ArticleGenetics, selection, evolution : GSE2021
Imputation accuracy to whole-genome sequence in Nellore cattle.
Article in Genetics, selection, evolution : GSE, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 56 citations in OpenAlex.
- Resolving Cattle GWAS Loci: Current Progress, Persistent Challenges and Future Directions.Current issues in molecular biology · 2026Review
- Multi-trait GWAS of reaction norm parameters reveals environment-responsive loci influencing reproductive performance in heifers.Journal of animal science and biotechnology · 2026Article
- Enhancing genomic prediction for key production traits in chickens through ultrasound phenotyping and multi-model comparative analysis.Journal of animal science and biotechnology · 2026Article
- Elucidating genetic backgrounds of myasthenia gravis in Japanese by genome-wide association studies and multi-omics analyses of thymoma.Nature communications · 2026Article
- Genotype imputation performance in Nellore cattle across different SNP panels and software tools.Tropical animal health and production · 2026Article
- The Spanish Polygenic Score reference distribution: a resource for personalized medicine.European journal of human genetics : EJHG · 2026Article
- Sequence-based genome-wide association study reveals genetic and metabolic mechanisms underlying feed efficiency-related traits in beef cattle.Journal of animal science and biotechnology · 2026Article
- Whole-genome sequencing GWAS reveals bovine genomic effects on enteric methane emissions in beef cattle.Scientific reports · 2025Article
- KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.International journal of molecular sciences · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- Genomic Prediction of Milk Fat Percentage Among Crossbred Cattle in the Indian Subcontinent.Animals : an open access journal from MDPI · 2025Article
- Comparative analysis of genotype imputation strategies for SNPs calling from RNA-seq.BMC genomics · 2025Article
- Using genotype imputation to integrate Canola populations for genome-wide association and genomic prediction of blackleg resistance.BMC genomics · 2025Article
- A primer on sequencing and genotype imputation in cattle.Journal of animal science · 2025Review
- Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.Genetics, selection, evolution : GSE · 2024Article
- Genome-Wide Association Study as an Efficacious Approach to Discover Candidate Genes Associated with Body Linear Type Traits in Dairy Cattle.Animals : an open access journal from MDPI · 2024Review
- Genome-wide association study considering genotype-by-environment interaction for productive and reproductive traits using whole-genome sequencing in Nellore cattle.BMC genomics · 2024Article
- Imputation strategies for genomic prediction using nanopore sequencing.BMC biology · 2023Article
- Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs.Journal of animal science and biotechnology · 2023Article
- Animal-SNPAtlas: a comprehensive SNP database for multiple animals.Nucleic acids research · 2023Article
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundA cost-effective strategy to explore the complete DNA sequence in animals for genetic evaluation purposes is to sequence key ancestors of a population, followed by imputation mechanisms to infer marker genotypes that were not originally reported in a target population of animals genotyped with single nucleotide polymorphism (SNP) panels. The feasibility of this process relies on the accuracy of the genotype imputation in that population, particularly for potential causal mutations which may be at low frequency and either within genes or regulatory regions. The objective of the present study was to investigate the imputation accuracy to the sequence level in a Nellore beef cattle population, including that for variants in annotation classes which are more likely to be functional.
methodsInformation of 151 key sequenced Nellore sires were used to assess the imputation accuracy from bovine HD BeadChip SNP (~ 777 k) to whole-genome sequence. The choice of the sires aimed at optimizing the imputation accuracy of a genotypic database, comprised of about 10,000 genotyped Nellore animals. Genotype imputation was performed using two computational approaches: FImpute3 and Minimac4 (after using Eagle for phasing). The accuracy of the imputation was evaluated using a fivefold cross-validation scheme and measured by the squared correlation between observed and imputed genotypes, calculated by individual and by SNP. SNPs were classified into a range of annotations, and the accuracy of imputation within each annotation classification was also evaluated.
resultsHigh average imputation accuracies per animal were achieved using both FImpute3 (0.94) and Minimac4 (0.95). On average, common variants (minor allele frequency (MAF) > 0.03) were more accurately imputed by Minimac4 and low-frequency variants (MAF ≤ 0.03) were more accurately imputed by FImpute3. The inherent Minimac4 Rsq imputation quality statistic appears to be a good indicator of the empirical Minimac4 imputation accuracy. Both software provided high average SNP-wise imputation accuracy for all classes of biological annotations.
conclusionsOur results indicate that imputation to whole-genome sequence is feasible in Nellore beef cattle since high imputation accuracies per individual are expected. SNP-wise imputation accuracy is software-dependent, especially for rare variants. The accuracy of imputation appears to be relatively independent of annotation classification.
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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.