Evidence map›Paper›PMID 42094450›Full record

ArticlebioRxiv : the preprint server for biology2026

Optimal Reference Panel Design in Ancient DNA Imputation from Coalescent Theory, Simulation, and Real Data Application with an Ancient Reference Panel.

Bárbara Sousa da Mota, Kiran H Kumar, David Reich, Sebastian Zöllner

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In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing 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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Bárbara Sousa da MotaDepartment of Human Evolutionary Biology, Harvard University, Cambridge, MA, USA.ORCID 0000-0002-5062-6234
Kiran H KumarDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.ORCID 0009-0005-5270-045X
David ReichDepartment of Genetics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0002-7037-5292
Sebastian ZöllnerDepartment of Biostatistics, 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
Solving ascertainment bias in ancient DNA using Ultima Genomics sequencingR01HG012287 · NHGRI · HARVARD MEDICAL SCHOOL · PI REICH, DAVID E · 2021 to 2025
$2.7M
NHGRI NIH HHS R01 HG005855NHGRI NIH HHS R01 HG012287
6 · The paper itself

Abstract

Imputation is widely used in the ancient DNA (aDNA) field to determine which phenotypically important alleles ancient individuals carried, to study natural selection, and to detect segments of the genome that are shared between individuals identical by descent. However, rare variant imputation is less accurate, and rare variants tend to be excluded from downstream analyses. State-of-the-art imputation methods leverage large reference panels, improving rare variant accuracy in modern targets. However, it is unclear how to identify optimal panels for aDNA targets. It seems plausible that aDNA reference panels would improve imputation of aDNA, but no such panels have been assembled or tested. We leveraged analytical results from coalescent theory and complementary simulations to evaluate both performance of large modern panels, and ancient panels' impact on aDNA imputation. For modern panels, sample sizes as small as 5,000 saturate imputation performance and model misspecifications in standard imputation algorithms increase imputation error for rare and intermediate frequency variants. For instance, for European hunter-gatherers, non-reference imputed variants with derived allele frequency less than at least 2% should be removed. Including ancient genomes in a modern reference panel substantially improved imputation accuracy in analytical modelling and simulations, particularly, for rare variants and older samples from groups with low effective population size. We assembled a joint reference panel with 1000 Genomes and 95 ancient samples and used it to impute 95 downsampled genomes, finding modest gains in imputation performance. This approach can rescue rare variants typically discarded from current imputation pipelines and may prove useful as the number of ancient samples increases.

Identifiers

PMID42094450
PMCPMC13142300

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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.