ArticleFrontiers in genetics2021
Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.
Article in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Multi-ancestry genome-wide association meta-analysis identifies candidate genes for computed tomography-based carcass composition traits in pigs.Genetics, selection, evolution : GSE · 2025Pooled it
- GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data.International journal of molecular sciences · 2022Pooled it
- Low-Coverage Whole-Genome Resequencing in Livestock and Poultry: Statistical Foundations, Applications and Future Directions.Biology · 2026Review
- Integrative multi-omics analysis identifies GNAZ and SLC5A4 as critical regulators of gestation length in Large White pigs.Genetics, selection, evolution : GSE · 2026Article
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- Dissection of local haplotype diversity at soybean rust loci reveals resistance-associated and context-dependent variation patterns in diverse germplasm.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- High-quality Population-specific Haplotype-resolved Reference Panel in the Genomic and Pangenomic Eras.Genomics, proteomics & bioinformatics · 2025Review
- 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
- Establishing Best Practices for Clinical GWAS: Tackling Imputation and Data Quality Challenges.International journal of molecular sciences · 2025Review
- Whole genome and transcriptome analyses identify genetic markers associated with growth traits in Qinchuan black pig.BMC genomics · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- STICI: Split-Transformer with integrated convolutions for genotype imputation.Nature communications · 2025Article
- Genomic Landscape and Prediction of Udder Traits in Saanen Dairy Goats.Animals : an open access journal from MDPI · 2025Article
- A primer on sequencing and genotype imputation in cattle.Journal of animal science · 2025Review
- Genomic analyses of withers height and linear conformation traits in German Warmblood horses using imputed sequence-level genotypes.Genetics, selection, evolution : GSE · 2024Article
- AGIDB: a versatile database for genotype imputation and variant decoding across species.Nucleic acids research · 2024Article
- Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations.Cell genomics · 2023Article
- CRISPR-Cas9-based repeat depletion for high-throughput genotyping of complex plant genomes.Genome research · 2023Article
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6 authors.
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Abstract
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.
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