Evidence map›Paper›PMID 40501721›Full record

ArticlebioRxiv : the preprint server for biology2025

Comprehensive molecular impact mapping of common and rare variants at GWAS loci.

Brad Balderson, Sanjana Tule, Mei-Lin Okino, William Jf Rieger, Sierra Corban, Jeff Jaureguy, Nathan Palpant, Kyle J Gaulton, Mikael Bodén, Graham McVicker

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

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

10 authors.

Brad BaldersonSalk Institute for Biological Studies, La Jolla CA, USA.ORCID 0000-0002-5153-6601
Sanjana TuleSchool of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia.ORCID 0000-0001-5630-5792
Mei-Lin OkinoBiomedical Sciences Graduate Program, University of California San Diego, La Jolla CA, USA.
William Jf RiegerSchool of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia.
Sierra CorbanDepartment of Pediatrics, University of California San Diego, La Jolla CA, USA.
Jeff JaureguySalk Institute for Biological Studies, La Jolla CA, USA.
Nathan PalpantInstitute for Molecular Biosciences, Brisbane, Australia.ORCID 0000-0002-9334-8107
Kyle J GaultonDepartment of Pediatrics, University of California San Diego, La Jolla CA, USA.ORCID 0000-0003-1318-7161
Mikael BodénSchool of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Australia.ORCID 0000-0003-3548-268X
Graham McVickerSalk Institute for Biological Studies, La Jolla CA, USA.ORCID 0000-0003-0991-0951

Funding

An interactive resource to generate and provide integrated knowledge of the human pancreasU24DK138512 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Noel P Burtt, Jason Flannick · 2024 to 2026
$9.6M
Training Program in Basic Clinical GeneticsT32GM008666 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HAMILTON, BRUCE A · 1998 to 2021
$8.6M
The impact of genomic variation on environment-induced changes in pancreatic beta cell statesU01HG012059 · NHGRI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Hannah Kathryn Carter, Kyle Jeffrie Gaulton · 2021 to 2026
$7.5M
Using genomic perturbations to understand trait-associated human genetic variationR35HG011315 · NHGRI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI MCVICKER, GRAHAM · 2021 to 2025
$2.9M
UC San Diego Genetics Training ProgramT32GM145427 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BRUCE A HAMILTON · 2022 to 2026
$2.6M
Predicting the effects of genetic variants on chromatin accessibility with a deep learning approachF31HG013262 · NHGRI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI JAUREGUY, JEFF · 2024 to 2025
$89k
NHGRI NIH HHS F31 HG013262NHGRI NIH HHS R35 HG011315NHGRI NIH HHS U01 HG012059NIDDK NIH HHS U24 DK138512NIGMS NIH HHS T32 GM008666NIGMS NIH HHS T32 GM145427
6 · The paper itself

Abstract

Deep learning sequence to function models can predict the molecular effects of genetic variants, but their predictions are limited to the cell types and assays they are trained on. Here we describe

Identifiers

PMID40501721
PMCPMC12157646

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.