Evidence map›Paper›PMID 40483267›Full record

ArticleCommunications biology2025

Genome-wide identification and analysis of recurring patterns of epigenetic variation across individuals.

Jennifer Zou, Emily Maciejewski, Jason Ernst

Abstract read
In one paragraph

Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Jennifer Zou *Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA.
Emily Maciejewski *Computer Science Department, University of California, Los Angeles, Los Angeles, CA, USA.
Jason ErnstComputer Science Department, University of California, Los Angeles, Los Angeles, CA, USA. jason.ernst@ucla.edu.ORCID http://orcid.org/0000-0003-4026-7853

Funding

Training Grant in Genomic Analysis and InterpretationT32HG002536 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Valerie A Arboleda, Harold Pimentel · 2002 to 2026
$8.6M
1/2 Genomic Strategies to Identify High-impact Psychiatric Risk VariantsU01MH105578 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREIMER, NELSON B., GESCHWIND, DANIEL H · 2014 to 2018
$8.5M
Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotencyU01HG012079 · NHGRI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Chongyuan Luo, Kathrin Plath · 2021 to 2026
$6.9M
1/3 Building integrative CNS networks for genomic analysis of autismR01MH109912 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GESCHWIND, DANIEL H · 2016 to 2020
$2.6M
Impact of coding and non-coding variation in progressive supranuclear palsyUH3NS104095 · NINDS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DICKSON, DENNIS WILLIAM, GESCHWIND, DANIEL H · 2020 to 2021
$2.5M
Training Grant in Neurobehavioral GeneticsT32MH073526 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI BEARDEN, CARRIE E, GESCHWIND, DANIEL H · 2006 to 2020
$2.4M
Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing DataDP1DA044371 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ERNST, JASON · 2017 to 2021
$2.3M
National Science Foundation (NSF) DGE-1650604NHGRI NIH HHS T32 HG002536U.S. Department of Health & Human Services | National Institutes of Health (NIH) DP1DA044371U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01MH109912U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32HG002536U.S. Department of Health & Human Services | National Institutes of Health (NIH) T32MH073526U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01HG012079U.S. Department of Health & Human Services | National Institutes of Health (NIH) U01MH105578U.S. Department of Health & Human Services | National Institutes of Health (NIH) UH3NS104095
6 · The paper itself

Abstract

Epigenetic mapping studies across individuals have identified many positions of epigenetic variation across the human genome. However the relationships between these positions, and in particular global patterns that recur in many regions of the genome, remains understudied. In this study, we use a stacked chromatin state model to systematically learn global patterns of epigenetic variation across individuals and annotate the human genome based on them. We apply this framework to histone modification data across individuals in lymphoblastoid cell lines and across autism spectrum disorder cases and controls in prefrontal cortex tissue. We find that global patterns are correlated across multiple histone modifications and with gene expression. We use the global patterns as a framework to predict trans-regulators and study a complex disorder. The frameworks for identifying and analyzing global patterns of epigenetic variation are general and we expect will be useful in other systems.

Indexed as

Autism Spectrum DisorderEpigenesis, GeneticGenetic VariationGenome, HumanChromatinGenome-Wide Association StudyHistone CodeHistonesHumansPrefrontal CortexChromatinHistones

Identifiers

PMID40483267
PMCPMC12145423

What OpenQuestion holds

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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 OpenQuestion graph.