Evidence map›Paper›PMID 42298188›Full record

ArticleNature genetics2026

Decoding common and rare noncoding variant effects across cellular and developmental contexts.

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 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Genetic architectures of brain-related traits are shaped by strong selective constraints.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  3. Review
  4. Article
  5. Article
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

19 authors.

Andrew R Marderstein *Department of Pathology, Stanford University, Stanford, CA, USA. mardera1@mskcc.org.ORCID http://orcid.org/0000-0003-0859-4969
Soumya Kundu *Department of Computer Science, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0001-5182-5326
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.ORCID http://orcid.org/0009-0006-4839-299X
Chang M YunDepartment of Chemical Engineering, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-3793-8265
Diego Pomales-MatosDepartment of Genetics, Stanford University, Stanford, CA, USA.
Yilin XieDepartment of Pathology, Stanford University, Stanford, CA, USA.
Serena H ChangGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.ORCID http://orcid.org/0009-0007-2908-9546
Iris M ChinGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
Aayushi J ShahGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.ORCID http://orcid.org/0009-0007-2070-5517
Zachary A GardellGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.
M Ryan CorcesGladstone Institute of Neurological Disease, Gladstone Institutes, San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-7465-7652
Daniel NachunDepartment of Pathology, Stanford University, Stanford, CA, USA.
Selin JessaDepartment of Genetics, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0003-4192-6523
Anshul KundajeDepartment of Computer Science, Stanford University, Stanford, CA, USA. akundaje@stanford.edu.ORCID http://orcid.org/0000-0003-3084-2287
Stephen B MontgomeryDepartment of Pathology, Stanford University, Stanford, CA, USA. smontgom@stanford.edu.ORCID http://orcid.org/0000-0002-5200-3903

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI SELWYN M VICKERS · 1985 to 2026
$347.4M
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
Restorative practice in repairing harm and promoting safe and inclusive practices in the laboratory.T32GM136547 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Adrian Erlebacher, Anita Sil · 2020 to 2026
$4.5M
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
Identifying causal genetic variants and molecular mechanisms impacting mental healthR01MH125244 · NIMH · STANFORD UNIVERSITY · PI KUNDAJE, ANSHUL, MONTGOMERY, STEPHEN · 2021 to 2025
$3.0M
NCI NIH HHS P30 CA008748NHGRI NIH HHS U01 HG012069NIA NIH HHS U01 AG072573NIGMS NIH HHS T32 GM136547NIMH NIH HHS R01 MH125244U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI) 5U01HG012069-03
6 · The paper itself

Abstract

Interpreting how noncoding variants act in specific cell types across human development is a major challenge. Here we generated 3 billion predictions from deep learning sequence models of chromatin accessibility across diverse fetal and adult cellular contexts. These prioritized functional variants and revealed a dichotomy: common variants are more cell-type-specific, whereas ultra-rare variants had larger and broader effects across cell types, with the strongest evidence of purifying selection in fetal neurons. Leveraging these insights, we developed FLARE (Functional Lasso Analysis of Regulatory Evolution), which integrates evolutionary constraint to prioritize noncoding variants with extreme regulatory effects. FLARE provided a general framework for studying regulatory variation, from de novo mutations in childhood disorders to rare variants underlying outlier adult brain expression and common variants enriched for schizophrenia heritability. Together, these results demonstrate how integrating single-cell chromatin accessibility, population genetics and deep learning can identify regulatory variants that influence human development and disease.

Indexed as

Genetic VariationBrainChromatinGene Expression Regulation, DevelopmentalHumansNeuronsPolymorphism, Single NucleotideSchizophreniaChromatin

Identifiers

PMID42298188
PMCPMC13335522

What OpenQuestion holds

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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.