MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, University of Bristol, Bristol, United Kingdom.
Translational Biology, Research and Development, Cambridge, Massachusetts, United States of America.
PLoS Genet. 2021 Jan 8;17(1):e1009224. doi: 10.1371/journal.pgen.1009224. eCollection 2021 Jan.
Discovering drugs that efficiently treat brain diseases has been challenging. Genetic variants that modulate the expression of potential drug targets can be utilized to assess the efficacy of therapeutic interventions. We therefore employed Mendelian Randomization (MR) on gene expression measured in brain tissue to identify drug targets involved in neurological and psychiatric diseases. We conducted a two-sample MR using cis-acting brain-derived expression quantitative trait loci (eQTLs) from the Accelerating Medicines Partnership for Alzheimer's Disease consortium (AMP-AD) and the CommonMind Consortium (CMC) meta-analysis study (n = 1,286) as genetic instruments to predict the effects of 7,137 genes on 12 neurological and psychiatric disorders. We conducted Bayesian colocalization analysis on the top MR findings (using P<6x10-7 as evidence threshold, Bonferroni-corrected for 80,557 MR tests) to confirm sharing of the same causal variants between gene expression and trait in each genomic region. We then intersected the colocalized genes with known monogenic disease genes recorded in Online Mendelian Inheritance in Man (OMIM) and with genes annotated as drug targets in the Open Targets platform to identify promising drug targets. 80 eQTLs showed MR evidence of a causal effect, from which we prioritised 47 genes based on colocalization with the trait. We causally linked the expression of 23 genes with schizophrenia and a single gene each with anorexia, bipolar disorder and major depressive disorder within the psychiatric diseases and 9 genes with Alzheimer's disease, 6 genes with Parkinson's disease, 4 genes with multiple sclerosis and two genes with amyotrophic lateral sclerosis within the neurological diseases we tested. From these we identified five genes (ACE, GPNMB, KCNQ5, RERE and SUOX) as attractive drug targets that may warrant follow-up in functional studies and clinical trials, demonstrating the value of this study design for discovering drug targets in neuropsychiatric diseases.
发现能够有效治疗脑部疾病的药物一直具有挑战性。可以利用调节潜在药物靶点表达的遗传变异来评估治疗干预措施的效果。因此,我们利用基于组织的顺式作用脑衍生表达数量性状基因座(eQTL)的孟德尔随机化(MR)方法,鉴定与神经和精神疾病相关的药物靶点。我们使用来自阿尔茨海默病加速药物合作组织(AMP-AD)和常见思维合作组织(CMC)荟萃分析研究的顺式作用脑衍生表达数量性状基因座(eQTL)(n = 1,286)作为遗传工具进行两样本 MR,以预测 7,137 个基因对 12 种神经和精神疾病的影响。我们对 top MR 发现(使用 P<6x10-7 作为证据阈值,针对 80,557 个 MR 检验进行 Bonferroni 校正)进行了贝叶斯共定位分析,以确认在每个基因组区域中基因表达和特征之间共享相同的因果变异。然后,我们将共定位的基因与 Online Mendelian Inheritance in Man(OMIM)中记录的已知单基因疾病基因以及 Open Targets 平台中注释为药物靶点的基因进行交叉,以鉴定有希望的药物靶点。80 个 eQTL 显示出因果效应的 MR 证据,我们根据与特征的共定位,从这些证据中优先选择了 47 个基因。我们将 23 个基因与精神分裂症的表达相关联,在精神疾病中,与厌食症、双相情感障碍和重度抑郁症各有一个基因相关联,在神经疾病中,与阿尔茨海默病的 9 个基因、帕金森病的 6 个基因、多发性硬化症的 4 个基因和肌萎缩侧索硬化症的 2 个基因相关联。从中我们确定了五个基因(ACE、GPNMB、KCNQ5、RERE 和 SUOX)作为有吸引力的药物靶点,这可能值得在功能研究和临床试验中进一步研究,证明了这种研究设计在发现神经精神疾病药物靶点方面的价值。