Evidence map›Paper›PMID 30547845›Full record

ArticleBMC systems biology2018

Co-expression of long non-coding RNAs and autism risk genes in the developing human brain.

Steven B Cogill, Anand K Srivastava, Mary Qu Yang, Liangjiang Wang

Open access · diamondAbstract read
In one paragraph

Article in BMC systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

20 citing papers in PubMed, 29 citations in OpenAlex.

  1. The Role of Long Non-Coding RNA in Anxiety Disorders: A Literature Review.International journal of molecular sciences · 2025
    Review
  2. Review
  3. Article
  4. Emerging Role of Non-coding RNAs in Autism Spectrum Disorder.Journal of molecular neuroscience : MN · 2022
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. From Junk to Function: LncRNAs in CNS Health and Disease.Frontiers in molecular neuroscience · 2021
    Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Genomic data mining for functional annotation of human long noncoding RNAs.Journal of Zhejiang University. Science. B · 2019
    Review
  19. Article
  20. 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

4 authors at 3 institutions in 1 country.

Steven B CogillDepartment of Genetics and Biochemistry, Clemson University, Clemson, SC, 29646, USA.
Anand K SrivastavaJ.C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, SC, 29646, USA.
Mary Qu YangMidSouth Bioinformatics Center, Department of Information Science, University of Arkansas at Little Rock, Little Rock, AR, 72204, USA.
Liangjiang WangDepartment of Genetics and Biochemistry, Clemson University, Clemson, SC, 29646, USA. liangjw@clemson.edu.
Clemson University · USGreenwood Genetic Center · USUniversity of Arkansas at Little Rock · US

Funding

Understanding Hesitant AdoptersP20GM103429 · NIGMS · UNIV OF ARKANSAS FOR MED SCIS · PI Lawrence E Cornett, JERRY WARE · 2012 to 2026
$60.9M
Developing integrative approaches for identifying disease-causing genes and dysfunctional networksR15GM114739 · NIGMS · UNIVERSITY OF ARKANSAS AT LITTLE ROCK · PI YANG, MARY · 2015 to 2015
$374k
NIGMS NIH HHS P20 GM103429NIGMS NIH HHS R15 GM114739
6 · The paper itself

Abstract

backgroundAutism Spectrum Disorder (ASD) is the umbrella term for a group of neurodevelopmental disorders convergent on behavioral phenotypes. While many genes have been implicated in the disorder, the predominant focus of previous research has been on protein coding genes. This leaves a vast number of long non-coding RNAs (lncRNAs) not characterized for their role in the disorder although lncRNAs have been shown to play important roles in development and are highly represented in the brain. Studies have also shown lncRNAs to be differentially expressed in ASD affected brains. However, there has yet to be an enrichment analysis of the shared ontologies and pathways of known ASD genes and lncRNAs in normal brain development.

resultsIn this study, we performed co-expression network analysis on the developing brain transcriptome to identify potential lncRNAs associated with ASD and possible annotations for functional role of lncRNAs in brain development. We found co-enrichment of lncRNA genes and ASD risk genes in two distinct groups of modules showing elevated prenatal and postnatal expression patterns, respectively. Further enrichment analysis of the module groups indicated that the early expression modules were comprised mainly of transcriptional regulators while the later expression modules were associated with synapse formation. Finally, lncRNAs were prioritized for their connectivity with the known ASD risk genes through analysis of an adjacency matrix. Collectively, the results imply early developmental repression of synaptic genes through lncRNAs and ASD transcriptional regulators.

conclusionHere we demonstrate the utility of mining the publically available brain gene expression data to further functionally annotate the role of lncRNAs in ASD. Our analysis indicates that lncRNAs potentially have a key role in ASD due to their convergence on shared pathways, and we identify lncRNAs of interest that may lead to further avenues of study.

Indexed as

Gene Expression RegulationAutistic DisorderBrainGene Regulatory NetworksGenetic Predisposition to DiseaseHumansRNA, Long NoncodingSynapsesTranscription, GeneticRNA, Long NoncodingAutismCo-expression networkDeveloping brain transcriptomelncRNA

Identifiers

PMID30547845
PMCPMC6293492
OpenAlexW2903726785

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.