Evidence map›Paper›PMID 42600622›Full record

ArticleCell genomics2026

Identifying independent causal cell types for human diseases and risk variants.

Artem Kim, Zixuan Eleanor Zhang, Come Legros, Hongbo Wang, Zeyun Lu, Adam J de Smith, Jill E Moore, Arun Durvasula, Nicholas Mancuso, Steven Gazal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cell genomics, 2026. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Artem KimDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: artemkim@usc.edu.
Zixuan Eleanor ZhangDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Come LegrosDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Hongbo WangDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Zeyun LuDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Adam J de SmithDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Jill E MooreDepartment of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Arun DurvasulaDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Nicholas MancusoDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA.
Steven GazalDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA. Electronic address: gazal@usc.edu.

Funding

Characterizing genetic signatures of natural selection to understand human diseasesR35GM147789 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Steven Gazal · 2022 to 2026
$2.0M
NIGMS NIH HHS R35 GM147789
6 · The paper itself

Abstract

Genome-wide association studies (GWASs) have shown that disease-associated variants are concentrated in candidate regulatory elements (cREs) from disease-relevant cell types. Here, we introduce cell-type fine-mapping (CT-FM) and CT-FM-SNP, probabilistic methods that account for cRE sharing across cell types to infer independent causal cell-type sets for complex traits and candidate causal variants. Applying CT-FM to 63 GWASs using 924 cRE annotations, we inferred 79 independent cell-type sets explaining 39.0% ± 1.8% of trait SNP heritability and identified 14 traits with multiple independent cellular mechanisms, including height, schizophrenia, and autoimmune diseases. Applying CT-FM-SNP to 39 UK Biobank traits, we assigned high-confidence causal cell types to 3,091 candidate non-coding variant-trait pairs. Most variants appeared to act through a single cell-type set, whereas pleiotropic variants often acted through different cell types depending on the phenotype context. Together, CT-FM and CT-FM-SNP provide a framework for dissecting the cellular architecture of complex traits.

Indexed as

Genetic Predisposition to DiseaseGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideQuantitative Trait LociRegulatory Sequences, Nucleic Acidcandidate regulatory elementscausal cell typescomplex traitscREsepigenomicsGWASstatistical genetics

Identifiers

PMID42600622
PMCPMC13576728

What OpenQuestion holds

Textmetadata
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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.