Evidence map›Paper›PMID 40375077›Full record

ArticleBMC medical genomics2025

Dynamic clustering of genomics cohorts beyond race, ethnicity-and ancestry.

Hussein Mohsen, Kim Blenman, Prashant S Emani, Quaid Morris, Jian Carrot-Zhang, Lajos Pusztai

Abstract read
In one paragraph

Article in BMC medical genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Genomic Medicine and Individual Autonomy: Reflections on Knowledge Societies and Governmentality.International journal of environmental research and public health · 2026
    Article
  2. Article
4 · The record

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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

6 authors.

Hussein MohsenComputational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA. hussein.mohsen@utoronto.ca.
Kim BlenmanBreast Medical Oncology, School of Medicine, Yale University, New Haven, CT, 06511, USA.
Prashant S EmaniMolecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06511, USA.
Quaid MorrisComputational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Jian Carrot-ZhangEpidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA.
Lajos PusztaiBreast Medical Oncology, School of Medicine, Yale University, New Haven, CT, 06511, USA.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
Breast Cancer Research Foundation Investigator Award BCRF-22-133NCI NIH HHS P30 CA008748Susan Komen Leadership Grant SAC220225
6 · The paper itself

Abstract

backgroundRecent decades have witnessed a steady decrease in the use of race categories in genomic studies. While studies that still include race categories vary in goal and type, these categories already build on a history during which racial color lines have been enforced and adjusted in the service of social and political systems of power and disenfranchisement. For early modern classification systems, data collection was also considerably arbitrary and limited. Fixed, discrete classifications have limited the study of human genomic variation and disrupted widely spread genetic and phenotypic continuums across geographic scales. Relatedly, the use of broad and predefined classification schemes-e.g. continent-based-across traits can risk missing important trait-specific genomic signals.

methodsTo address these issues, we introduce a dynamic approach to clustering human genomics cohorts based on genomic variation in trait-specific loci and without using a set of predefined categories. We tested the approach on whole-exome sequencing datasets in ten cancer types and partitioned them based on germline variants in cancer-relevant genes that could confer cancer type-specific disease predisposition.

resultsResults demonstrate clustering patterns that transcend discrete continent-based categories across cancer types. Functional analysis based on cancer type-specific clusterings also captures the fundamental biological processes underlying cancer, differentiates between dynamic clusters on a functional level, and identifies novel potential drivers overlooked by a predefined continent-based clustering.

conclusionsThrough a trait-based lens, the dynamic clustering approach reveals genomic patterns that transcend predefined classification categories. We propose that coupled with diverse data collection, new clustering approaches have the potential to draw a more complete portrait of genomic variation and to address, in parallel, technical and social aspects of its study.

Indexed as

EthnicityGenomicsRacial GroupsCluster AnalysisCohort StudiesHumansNeoplasmsAncestryCancer genomicsClassificationEthnicityGenetic variationRace

Identifiers

PMID40375077
PMCPMC12082885

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.