Evidence map›Paper›PMID 41959181›Full record

ArticlebioRxiv : the preprint server for biology2026

Linking Genetic Risk to Disease-Relevant Cellular States via Metacell-Informed Modeling with ICePop.

Hao Yuan, Aishwarya Mandava, Kewalin Samart, Julia Ganz, Arjun Krishnan

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

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.

Hao YuanMichigan State University, Genetics and Genome Sciences Program, East Lansing, 48823, USA.ORCID 0000-0002-8848-1595
Aishwarya MandavaUniversity of Colorado Anschutz Medical Campus, Department of Biomedical Informatics, Aurora, 80045, USA.
Kewalin SamartUniversity of Colorado Anschutz Medical Campus, Department of Biomedical Informatics, Aurora, 80045, USA.
Julia GanzMichigan State University, Department of Integrative Biology, East Lansing, 48823, USA.
Arjun KrishnanUniversity of Colorado Anschutz Medical Campus, Department of Biomedical Informatics, Aurora, 80045, USA.ORCID 0000-0002-7980-4110

Funding

Resolving and understanding the genomic basis of heterogeneous complex traits and diseasesR35GM128765 · NIGMS · UNIVERSITY OF COLORADO DENVER · PI KRISHNAN, ARJUN · 2018 to 2022
$2.0M
NIGMS NIH HHS R35 GM128765
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have implicated thousands of loci in complex diseases, but translating these population-level signals into specific cellular contexts remains a central challenge. Integrating GWAS with single-cell transcriptomics data has enabled systematic identification of disease-relevant cell types, yet existing methods face a fundamental tradeoff: approaches like seismic that optimized for statistical power operate at the annotated cell-type level and miss heterogeneous disease signals concentrated in specific cellular states, while single-cell-resolution approaches like scDRS that capture such heterogeneity often lack sufficient power to detect subtle associations. Here we present ICePop (

Indexed as

Disease-cell type associationGWAS integrationMetacell analysisSingle-cell RNA-seq

Identifiers

PMID41959181
PMCPMC13060209

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.