Evidence map›Paper›PMID 26180553›Full record

ArticleEpigenetics & chromatin2015

Motif signatures in stretch enhancers are enriched for disease-associated genetic variants.

Daniel X Quang, Michael R Erdos, Stephen C J Parker, Francis S Collins

Abstract read
In one paragraph

Article in Epigenetics & chromatin, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

24 citing papers in PubMed.

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  18. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes.Proceedings of the National Academy of Sciences of the United States of America · 2017
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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

4 authors.

Daniel X QuangDepartment of Computer Science, University of California, Irvine, Irvine, CA, 92697, USA.
Michael R ErdosNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
Stephen C J ParkerDepartments of Computational Medicine and Bioinformatics and Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA. scjp@umich.edu.
Francis S CollinsNational Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.

Funding

Genetic analysis of type II diabetes in Finnish populationZIAHG000024 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ERDOS, MICHAEL · 2009 to 2025
$38.2M
Mathematical, Computational and Systems BiologyT32EB009418 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI LANDER, ARTHUR D, NIE, QING · 2009 to 2019
$2.4M
Synthesizing genome, epigenome, and transcriptome datasets in type 2 diabetes.R00DK099240 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI PARKER, STEPHEN CJ · 2015 to 2017
$726k
Machine learning methods to increase genomic accessibility by next-gen sequencingR01HG006870 · NHGRI · UNIVERSITY OF CALIFORNIA-IRVINE · PI XIE, XIAOHUI · 2012 to 2014
$662k
Intramural NIH HHS ZIA HG000024NHGRI NIH HHS R01 HG006870NIBIB NIH HHS T32 EB009418NIDDK NIH HHS R00 DK099240
6 · The paper itself

Abstract

backgroundStretch enhancers (SEs) are large chromatin-defined regulatory elements that are at least 3,000 base pairs (bps) long, in contrast to the median enhancer length of 800 bps. SEs tend to be cell-type specific, regulate cell-type specific gene expression, and are enriched in disease-associated genetic variants in disease-relevant cell types. Transcription factors (TFs) can bind to enhancers to modulate enhancer activity, and their sequence specificity can be represented by motifs. We hypothesize motifs can provide a biological context for how genetic variants contribute to disease.

resultsWe integrated chromatin state, gene expression, and chromatin accessibility [measured as DNase I Hypersensitive Sites (DHSs)] maps across nine different cell types. Motif enrichment analyses of chromatin-defined enhancer sequences identify several known cell-type specific "master" factors. Furthermore, de novo motif discovery not only recovers many of these motifs, but also identifies novel non-canonical motifs, providing additional insight into TF binding preferences. Across the length of SEs, motifs are most enriched in DHSs, though relative enrichment is also observed outside of DHSs. Interestingly, we show that single nucleotide polymorphisms associated with diseases or quantitative traits significantly overlap motif occurrences located in SEs, but outside of DHSs.

conclusionsThese results reinforce the role of SEs in influencing risk for diseases and suggest an expanded regulatory functional role for motifs that occur outside highly accessible chromatin. Furthermore, the motif signatures generated here expand our understanding of the binding preference of well-characterized TFs.

Identifiers

PMID26180553
PMCPMC4502539

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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.