Evidence map›Paper›PMID 40027628›Full record

ArticlebioRxiv : the preprint server for biology2025

Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental contexts in the brain and heart.

Andrew R Marderstein, Soumya Kundu, Evin M Padhi, Salil Deshpande, Austin Wang, Esther Robb, Ying Sun, Chang M Yun, Diego Pomales-Matos, Yilin Xie and 4 more

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

14 authors.

Andrew R MardersteinDepartment of Pathology, Stanford University, Stanford, CA, USA.ORCID 0000-0003-0859-4969
Soumya KunduDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Evin M PadhiDepartment of Pathology, Stanford University, Stanford, CA, USA.
Salil DeshpandeDepartment of Genetics, Stanford University, Stanford, CA, USA.
Austin WangDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Esther RobbDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Ying SunDepartment of Pathology, Stanford University, Stanford, CA, USA.
Chang M YunDepartment of Chemical Engineering, Stanford University, Stanford, CA, USA.
Diego Pomales-MatosDepartment of Genetics, Stanford University, Stanford, CA, USA.
Yilin XieDepartment of Pathology, Stanford University, Stanford, CA, USA.
Daniel NachunDepartment of Pathology, Stanford University, Stanford, CA, USA.
Selin JessaDepartment of Genetics, Stanford University, Stanford, CA, USA.
Anshul KundajeDepartment of Computer Science, Stanford University, Stanford, CA, USA.
Stephen B MontgomeryDepartment of Pathology, Stanford University, Stanford, CA, USA.

Funding

Multi-omic functional assessment of novel AD variants using high-throughput and single-cell technologiesU01AG072573 · NIA · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2021 to 2025
$8.3M
Predicting context-specific molecular and phenotypic effects of genetic variation through the lens of the cis-regulatory codeU01HG012069 · NHGRI · STANFORD UNIVERSITY · PI Anshul Kundaje · 2021 to 2026
$3.9M
Mapping Molecular and Phenotypic Interactions in Alzheimers DiseaseR01AG066490 · NIA · STANFORD UNIVERSITY · PI MONTGOMERY, STEPHEN · 2020 to 2024
$3.6M
Identifying causal genetic variants and molecular mechanisms impacting mental healthR01MH125244 · NIMH · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2021 to 2025
$3.0M
NHGRI NIH HHS U01 HG012069NIA NIH HHS R01 AG066490NIA NIH HHS U01 AG072573NIMH NIH HHS R01 MH125244
6 · The paper itself

Abstract

Whole genome sequencing has identified over a billion non-coding variants in humans, while GWAS has revealed the non-coding genome as a significant contributor to disease. However, prioritizing causal common and rare non-coding variants in human disease, and understanding how selective pressures have shaped the non-coding genome, remains a significant challenge. Here, we predicted the effects of 15 million variants with deep learning models trained on single-cell ATAC-seq across 132 cellular contexts in adult and fetal brain and heart, producing nearly two billion context-specific predictions. Using these predictions, we distinguish candidate causal variants underlying human traits and diseases and their context-specific effects. While common variant effects are more cell-type-specific, rare variants exert more cell-type-shared regulatory effects, with selective pressures particularly targeting variants affecting fetal brain neurons. To prioritize

Identifiers

PMID40027628
PMCPMC11870466

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.