Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China.
Schizophr Bull. 2023 Sep 7;49(5):1305-1315. doi: 10.1093/schbul/sbad100.
Psychiatric disorders impose a huge health and economic burden on modern society. However, there is currently no proven completely effective treatment available, partly owing to the inefficiency of drug target identification and validation. We aim to identify therapeutic targets relevant to psychiatric disorders by conducting Mendelian randomization (MR) analysis.
We performed genome-wide MR analysis by integrating expression quantitative trait loci (eQTL) of 4479 actionable genes that encode druggable proteins and genetic summary statistics from genome-wide association studies of psychiatric disorders. After conducting colocalization analysis on the brain MR findings, we employed protein quantitative trait loci (pQTL) data as genetic proposed instruments for intersecting the colocalized genes to provide further genetic evidence.
By performing MR and colocalization analysis with eQTL genetic instruments, we obtained 31 promising drug targets for psychiatric disorders, including 21 significant genes for schizophrenia, 7 for bipolar disorder, 2 for depression, 1 for attention deficit and hyperactivity (ADHD) and none for autism spectrum disorder. Combining MR results using pQTL genetic instruments, we finally proposed 8 drug-targeting genes supported by the strongest MR evidence, including gene ACE, BTN3A3, HAPLN4, MAPK3 and NEK4 for schizophrenia, gene NEK4 and HAPLN4 for bipolar disorder, and gene TIE1 for ADHD.
Our findings with genetic support were more likely to be to succeed in clinical trials. In addition, our study prioritizes approved drug targets for the development of new therapies and provides critical drug reuse opportunities for psychiatric disorders.
精神疾病给现代社会带来了巨大的健康和经济负担。然而,目前还没有被证实的完全有效的治疗方法,部分原因是药物靶点的识别和验证效率低下。我们旨在通过进行孟德尔随机化(MR)分析来确定与精神疾病相关的治疗靶点。
我们通过整合 4479 个可作用于药物的蛋白质编码基因的表达数量性状基因座(eQTL)和精神疾病的全基因组关联研究的遗传汇总统计数据,进行了全基因组 MR 分析。在对大脑 MR 发现进行共定位分析后,我们使用蛋白质数量性状基因座(pQTL)数据作为遗传拟议工具来交叉共定位基因,以提供进一步的遗传证据。
通过使用 eQTL 遗传工具进行 MR 和共定位分析,我们获得了 31 个有希望的精神疾病药物靶点,包括 21 个精神分裂症的显著基因、7 个双相情感障碍的基因、2 个抑郁症的基因、1 个注意力缺陷多动障碍(ADHD)的基因和 0 个自闭症谱系障碍的基因。结合使用 pQTL 遗传工具的 MR 结果,我们最终提出了 8 个药物靶向基因,这些基因得到了最强的 MR 证据支持,包括精神分裂症的基因 ACE、BTN3A3、HAPLN4、MAPK3 和 NEK4,双相情感障碍的基因 NEK4 和 HAPLN4,以及 ADHD 的基因 TIE1。
我们的研究结果得到了遗传支持,更有可能在临床试验中取得成功。此外,我们的研究为新疗法的开发确定了已批准的药物靶点,并为精神疾病提供了关键的药物再利用机会。