molecular genetic markers for dissecting complex traits and predicting unobserved phenotypes. SNP genotyping is based on commercial SNP arrays Another alternative strategy to obtain large-scale HCS data is to perform low-coverage sequencing (LCS) at 1× or less and impute the LCS data to highcoverage WGS data (Li et al, 2011; Zan et al, 2019). It is based on a Hidden Markov model and adapts to the fact that SNPs in sequences are not independent of each other Far, these imputation methods have been successfully applied to LCS data in human and other species (Davies et al, 2016; Nicod et al, 2016; Liu et al, 2018; Jiang et al, 2021; Yang et al, 2021; Zhang et al, 2021), but whether they are suitable for livestock, especially Holstein cattle, has received less attention

Methods

Results

Discussion

Conclusion