ArticlePloS one2022
A comparative analysis of current phasing and imputation software.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure.Briefings in bioinformatics · 2022Pooled it
- Selphi, a tool for improving genotype imputation accuracy.Scientific reports · 2026Article
- A Benchmark of Modern Statistical Phasing Methods.bioRxiv : the preprint server for biology · 2026Article
- A rapid review of genetic association studies of parent-of-origin effects and fetal growth.Molecular and cellular pediatrics · 2026Review
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- Integration and imputation of GBS-derived and DArTseq-derived SNP markers in assessing genetic diversity of bread wheat genotypes.BMC genomics · 2026Article
- Chimeric reference panels for genomic imputation.Genetics · 2026Article
- Removing array-specific batch effects in GWAS mega-analyses by applying a two-step imputation workflow.Bioinformatics advances · 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
- Boosting the Power of Rare Variant Association Studies by Imputation Using Large-scale Sequencing Population.Genomics, proteomics & bioinformatics · 2025Article
- Imputef: imputation of polyploid genotype classes and allele frequencies.BMC genomics · 2025Article
- Identifying individuals with rare disease variants by inferring shared ancestral haplotypes from SNP array data.NAR genomics and bioinformatics · 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 vision of how low-coverage sequence data should contribute to genetic evaluation in the future.Journal of animal science · 2025Article
- Empirical versus estimated accuracy of imputation: optimising filtering thresholds for sequence imputation.Genetics, selection, evolution : GSE · 2024Article
- Article
- Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology.Journal of human genetics · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole-genome data has become significantly more accessible over the last two decades. This can largely be attributed to both reduced sequencing costs and imputation models which make it possible to obtain nearly whole-genome data from less expensive genotyping methods, such as microarray chips. Although there are many different approaches to imputation, the Hidden Markov Model (HMM) remains the most widely used. In this study, we compared the latest versions of the most popular HMM-based tools for phasing and imputation: Beagle5.4, Eagle2.4.1, Shapeit4, Impute5 and Minimac4. We benchmarked them on four input datasets with three levels of chip density. We assessed each imputation software on the basis of accuracy, speed and memory usage, and showed how the choice of imputation accuracy metric can result in different interpretations. The highest average concordance rate was achieved by Beagle5.4, followed by Impute5 and Minimac4, using a reference-based approach during phasing and the highest density chip. IQS and R2 metrics revealed that Impute5 and Minimac4 obtained better results for low frequency markers, while Beagle5.4 remained more accurate for common markers (MAF>5%). Computational load as measured by run time was lower for Beagle5.4 than Minimac4 and Impute5, while Minimac4 utilized the least memory of the imputation tools we compared. ShapeIT4, used the least memory of the phasing tools examined with genotype chip data, while Eagle2.4.1 used the least memory phasing WGS data. Finally, we determined the combination of phasing software, imputation software, and reference panel, best suited for different situations and analysis needs and created an automated pipeline that provides a way for users to create customized chips designed to optimize their imputation results.
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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.