Identifying Causal Genes for Depression Via Integration of the Proteome and Transcriptome from Brain and Blood

Overview

Journal Mol Psychiatry

Specialties Molecular Biology
Psychiatry

Date 2022 Mar 17

PMID 35296807

Authors

Yue-Ting Deng

Ya-Nan Ou

Bang-Sheng Wu

Yu-Xiang Yang

Yan Jiang

Yu-Yuan Huang

Yi Liu

Lan Tan

Qiang Dong

John Suckling

Fei Li

Jin-Tai Yu

Affiliations

Soon will be listed here.

Abstract

Genome-wide association studies (GWASs) have identified numerous risk genes for depression. Nevertheless, genes crucial for understanding the molecular mechanisms of depression and effective antidepressant drug targets are largely unknown. Addressing this, we aimed to highlight potentially causal genes by systematically integrating the brain and blood protein and expression quantitative trait loci (QTL) data with a depression GWAS dataset via a statistical framework including Mendelian randomization (MR), Bayesian colocalization, and Steiger filtering analysis. In summary, we identified three candidate genes (TMEM106B, RAB27B, and GMPPB) based on brain data and two genes (TMEM106B and NEGR1) based on blood data with consistent robust evidence at both the protein and transcriptional levels. Furthermore, the protein-protein interaction (PPI) network provided new insights into the interaction between brain and blood in depression. Collectively, four genes (TMEM106B, RAB27B, GMPPB, and NEGR1) affect depression by influencing protein and gene expression level, which could guide future researches on candidate genes investigations in animal studies as well as prioritize antidepressant drug targets.

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