Evidence map›Paper›PMID 41050061›Full record

ReviewFrontiers in genetics2025

Beyond single references: pangenome graphs and the future of genomic medicine.

Denis M Nyaga, Roan E Zaied, Olin K Silander, Michael A Black, Justin M O'Sullivan

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. The role of satellite DNA-enriched heterochromatic variants in reproductive disorders: Insights from standardized cytogenetic analysis.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2026
    Article
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

5 authors.

Denis M NyagaLiggins Institute, University of Auckland, Auckland, New Zealand.
Roan E ZaiedLiggins Institute, University of Auckland, Auckland, New Zealand.
Olin K SilanderLiggins Institute, University of Auckland, Auckland, New Zealand.
Michael A BlackDepartment of Biochemistry, University of Otago, Dunedin, New Zealand.
Justin M O'SullivanLiggins Institute, University of Auckland, Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic medicine relies on single reference genomes that miss crucial genetic diversity, creating diagnostic gaps that disproportionately affect underrepresented populations. Pangenome graphs, collections of diverse genomes represented as interconnected genetic paths, offer a powerful alternative to the standard reference genome approach. Pangenome-based approaches capture the spectrum of human variation, dramatically improving how we detect complex structural variants, reconstruct haplotypes, and reduce bias in genetic studies. Projects like the Human Pangenome Reference Consortium have identified hundreds of megabases of missing genetic diversity, leading to remarkable improvements in variant detection across different populations. Yet, as pangenomes grow larger and computationally complex, they become more challenging to interpret clinically, creating a trade-off between comprehensiveness and usability. This review discusses the technical and conceptual advances enabling clinical applications of pangenomes in rare disease diagnosis. Realizing the future potential of pangenome graphs in genomic medicine will require innovative implementation strategies, thorough clinical testing, and user-friendly approaches.

Indexed as

clinical diagnosisgenetic diversitygenome assemblyhaplotypeslong-readsequencingpangenome graphsreference genomes

Identifiers

PMID41050061
PMCPMC12492951

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.