ArticleGenetics, selection, evolution : GSE2014
Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle.
Article in Genetics, selection, evolution : GSE, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 2 of them syntheses that pooled it.
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Who cites it
86 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data.International journal of molecular sciences · 2022Pooled it
- Large-scale association study on daily weight gain in pigs reveals overlap of genetic factors for growth in humans.BMC genomics · 2022Pooled it
- Body-weight variation as a potential indirect indicator of abdominal fat weight with relevance to laying rate in 90-week-old laying hens.Poultry science · 2026Article
- Genomic Dissection of Growth Traits and Spatial Independence from Breed-Defining Loci in Jining Grey Goats.Animals : an open access journal from MDPI · 2026Article
- Detection of quantitative trait loci and genetic correlations of lipid reserves and production traits in aging laying hens.Poultry science · 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
- Comparison between SNP array and imputed data to estimate population structure and ROH hotspots in horse breeds.BMC genomics · 2025Article
- Sequence-based GWAS in 180,000 German Holstein cattle reveals new candidate variants for milk production traits.Genetics, selection, evolution : GSE · 2025Article
- Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.Genetics, selection, evolution : GSE · 2024Article
- Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes.BMC genomics · 2024Article
- Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes.Genetics, selection, evolution : GSE · 2024Article
- Identification of genetic variants and individual genes associated with postpartum hypocalcemia in Holstein cows.Scientific reports · 2023Article
- Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals.Genetics, selection, evolution : GSE · 2023Article
- Genomic prediction based on selective linkage disequilibrium pruning of low-coverage whole-genome sequence variants in a pure Duroc population.Genetics, selection, evolution : GSE · 2023Article
- GWAS on Imputed Whole-Genome Sequence Variants Reveal Genes Associated with Resistance toGenes · 2022Article
- Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle.Genetics, selection, evolution : GSE · 2022Article
- Evaluation of Whole-Genome Sequence Imputation Strategies in Korean Hanwoo Cattle.Animals : an open access journal from MDPI · 2022Article
- Development and validation of a horse reference panel for genotype imputation.Genetics, selection, evolution : GSE · 2022Article
- Genetic variants associated with two major bovine milk fatty acids offer opportunities to breed for altered milk fat composition.Genetics, selection, evolution : GSE · 2022Article
26 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundThe use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle.
methodsWhole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated.
resultsMean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs.
conclusionsAccuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.
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