Evidence map›Paper›PMID 41795469›Full record

ArticleAmerican journal of human genetics2026

MetaGLIMPSE: Meta-imputation of low-coverage sequencing data for modern and ancient genomes.

Kiran H Kumar, Simone Rubinacci, Sebastian Zӧllner

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Kiran H KumarDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: kiranhk@umich.edu.
Simone RubinacciInstitute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
Sebastian ZӧllnerDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Population genetics for large-scale sequencing studies of diverse populationsR01HG005855 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Noah Rosenberg, Paul A Scheet · 2010 to 2026
$5.8M
Leveraging long-range haplotypes in sequencing data to advance large scale genetic studiesR01HG011031 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZOELLNER, SEBASTIAN · 2020 to 2023
$1.4M
NHGRI NIH HHS R01 HG005855NHGRI NIH HHS R01 HG011031
6 · The paper itself

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.

Indexed as

DNA, AncientGenome, HumanSequence Analysis, DNASoftwareAlgorithmsGene FrequencyGenotypeHumansPolymorphism, Single NucleotideDNA, Ancientancient DNAgenotype imputationlow-coverage sequencingmeta-imputation

Identifiers

PMID41795469
PMCPMC13087409

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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.