Evidence map›Paper›PMID 38798383›Full record

ArticlemedRxiv : the preprint server for health sciences2025

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

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

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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

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

9 authors.

Artem KimDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0001-8824-2853
Zixuan Eleanor ZhangDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0001-7193-8694
Come LegrosDepartment of Population and Public Health Sciences, 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.ORCID 0000-0002-3511-6850
Adam J de SmithDepartment of Population and Public Health Sciences, 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.
Nicholas MancusoDepartment of Population and Public Health Sciences, Keck School of Medicine, 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.

Funding

Characterizing the evolutionary architecture of complex disease within and across diverse populationsR01HG012133 · NHGRI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI MANCUSO, NICHOLAS · 2021 to 2025
$3.6M
Characterizing genetic signatures of natural selection to understand human diseasesR35GM147789 · NIGMS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Steven Gazal · 2022 to 2026
$2.0M
NHGRI NIH HHS R01 HG012133NIGMS NIH HHS R35 GM147789
6 · The paper itself

Abstract

The SNP-heritability of human diseases is extremely enriched in candidate regulatory elements (cREs) from disease-relevant cell types. Critical next steps are to understand whether these enrichments are driven by multiple causal cell types and whether individual variants impact disease risk via a single or multiple of cell types. Here, we propose CT-FM and CT-FM-SNP, 2 methods accounting for cREs shared across cell types to identify independent sets of causal cell types for a trait and its candidate causal variants, respectively. We applied CT-FM to 63 GWAS summary statistics (average

Identifiers

PMID38798383
PMCPMC11118635

What OpenQuestion holds

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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.