Gong Weiming, Guo Ping, Liu Lu, Yan Ran, Liu Shuai, Wang Shukang, Xue Fuzhong, Zhou Xiang, Sun Xiubin, Yuan Zhongshang
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China; Institute for Medical Dataology, Shandong University, Jinan, Shandong, China.
Department of Biostatistics, University of Michigan, Ann Arbor, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, USA.
J Affect Disord. 2025 Feb 1;370:124-133. doi: 10.1016/j.jad.2024.10.126. Epub 2024 Nov 2.
Genome-wide association studies (GWAS) have identified numerous variants associated with psychiatric disorders. However, it remains largely unknown on how GWAS risk variants contribute to psychiatric disorders.
Through integrating two largest, publicly available, independent protein quantitative trait loci datasets of plasma protein and nine large-scale GWAS summary statistics of psychiatric disorders, we first performed proteome-wide association study (PWAS) to identify psychiatric disorders-associated plasma proteins, followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we conducted Mendelian randomization (MR) and Bayesian colocalization (COLOC) analyses, with both discovery and parallel replication datasets, to further identify protein-disorder pairs with putatively causal relationships. We finally prioritized the potential drug targets using Drug Gene Interaction Database.
PWAS totally identified 112 proteins, which were significantly enriched in biological processes relevant to immune regulation and response to stimulus including regulation of immune system process (adjusted P = 1.69 × 10) and response to external stimulus (adjusted P = 4.13 × 10), and viral infection related pathways, including COVID-19 (adjusted P = 2.94 × 10). MR and COLOC analysis further identified 26 potentially causal protein-disorder pairs in both discovery and replication analysis. Notably, eight protein-coding genes were immune-related, such as IRF3, CSK, and ACE, five among 16 druggable genes were reported to interact with drugs, including ACE, CSK, PSMB4, XPNPEP1, and MICB.
Our findings highlighted the immunological hypothesis and identified potentially causal plasma proteins for psychiatric disorders, providing biological insights into the pathogenesis and benefit the development of preventive or therapeutic drugs for psychiatric disorders.
全基因组关联研究（GWAS）已鉴定出许多与精神疾病相关的变异。然而，GWAS风险变异如何导致精神疾病在很大程度上仍不清楚。
通过整合两个最大的、公开可用的、独立的血浆蛋白蛋白质定量性状位点数据集以及九个大规模精神疾病GWAS汇总统计数据，我们首先进行了全蛋白质组关联研究（PWAS）以鉴定与精神疾病相关的血浆蛋白，随后进行富集分析以揭示潜在的生物学过程和途径。然后，我们使用发现数据集和平行复制数据集进行孟德尔随机化（MR）和贝叶斯共定位（COLOC）分析，以进一步确定具有潜在因果关系的蛋白质-疾病对。我们最终使用药物基因相互作用数据库对潜在的药物靶点进行了优先级排序。
PWAS共鉴定出112种蛋白质，这些蛋白质在与免疫调节和对刺激的反应相关的生物学过程中显著富集，包括免疫系统过程的调节（校正P = 1.69×10）和对外部刺激的反应（校正P = 4.13×10），以及与病毒感染相关的途径，包括COVID-19（校正P = 2.94×10）。MR和COLOC分析在发现和复制分析中进一步确定了26对潜在的因果蛋白质-疾病对。值得注意的是，八个蛋白质编码基因与免疫相关，如IRF3、CSK和ACE，16个可成药基因中有五个据报道与药物相互作用，包括ACE、CSK、PSMB4、XPNPEP1和MICB。
我们的研究结果突出了免疫学假说，并确定了精神疾病潜在的因果血浆蛋白，为发病机制提供了生物学见解，并有利于精神疾病预防或治疗药物的开发。
