Evidence map›Paper›PMID 35100265›Full record

ArticlePLoS genetics2022

Super interactive promoters provide insight into cell type-specific regulatory networks in blood lineage cell types.

Jia Wen, Taylor M Lagler, Quan Sun, Yuchen Yang, Jiawen Chen, Yuriko Harigaya, Vijay G Sankaran, Ming Hu, Alexander P Reiner, Laura M Raffield and 1 more

Open access · goldAbstract read
In one paragraph

Article in PLoS genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 46% of its field
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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
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

11 authors at 4 institutions in 2 countries.

Jia WenDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0003-3273-7704
Taylor M LaglerDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Quan SunDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0001-8324-2803
Yuchen YangDepartment of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0001-5977-1617
Jiawen ChenDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0002-6193-534X
Yuriko HarigayaDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0002-1879-5214
Vijay G SankaranDivision of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, United States of America.ORCID 0000-0003-0044-443X
Ming HuDepartment of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America.ORCID 0000-0003-0987-2916
Alexander P ReinerDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America.ORCID 0000-0002-1427-4470
Laura M RaffieldDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0002-7892-193X
Yun LiDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID 0000-0002-9275-4189
University of North Carolina at Chapel Hill · USBroad Institute · USCleveland Clinic · USUniversity of Washington · US

Funding

North Carolina Translational and Clinical Science Institute (NC TraCS) KL2KL2TR002490 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WEINBERGER, MORRIS · 2018 to 2022
$11.0M
Preclinical CoreP50HD103573 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Mark D Shen · 2020 to 2026
$9.7M
Systematic Genetic Dissection of Human ErythropoiesisR01DK103794 · NIDDK · BOSTON CHILDREN'S HOSPITAL · PI Vijay Ganesh Sankaran · 2014 to 2026
$5.9M
Next generation functional genomics of hematology traitsR01HL146500 · NHLBI · UNIVERSITY OF WASHINGTON · PI ALEXANDER P REINER · 2020 to 2026
$5.7M
Polygenic risk scores for cardiometabolic disorders: the role of blood cells immune response and evolutionary adaptationU01HG011720 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Yun Li, ALEXANDER P REINER · 2021 to 2026
$5.4M
Statistical Methods for RNA-seq Data AnalysisR01GM105785 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Wei Sun · 2014 to 2026
$4.6M
The Genetic Epidemiology of Heart, Lung, and Blood TraitsTraining Grant (GenHLB)T32HL129982 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Christy Leigh Avery, KAREN L. MOHLKE · 2016 to 2026
$3.8M
Genetic Studies of Blood Cell Traits in Multi-Ethnic CohortsR01HL129132 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LI, YUN, REINER, ALEXANDER P · 2016 to 2019
$2.7M
NCATS NIH HHS KL2 TR002490NHGRI NIH HHS U01 HG011720NHLBI NIH HHS R01 HL129132NHLBI NIH HHS R01 HL146500NHLBI NIH HHS T32 HL129982NICHD NIH HHS P50 HD103573NIDDK NIH HHS R01 DK103794NIGMS NIH HHS R01 GM105785
6 · The paper itself

Abstract

Existing studies of chromatin conformation have primarily focused on potential enhancers interacting with gene promoters. By contrast, the interactivity of promoters per se, while equally critical to understanding transcriptional control, has been largely unexplored, particularly in a cell type-specific manner for blood lineage cell types. In this study, we leverage promoter capture Hi-C data across a compendium of blood lineage cell types to identify and characterize cell type-specific super-interactive promoters (SIPs). Notably, promoter-interacting regions (PIRs) of SIPs are more likely to overlap with cell type-specific ATAC-seq peaks and GWAS variants for relevant blood cell traits than PIRs of non-SIPs. Moreover, PIRs of cell-type-specific SIPs show enriched heritability of relevant blood cell trait (s), and are more enriched with GWAS variants associated with blood cell traits compared to PIRs of non-SIPs. Further, SIP genes tend to express at a higher level in the corresponding cell type. Importantly, SIP subnetworks incorporating cell-type-specific SIPs and ATAC-seq peaks help interpret GWAS variants. Examples include GWAS variants associated with platelet count near the megakaryocyte SIP gene EPHB3 and variants associated lymphocyte count near the native CD4 T-Cell SIP gene ETS1. Interestingly, around 25.7% ~ 39.6% blood cell traits GWAS variants residing in SIP PIR regions disrupt transcription factor binding motifs. Importantly, our analysis shows the potential of using promoter-centric analyses of chromatin spatial organization data to identify biologically important genes and their regulatory regions.

Indexed as

Gene Regulatory NetworksPromoter Regions, GeneticBlood CellsCell LineageGenome-Wide Association StudyHumansProto-Oncogene Protein c-ets-1Receptor, EphB3ETS1 protein, humanProto-Oncogene Protein c-ets-1Receptor, EphB3

Identifiers

PMID35100265
PMCPMC8830683
OpenAlexW4225486160

What OpenQuestion holds

Textmetadata
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.