ArticleMolecular ecology resources2025
Assessing Genotype Imputation Methods for Low-Coverage Sequencing Data in Populations With Differing Relatedness and Inbreeding Levels.
Article in Molecular ecology resources, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Low-Coverage Whole-Genome Resequencing in Livestock and Poultry: Statistical Foundations, Applications and Future Directions.Biology · 2026Review
- Imputation in the Wild: Genome-Wide Robustness and Fine-Scale Limitations of Low-Coverage Genomes in Endangered Species.Molecular ecology resources · 2026Article
- Genome-Wide Homozygosity Predicts Inbreeding Depression in the Hihi/Stitchbird (Evolutionary applications · 2026Article
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- Robust imputation-based method for eye, hair, and skin colour prediction from low-coverage ancient DNA.Scientific reports · 2026Article
- Missing or Mis-Telling the Story? Trade-Offs for Restriction-Site Associated Compared to Whole Genome Sequencing.Molecular ecology · 2026Article
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Authors and funding
5 authors.
Funding
Abstract
Low-coverage sequencing (LCS) followed by genotype imputation has become a cost-efficient approach for obtaining whole-genome SNPs. Several imputation methods for LCS data have been developed over the last decade. However, comparisons of their accuracy in inferring missing genotypes and their effectiveness for downstream analysis such as population genetics have not been comprehensively studied. In the present study, we assessed the imputation performance of five different tools: GLIMPSE2, GeneImp, QUILT2, STITCH and Beagle5.4, using populations simulated by SLiM4 that represent different levels of genetic relatedness and inbreeding. Imputation accuracy was calculated at the level of variant, haplotype and sample. The effectiveness of using imputed genotypes in recovering genetic structure, relatedness, inbreeding coefficients and demographic history was subsequently evaluated. The imputation accuracy of different methods was further tested in a real population of 283 hihi (stitchbird) samples. Our results suggest a high accuracy of all the tested methods on populations with high levels of genetic relatedness. However, in populations with low relatedness, the imputation accuracy differed across different tools and impacted the results of some downstream analyses. The simulation and imputation pipeline presented here can help determine the most suitable imputation method for different population scenarios.
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