MetaGLIMPSE: Meta-imputation of Low Coverage Sequencing Data for Modern and Ancient Genomes

Jul 10, 2026

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Efficient low-coverage sequencing imputation provides an unbiased alternative to SNP array imputation, improving rare-variant imputation accuracy across populations. Imputation quality typically depends on reference panel size and ancestry match, but privacy restrictions prevent access to individual genotypes and complicate building merged “mega” panels. MetaGLIMPSE introduces a meta-imputation approach that combines single-panel imputation outputs using individual- and marker-specific weights. Across scenarios, it outperforms the best single panel (0.5x–8x) and matches combined-panel performance in some settings.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

Abstract

Introduction

Low coverage sequencing and imputation motivation

Challenges in low-coverage genotype calling

Existing methods (GLIMPSE and GLIMPSE2)

Determinants of imputation accuracy

Limitations of combining reference panels

Prior meta-imputation approaches

bioRxiv preprint doi: [https://doi.org/10.1101/2025.06.24.660721](https://doi.org/10.1101/2025.06.24.660721); this version posted June 24, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. MetaGLIMPSE: Meta Imputation of Low Coverage Sequencing Data for Modern and Ancient Genomes Kiran H. Kumar1 *, Simone Rubinacci2, Sebastian Zӧllner1 1 Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA 2 Institute of Molecular Medicine Finland, University of Helsinki, Helsinki, Finland *Corresponding author: [kiranhk@umich.edu](kiranhk@umich.edu) 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 panel size and closer ancestry match between target and reference samples, leveraging imputation from multiple reference panels would facilitate better imputation accuracy; however, individual reference panel genotypes are often privacy protected. We present a novel meta-imputation method, MetaGLIMPSE, that combines estimates from multiple reference panels for low coverage sequencing imputation. We directly combine single panel imputation results using weights estimated for each individual and imputed marker. Across all our scenarios, MetaGLIMPSE outperforms the best single panel imputation for coverages 0.5x-8x and across all minor allele frequencies, and for certain coverages and scenarios, performs equally to the combined panel imputation. bioRxiv preprint doi: [https://doi.org/10.1101/2025.06.24.660721](https://doi.org/10.1101/2025.06.24.660721); this version posted June 24, 2025. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. Introduction Genomic imputation is a cost-effective tool that powers downstream analysis such as GWAS, fine mapping, and meta-analyses of cohort studies (Browning and Browning 2007; Das et al. 2018; Howie et al. 2012; Fuchsberger et al. 2015; Li et al. 2010; Marcini et al. 2010; Yu et al. 2022) . Earlier papers developed genomic imputation algorithms for SNP array data (Browning and Browning et al. 2007; Fuchsberger et al. 2015; Howie et al. 2012) . Nowadays, low coverage sequencing is a growing alternative to SNP arrays due to decreasing sequencing costs (Boltz et al. 2024; Martin et al. 2021) . Low coverage sequencing utilizes shotgun sequencing at low mean depths between 0. 1x and 8x (Boltz et al. 2024; Martin et al. 2021; Rubinacci et al. 2021) . While such read data is often insufficient to confidently call genotypes, imputation accuracy for low pass sequencing at coverages as low as 0.5x is comparable to commonly used SNP arrays, and a coverage of 1x suffices for confident rare variant detection. (Martin et al. 2021; Rubinacciet al. 2021) . Moreover, low coverage sequencing is especially useful for non-European populations as it provides unbiased coverage across the genome and eliminates ascertainment bias, a drawback of SNP arrays (Boltz et al. 2024; Martin et al. 2021) . The challenge of low coverage imputation is that individual genotypes are more uncertain, which precludes pre-phasing, a computation time saving strategy used in the SNP array imputation (Howie et al. 2012) . Thus, an algorithm for low coverage imputation must handle both phasing and imputation with computational efficiency. A commonly used method for low coverage imputation (Allentoft et al. 2024; Boltz et al. 2024; Erven et al. 2024; Nakamura et al. 2024; Ringbauer et al. 2024

FAQs

What problem does MetaGLIMPSE address in low-coverage sequencing studies?

MetaGLIMPSE targets the difficulty of accurate genotype imputation from low-coverage sequencing, especially when reference panels differ in size and ancestry match but cannot be easily merged due to privacy constraints.

How does MetaGLIMPSE combine information from multiple reference panels?

It performs imputation for each reference panel and then directly combines the resulting single-panel imputed genotypes using weights estimated for each individual and each imputed marker.

What factors most strongly influence imputation accuracy, according to the text?

Imputation accuracy depends mainly on how well the reference panel ancestry matches the target samples and on the reference panel size, which is particularly important for rare variants.

Jul 10, 2026

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