Evidence map›Paper›PMID 38816585›Full record

ArticleMolecular psychiatry2024

A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.

Qilong Tan, Xiaohang Xu, Hanyi Zhou, Junlin Jia, Yubing Jia, Huakang Tu, Dan Zhou, Xifeng Wu

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Article in Molecular psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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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

8 authors.

Qilong TanDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Xiaohang XuDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Hanyi ZhouDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Junlin JiaDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Yubing JiaDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Huakang TuDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
Dan ZhouDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.ORCID 0000-0002-5313-8164
Xifeng WuDepartment of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China. xifengw@zju.edu.cn.ORCID 0000-0003-2980-8129

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

Indexed as

Cerebral CortexGenome-Wide Association StudyQuantitative Trait LociSmokingTranscriptomeWhite PeopleBlack PeopleEast Asian PeopleFemaleGenetic Predisposition to DiseaseHumansMalePolymorphism, Single NucleotideTobacco Use Disorder

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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.