Evidence map›Paper›PMID 39694036›Full record

ReviewCell genomics2025

Bridging genomics' greatest challenge: The diversity gap.

Manuel Corpas, Mkpouto Pius, Marie Poburennaya, Heinner Guio, Miriam Dwek, Shivashankar Nagaraj, Catalina Lopez-Correa, Alice Popejoy, Segun Fatumo

Abstract readReview
In one paragraph

Review in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 1 pooled it
–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

51 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Health inequalities in precision medicine: A systematic review.European journal of human genetics : EJHG · 2026
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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

9 authors.

Manuel CorpasLife Sciences, University of Westminster, 115 New Cavendish Street, W1W 6UW London, UK; The Alan Turing Institute, London, UK; Cambridge Precision Medicine Ltd., ideaSpace, University of Cambridge Biomedical Innovation Hub, Cambridge, UK. Electronic address: m.corpas@westminster.ac.uk.
Mkpouto PiusLife Sciences, University of Westminster, 115 New Cavendish Street, W1W 6UW London, UK.
Marie PoburennayaQueen Mary University of London, London, UK.
Heinner GuioINBIOMEDIC Research and Technological Center, Lima, Peru.
Miriam DwekLife Sciences, University of Westminster, 115 New Cavendish Street, W1W 6UW London, UK.
Shivashankar NagarajCentre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, QLD, Australia.
Catalina Lopez-CorreaGenome Canada, Ottawa, ON, Canada.
Alice PopejoyDepartment of Public Health Sciences (Epidemiology), School of Medicine, University of California, Davis, Davis, CA, USA; UC Davis Comprehensive Cancer Center (UCDCCC), UC Davis Health, University of California, Davis, Sacramento, CA, USA.
Segun FatumoAfrican Computational Genomics (TACG) Research Group, The MRC Uganda Medical Informatics Centre (UMIC), MRC/UVRI and LSHTM, Entebbe, Uganda; Precision Health University Research Institute, Queen Mary University of London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity. Some populations have greater proportional representation in data relative to their population size and the genomic diversity present in their ancestral haplotypes. As insights from genomics become increasingly integrated into evidence-based medicine, strategic inclusion and effective mechanisms to ensure representation of global genomic diversity in datasets are imperative.

Indexed as

Genetic VariationGenome-Wide Association StudyGenomicsGenetic TestingGenome, HumanHumansPharmacogeneticsancestrybiasdirect-to-consumerdiversityequitygenomicsglobal datasetsGWAShealthcareinclusionpharmacogeneticsrepresentation

Identifiers

PMID39694036
PMCPMC11770215

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.