Evidence map›Paper›PMID 41040238›Full record

ArticlebioRxiv : the preprint server for biology2025

Comparing Neanderthal introgression maps reveals core agreement but substantial heterogeneity.

Yaen Chen, Keila Velazquez-Arcelay, John A Capra

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. 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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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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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

5 · Who and what money

Authors and funding

3 authors.

Yaen ChenBiological and Medical Informatics PhD Program, University of California, San Francisco, CA.ORCID 0000-0002-1389-5398
Keila Velazquez-ArcelayDivision of Hematology/Oncology, Department of Medicine, University of California, San Francisco, CA.ORCID 0000-0001-7937-7182
John A CapraDepartment of Bioengineering, University of California, San Francisco, CA.ORCID 0000-0001-9743-1795

Funding

Molecular and Cellular Mechanisms in CancerT32CA108462 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GOGA, ANDREI · 2004 to 2024
$10.4M
The Evolution of Gene Regulation and Human DiseaseR35GM127087 · NIGMS · VANDERBILT UNIVERSITY · PI John Anthony Capra · 2018 to 2026
$3.2M
NCI NIH HHS T32 CA108462NIGMS NIH HHS R35 GM127087
6 · The paper itself

Abstract

Statistical methods to identify Neanderthal ancestry in modern human genomes rest on varying assumptions and inputs. Nonetheless, most studies of introgression use only a single method to define Neanderthal ancestry. Due to a lack of "ground truth," we have a limited understanding of the accuracy, comparative strengths and weaknesses, and the sensitivity of downstream conclusions for these methods. Here, we performed large-scale comparisons of genome-wide introgression maps from 12 representative Neanderthal introgression detection algorithms. These span methods that consider archaic and human reference genomes not from Africa (ArchaicSeeker2, CRF, DICAL-ADMIX), only archaic genomes (S*, Sprime, HMM, SARGE, ARGWeaver-D), only human reference genomes, including from Africa (IBDmix), or simulated data (ArchIE). Our results highlight a core set of regions predicted by nearly all methods, as well as substantial heterogeneity in commonly used Neanderthal introgression maps. Furthermore, we find that downstream analyses may result in different conclusions depending on the map used. Thus, we recommend careful consideration of map(s) chosen for an analysis and support the use of multiple maps to ensure robustness of conclusions. We make integrated prediction sets available, enabling further understanding of Neanderthal introgression's legacy on modern humans.

Identifiers

PMID41040238
PMCPMC12485899

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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.