Evidence map›Paper›PMID 41993270›Full record

ArticlebioRxiv : the preprint server for biology2026

FM-GPT: Bayesian fine mapping for phenome-wide transcriptome-wide association studies.

Travis Canida, Zhenyao Ye, Shao-Hsuan Wang, Hsin-Hsiung Huang, Yezhi Pan, Menglu Liang, Shuo Chen, Tianzhou Ma

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

5 · Who and what money

Authors and funding

8 authors.

Travis CanidaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
Zhenyao YeMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
Shao-Hsuan WangGraduate Institute of Statistics, National Central University, Taiwan.
Hsin-Hsiung HuangDepartment of Statistics and Data Science, University of Central Florida, Florida, United States of America.
Yezhi PanDepartment of Mathematics, University of Maryland, College Park, Maryland, United States of America.ORCID 0009-0001-4014-0302
Menglu LiangDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.
Shuo ChenMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America.
Tianzhou MaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America.ORCID 0000-0003-3605-0811

Funding

A novel transcriptome-connectome approach to study the neurogenetic mechanism of nicotine and cannabis addictionK01DA059603 · NIDA · UNIV OF MARYLAND, COLLEGE PARK · PI Tianzhou MA · 2024 to 2026
$579k
NIDA NIH HHS K01 DA059603
6 · The paper itself

Abstract

Transcriptome-wide association studies (TWAS) integrate genome wide association studies with expression quantitative trait locus reference panels to identify genes associated with traits of interest. However, linkage disequilibrium and correlated gene expression can induce spurious TWAS signals, motivating fine mapping methods to prioritize putatively causal genes within associated loci. The rapid growth of large-scale phenomic resources (e.g. electronic health records (EHRs)) has shifted genetic studies from single-trait analyses to phenome-wide investigations that jointly evaluate many closely related phenotypes. We introduce FM-GPT (

Indexed as

Bayesian fine mappingelectronic health recordsneuroimagingphenome-wide studiestranscriptome-wide association studies

Identifiers

PMID41993270
PMCPMC13081891

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.