Evidence map›Paper›PMID 41689808›Full record

ArticleSTAR protocols2026

Protocol to perform cell-type-specific transcriptome-wide association study using scPrediXcan framework.

Yichao Zhou, Sarah Sumner, Temidayo Adeluwa, Lisha Zhu, Sofia Salazar-Magaña, Hyunki Kim, Saideep Gona, Festus Nyasimi, Rohit Kulkarni, Joseph Powell and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in STAR protocols, 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

14 authors.

Yichao ZhouCommittee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
Sarah SumnerDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
Temidayo AdeluwaCommittee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
Lisha ZhuDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
Sofia Salazar-MagañaDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
Hyunki KimDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Saideep GonaCommittee of Genetic, Genomics, and Systems Biology, University of Chicago, Chicago, IL, USA.
Festus NyasimiDepartment of Human Genetics, University of Chicago, Chicago, IL, USA.
Rohit KulkarniDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Joseph PowellUNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia; Translational Genomics, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
Ravi MadduriData Science and Learning Division, Argonne National Laboratory, Chicago, IL, USA.
Boxiang LiuDepartment of Pharmacy and Pharmaceutical Sciences, National University of Singapore, Singapore, Singapore.
Mengjie ChenDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA. Electronic address: mengjiechen@uchicago.edu.
Hae Kyung ImDepartment of Medicine, Section of Genetic Medicine, University of Chicago, Chicago, IL, USA. Electronic address: haky@uchicago.edu.

Funding

A Framework for Translating Polygenic Findings Related to Alcohol Use Disorder Across SpeciesR01AA029688 · NIAAA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Hae Kyung Im, Abraham A Palmer · 2022 to 2026
$3.1M
NIAAA NIH HHS R01 AA029688
6 · The paper itself

Abstract

The scPrediXcan framework enables cell-type-specific transcriptome-wide association studies (TWASs) by integrating deep learning-based prediction of gene expression from DNA sequence and epigenetic features. We present a protocol for scPrediXcan: training cell-type-specific models for expression prediction, predicting personalized expression, and testing associations with genome-wide association study (GWAS) summary statistics. This framework produces scalable TWAS models for different cellular contexts with minimal computational burden. For complete details on the use and execution of this protocol, please refer to Zhou et al.

Indexed as

Computational BiologyGene Expression ProfilingGenome-Wide Association StudySoftwareTranscriptomeDeep LearningHumansBioinformaticsgene expressionGeneticsgenomicssequence analysis

Identifiers

PMID41689808
PMCPMC12925207

What Socratic holds

Textmetadata
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 Socratic graph.