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

Qilong Tan, Department 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 Xu, Department 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 Zhou, Department 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 Jia, Department 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 Jia, Department 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 Tu, Department 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 Zhou, Department 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.
Xifeng Wu, Department 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.

Document Type

Journal Article

Publication Date

5-30-2024

Journal

Molecular psychiatry

DOI

10.1038/s41380-024-02605-6

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.

Department

Surgery

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