Evidence map›Paper›PMID 35052378›Full record

SynthesisGenes2021

Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction.

Zhenyao Ye, Chen Mo, Hongjie Ke, Qi Yan, Chixiang Chen, Peter Kochunov, L Elliot Hong, Braxton D Mitchell, Shuo Chen, Tianzhou Ma

Open access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in Genes, 2021. 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
0.5field-weighted citation impact, top 28% 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. Review
  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

10 authors at 3 institutions in 1 country.

Zhenyao YeMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Chen MoMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Hongjie KeDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA.
Qi YanIrving Medical Center, Department of Obstetrics & Gynecology, Columbia University, New York, NY 10032, USA.
Chixiang ChenDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Peter KochunovMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
L Elliot HongMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Braxton D MitchellDepartment of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA.ORCID 0000-0003-4920-4744
Shuo ChenMaryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21201, USA.
Tianzhou MaDepartment of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA.ORCID 0000-0003-3605-0811
University of Maryland, Baltimore · USUniversity of Maryland, College Park · USColumbia University Irving Medical Center · US

Funding

SOLAR-Eclipse Computational Tools for Imaging GeneticsR01EB015611 · NIBIB · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI KOCHUNOV, PETER V. · 2012 to 2024
$5.0M
A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use ResearchDP1DA048968 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI CHEN, SHUO · 2019 to 2023
$2.3M
Redefine Trans-Neuropsychiatric Disorder Brain Patterns through Big-Data and Machine LearningRF1MH123163 · NIMH · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V., THOMPSON, PAUL M · 2021 to 2021
$1.2M
Hybrid GPU/CPU Computing Resource to Support Connectomic and GenomicsS10OD023696 · OD · UNIVERSITY OF MARYLAND BALTIMORE · PI KOCHUNOV, PETER V. · 2018 to 2018
$593k
Medical Research Council MC_PC_17228Medical Research Council MC_QA137853NIBIB NIH HHS R01 EB015611NIDA NIH HHS DP1 DA048968NIH HHS S10 OD023696NIMH NIH HHS RF1 MH123163
6 · The paper itself

Abstract

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

Indexed as

AdultAgedBrainFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLinkage DisequilibriumMaleMiddle AgedPhenotypePolymorphism, Single NucleotideQuantitative Trait LociTobacco Use DisorderTranscriptomeexpression quantitative trait locigenome-wide association studymeta-analysisnicotine addictiontranscriptome-wide association study

Identifiers

PMID35052378
PMCPMC8775257
OpenAlexW4226173355

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.