ArticleGenetics, selection, evolution : GSE2023
The size and composition of haplotype reference panels impact the accuracy of imputation from low-pass sequencing in cattle.
Article in Genetics, selection, evolution : GSE, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs.BMC genomics · 2023Pooled it
- Article
- Genomic dissection of hoof and leg conformation in Swiss dairy cattle populations reveals polygenic architecture and a recessive HYAL1 nonsense variant affecting longevity in Holstein cattle.Genetics, selection, evolution : GSE · 2026Article
- Imputation in the Wild: Genome-Wide Robustness and Fine-Scale Limitations of Low-Coverage Genomes in Endangered Species.Molecular ecology resources · 2026Article
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- Across-breed analyses of genome-wide association studies for stature and mammary gland morphology in cattle reveal pleiotropic effects of the Friesian POLLED haplotype.Genetics, selection, evolution : GSE · 2026Article
- Opportunities and computational challenges in large-scale whole-genome sequencing data analysis.Journal of animal science · 2026Review
- Low-Coverage Whole-Genome Sequencing (lcWGS) in Cattle: Analysis of Potential and Prospects for Application.Animals : an open access journal from MDPI · 2025Review
- Low-coverage whole-genome sequencing facilitates accurate and cost-effective haplotype reconstruction in complex mouse crosses.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- High Imputation Accuracy Can Be Achieved Using a Small Reference Panel in a Natural Population With Low Genetic Diversity.Molecular ecology resources · 2025Article
- Assessing Genotype Imputation Methods for Low-Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels.Molecular ecology resources · 2025Article
- Inferring Domestic Goat Demographic History Through Ancient Genome Imputation.Genome biology and evolution · 2025Article
- Fine mapping genetic variants affecting birth weight in sheep: a GWAS of 3007 individuals using low-coverage whole genome sequencing.Journal of animal science and biotechnology · 2025Article
- Overcoming limitations to customize DeepVariant for domesticated animals with TrioTrain.Genome research · 2025Article
- A primer on sequencing and genotype imputation in cattle.Journal of animal science · 2025Review
- Development and application of an updated haplotype reference panel for association analysis of spontaneous sex reversal in XX rainbow trout.Frontiers in genetics · 2025Article
- Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout.G3 (Bethesda, Md.) · 2024Article
- RNA-DNA differences in variant calls from cattle tissues result in erroneous eQTLs.BMC genomics · 2024Article
- A cautionary tale of low-pass sequencing and imputation with respect to haplotype accuracy.Genetics, selection, evolution : GSE · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundLow-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 panels and their interplay with the coverage of low-pass whole-genome sequencing data have not been sufficiently explored in typical livestock settings where only a small number of reference samples is available.
methodsSequence variant genotyping accuracy was compared between two variant callers, GATK and DeepVariant, in 50 Brown Swiss cattle with sequencing coverages ranging from 4- to 63-fold. Haplotype reference panels of varying sizes and composition were built with DeepVariant based on 501 individuals from nine breeds. High-coverage sequence data for 24 Brown Swiss cattle were downsampled to between 0.01- and 4-fold to mimic low-pass sequencing. GLIMPSE was used to infer sequence variant genotypes from the low-pass sequencing data using different haplotype reference panels. The accuracy of the sequence variant genotypes that were inferred from low-pass sequencing data was compared with sequence variant genotypes called from high-coverage data.
resultsDeepVariant was used to establish bovine haplotype reference panels because it outperformed GATK in all evaluations. Within-breed haplotype reference panels were more accurate and efficient to impute sequence variant genotypes from low-pass sequencing than equally-sized multibreed haplotype reference panels for all target sample coverages and allele frequencies. F1 scores greater than 0.9, which indicate high harmonic means of recall and precision of called genotypes, were achieved with 0.25-fold sequencing coverage when large breed-specific haplotype reference panels (n = 150) were used. In absence of such large within-breed haplotype panels, variant genotyping accuracy from low-pass sequencing could be increased either by adding non-related samples to the haplotype reference panel or by increasing the coverage of the low-pass sequencing data. Sequence variant genotyping from low-pass sequencing was substantially less accurate when the reference panel lacked individuals from the target breed.
conclusionsVariant genotyping is more accurate with DeepVariant than GATK. DeepVariant is therefore suitable to establish bovine haplotype reference panels. Medium-sized breed-specific haplotype reference panels and large multibreed haplotype reference panels enable accurate imputation of low-pass sequencing data in a typical cattle breed.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.