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

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

doi:10.3168/jds.2021-21360

Cited by 15 publications

(18 citation statements)

References 47 publications

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“…At a sequencing depth of 1.0X, Base-Var + STITCH reached a high imputation accuracy with a GC > 98.84% and GA > 0.97 when sample size was larger than 300. The patterns of imputation accuracy obtained in this study with the different pipelines and with different levels of sequencing depth and sample sizes are in line with results from previous studies [11,26,57,58].…”

Section: Discussionsupporting

confidence: 90%

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Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

et al. 2022

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Background Rabbit wool traits are important in fiber production and for model organism research on hair growth, but their genetic architecture remains obscure. In this study, we focused on wool characteristics in Angora rabbits, a breed well-known for the quality of its wool. Considering the cost to generate population-scale sequence data and the biased detection of variants using chip data, developing an effective genotyping strategy using low-coverage whole-genome sequencing (LCS) data is necessary to conduct genetic analyses. Results Different genotype imputation strategies (BaseVar + STITCH, Bcftools + Beagle4, and GATK + Beagle5), sequencing coverages (0.1X, 0.5X, 1.0X, 1.5X, and 2.0X), and sample sizes (100, 200, 300, 400, 500, and 600) were compared. Our results showed that using BaseVar + STITCH at a sequencing depth of 1.0X with a sample size larger than 300 resulted in the highest genotyping accuracy, with a genotype concordance higher than 98.8% and genotype accuracy higher than 0.97. We performed multivariate genome-wide association studies (GWAS), followed by conditional GWAS and estimation of the confidence intervals of quantitative trait loci (QTL) to investigate the genetic architecture of wool traits. Six QTL were detected, which explained 0.4 to 7.5% of the phenotypic variation. Gene-level mapping identified the fibroblast growth factor 10 (FGF10) gene as associated with fiber growth and diameter, which agrees with previous results from functional data analyses on the FGF gene family in other species, and is relevant for wool rabbit breeding. Conclusions We suggest that LCS followed by imputation can be a cost-effective alternative to array and high-depth sequencing for assessing common variants. GWAS combined with LCS can identify new QTL and candidate genes that are associated with quantitative traits. This study provides a cost-effective and powerful method for investigating the genetic architecture of complex traits, which will be useful for genomic breeding applications.

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Section: Discussionsupporting

confidence: 90%

“…According to Teng et al [26], when imputation is done with STITCH, it is necessary to complete the remaining missing variants using Beagle. Thus, we also carried out such two-stage imputation, i.e., "Basevar-Stitch-Beagle5".…”

Section: Selection Of Tagging Snps and Annotationmentioning

confidence: 99%

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

et al. 2022

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“…The differences appear to be more pronounced for reference haplotype panels that are of different breed to the target sample and at lower coverages ( i . e ., less than 0.25-fold coverage, where accuracies estimated by GLIMPSE’s are inaccurate (6)). While, for example, multibreed panels are near equally accurate to the 150 sample BSW panel, the INFO scores are noticeably lower.…”

Section: Discussionmentioning

confidence: 99%

Size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle

Lloret-Villas

Pausch

Leonard

2023

Preprint

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Motivation: Low-pass sequencing followed by sequence variant genotype imputation is an alternative to the routine microarray-based genotyping in cattle. However, the impact of haplotype reference panel composition and its interplay with the coverage of low-pass whole-genome sequencing data has not been sufficiently explored in typical livestock settings where only a small number of reference samples are available. Results: Benchmarking against a microarray truth set confirms that DeepVariant is a suitable variant caller to establish bovine haplotype reference panels. We build haplotype reference panels of varying sizes and composition with DeepVariant and use GLIMPSE to impute genotypes for cattle that had between 0.01- and 4-fold genome sequence data. Same-breed haplotype reference panels perform better than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9 can be achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) are used. In absence of such large same-breed haplotype panels, sequence variant genotyping accuracy can be increased either by adding non-related samples or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low pass sequencing is substantially less accurate when the reference panel lacks individuals from the target breed. Availability: Sequencing data are publicly available as indicated in the Supplementary Files. Scripts and workflows are available on Github: https://github.com/AnimalGenomicsETH/Low_pass_imputation. Contact: avillas@ethz.ch

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“…Low‐coverage resequencing can reduce the cost of genotyping and has broad application prospects. Studies have shown that when sequencing depth is less than or equal to 1X, the use of genotype imputation can produce genomic prediction accuracy similar to high‐coverage resequencing in pig ( Sus scrofa ) 23 and dairy cattle ( Bos taurus ) 24 . Zhang et al 25 compared the effect of different sequencing depths on the accuracy of genome prediction in large yellow croaker ( Larimichthys crocea ) populations and noted that the genomic prediction accuracy obtained using 0.5X was similar to that obtained using 8X.…”

Section: Technology Of Gsmentioning

confidence: 99%

“…Studies have shown that when sequencing depth is less than or equal to 1X, the use of genotype imputation can produce genomic prediction accuracy similar to highcoverage resequencing in pig (Sus scrofa) 23 and dairy cattle (Bos taurus). 24 Zhang et al 25 compared the effect of different sequencing depths on the accuracy of genome prediction in large yellow croaker (Larimichthys crocea) populations and noted that the genomic prediction accuracy obtained using 0.5X was similar to that obtained using 8X. In addition, genotyping by sequencing (GBS) has allowed for rapid advances in aquaculture genetics and breeding to date, as reviewed by Robledo et al 26 In particular, restriction site-associated DNA sequencing (RAD-Seq) has been widely used to generate populationlevel SNP data.…”

Section: Precondition For Gsmentioning

confidence: 99%

Genomic selection and its research progress in aquaculture breeding

Song

Tian

Yan

et al. 2022

Reviews in Aquaculture

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Since its introduction in 2001, genomic selection (GS) has progressed rapidly. As a research and application hot topic, GS has led to a revolution in the field of animal and plant breeding. Thanks to its ability to overcome the shortcomings of traditional breeding methods, GS has garnered increasing attention. Both theoretical and practical breeding studies have revealed the higher accuracy of GS than that of traditional breeding, which can accelerate genetic gain. In recent years, many GS studies have been conducted on aquaculture species, which have shown that GS produces higher prediction accuracy than traditional pedigree-based method. The present study reviews the principles and processes, preconditions, advantages, analytical methods and factors influencing GS as well as the progress of research in aquaculture into these aspects. Furthermore, future directions of GS in aquaculture are also discussed, which should expand its application to more aquaculture species.

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

Cited by 15 publications

References 47 publications

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing

Size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle

Genomic selection and its research progress in aquaculture breeding

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