ArticleAmerican journal of human genetics2026
MetaGLIMPSE: Meta-imputation of low-coverage sequencing data for modern and ancient genomes.
Article in American journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- 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
- Recent advances in methods to characterize archaic introgression in modern humans.Genome research · 2026Review
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Abstract
The advent of efficient and accurate imputation for low-coverage sequencing offers an unbiased alternative to SNP array imputation, increasing the accuracy of rare variant imputation across all populations. Since imputation accuracy generally increases with larger reference panels and closer ancestry match between target and reference samples, leveraging imputation from multiple reference panels improves imputation accuracy; however, individual reference panel genotypes are often privacy protected. Meta-imputation bypasses individual-level data by combining single-panel imputed genotypes through estimating panel- and marker-specific weights. We present a meta-imputation method, MetaGLIMPSE, that combines estimates from multiple reference panels for low-coverage sequencing imputation. Across all our scenarios, for both modern and ancient DNA samples, MetaGLIMPSE consistently outperforms the best single-panel imputation for coverages of 0.1×-8× and across all minor-allele frequencies, equaling the combined panel imputation for some parameters. Finally, MetaGLIMPSE is computationally efficient, meta-imputing 500 whole genomes in 16% of the time of GLIMPSE2.
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