Evidence map›Paper›PMID 32170004›Full record

ArticleJournal of medical genetics2020

Diverse types of genomic evidence converge on alcohol use disorder risk genes.

Yulin Dai, Ruifeng Hu, Guangsheng Pei, Huiping Zhang, Zhongming Zhao, Peilin Jia

Open access · greenAbstract read
In one paragraph

Article in Journal of medical genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.3field-weighted citation impact, top 21% 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

11 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Association ofbioRxiv : the preprint server for biology · 2021
    Article
  10. Article
  11. 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

6 authors at 3 institutions in 1 country.

Yulin DaiSchool of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0003-1874-7893
Ruifeng HuSchool of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Guangsheng PeiSchool of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Huiping ZhangDepartment of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, USA.
Zhongming ZhaoSchool of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Peilin JiaSchool of Biomedical Science, University of Texas Health Science Center at Houston, Houston, Texas, USA Peilin.Jia@uth.tmc.edu.
The University of Texas Health Science Center at Houston · USBoston University · USVanderbilt University Medical Center · US

Funding

Brain Tissue Resource Centre for Alcohol ResearchR28AA012725 · NIAAA · UNIVERSITY OF SYDNEY · PI Greg Trevor Sutherland · 2012 to 2026
$7.0M
Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
NIAAA NIH HHS R28 AA012725NLM NIH HHS R01 LM012806
6 · The paper itself

Abstract

backgroundAlcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%-60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations.

methodsTo explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes).

resultsWe identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them.

conclusionWe characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

Indexed as

Genome-Wide Association StudyAlcoholismBrainCerebellumComputational BiologyDNA MethylationEpigenomicsFemaleFrontal LobeGene Expression RegulationGenetic Association StudiesGenetic Predisposition to DiseaseGenome, HumanGenomicsHippocampusHumansalcohol use disorderepigeneticsGWASintegrative studyTWAS

Identifiers

PMID32170004
PMCPMC7487038
OpenAlexW3011849883

What OpenQuestion holds

Textmetadata
LicenceTDM
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.