ArticleG3 (Bethesda, Md.)2022
An empirical evaluation of genotype imputation of ancient DNA.
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Ancient wheat DNA: How and what for?Journal, genetic engineering & biotechnology · 2026Article
- Unified Multi-Caller Ensemble (UME) Generates an Unbiased Maize Haplotype Map for Variable Coverage Whole Genome Data.Molecular ecology resources · 2026Article
- Optimal Reference Panel Design in Ancient DNA Imputation from Coalescent Theory, Simulation, and Real Data Application with an Ancient Reference Panel.bioRxiv : the preprint server for biology · 2026Article
- MetaGLIMPSE: Meta-imputation of low-coverage sequencing data for modern and ancient genomes.American journal of human genetics · 2026Article
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
- The missing data problem in population genomics and statistical methods to address them.G3 (Bethesda, Md.) · 2026Article
- Predicting Physical Appearance from Low Template: State of the Art and Future Perspectives.Genes · 2026Review
- High-quality Population-specific Haplotype-resolved Reference Panel in the Genomic and Pangenomic Eras.Genomics, proteomics & bioinformatics · 2025Review
- Inferring Domestic Goat Demographic History Through Ancient Genome Imputation.Genome biology and evolution · 2025Article
- Genotype imputation from low-coverage data for medical and population genetic analyses.Genome research · 2025Article
- MetaGLIMPSE: Meta Imputation of Low Coverage Sequencing Data for Modern and Ancient Genomes.bioRxiv : the preprint server for biology · 2025Article
- The genomic footprints of migration: how ancient DNA reveals our history of mobility.Genome biology · 2025Review
- Evolutionary Influences on Local Patterns of Genetic Relatedness.bioRxiv : the preprint server for biology · 2025Article
- A probabilistic approach to visualize the effect of missing data on PCA in ancient human genomics.BMC genomics · 2025Article
- Pre-processing of paleogenomes: mitigating reference bias and postmortem damage in ancient genome data.Genome biology · 2025Article
- Assessing the impact of post-mortem damage and contamination on imputation performance in ancient DNA.Scientific reports · 2024Article
- Predicting Functional Consequences of Recent Natural Selection in Britain.Molecular biology and evolution · 2024Article
- Review: Computational analysis of human skeletal remains in ancient DNA and forensic genetics.iScience · 2023Review
- Predicting functional consequences of recent natural selection in Britain.bioRxiv : the preprint server for biology · 2023Article
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4 authors.
Funding
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Abstract
With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase the power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle v4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference, and study sample size. Making use of five ancient individuals with high-coverage data available, we evaluated imputed data for accuracy, reference bias, and genetic affinities as captured by principal component analysis. We obtained genotype concordance levels of over 99% for data with 1× coverage, and similar levels of accuracy and reference bias at levels as low as 0.75×. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1×. We also show that a large and varied phased reference panel as well as the inclusion of low- to moderate-coverage ancient individuals in the study sample can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for postprocessing and validation prior to downstream analysis.
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