Evidence map›Paper›PMID 42427534›Full record

ArticlebioRxiv : the preprint server for biology2026

Scalable ARG-free Detection of Denisovan-mediated Superarchaic Introgression Reveals Heterogeneous Patterns across Populations.

Noel McAllister, Sebastian Zöllner, Xinjun Zhang

Abstract readPreprint
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.

0numbers the graph read from it
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

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

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

3 authors.

Noel McAllisterDepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, 48109, Michigan, United States.ORCID 0000-0002-2990-0673
Sebastian ZöllnerDepartment of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, 48109, Michigan, United States.ORCID 0000-0001-7362-156X
Xinjun ZhangDepartment of Human Genetics, University of Michigan Medical School, 1241 Catherine St, Ann Arbor, 48109, Michigan, United States.ORCID 0000-0003-1298-3545

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
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
Deciphering The Evolutionary and Biological Impact of Human AdmixtureR35GM154856 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Xinjun Zhang · 2024 to 2026
$1.0M
NHGRI NIH HHS R01 HG005855NHGRI NIH HHS T32 HG000040NIGMS NIH HHS R35 GM154856
6 · The paper itself

Abstract

Ghost introgression from unsampled hominin lineages has emerged as an increasingly important component of human evolutionary history. Recent studies suggest that deeply divergent hominin lineages may have contributed ancestry either directly to modern humans or indirectly through Denisovan introgression, while inference remains difficult due to few reference genomes, weak signal, and uncertainty in reconstructing deep genealogies. Here we show analytically and through simulations that Denisovan-mediated superarchaic introgression produces predictable shifts in local coalescent depth that can be approximated by scalable summary statistics, particularly pairwise sequence divergence, suggesting that substantial information regarding deeply divergent ancestry is preserved in sequence variations without explicit reconstruction of genealogies. Leveraging this insight, we develop DEEP (

Identifiers

PMID42427534
PMCPMC13345182

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Registered trials

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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.