Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.
Identifying drug targets for neurological and psychiatric disease via genetics and the brain transcriptome.
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DOI：
10.1371/journal.pgen.1009224
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发表时间：
2021-01
期刊：
影响因子：
4.5
通讯作者：
AMP-AD eQTL working group
中科院分区：
文献类型：
--
作者：
Baird DA;Liu JZ;Zheng J;Sieberts SK;Perumal T;Elsworth B;Richardson TG;Chen CY;Carrasquillo MM;Allen M;Reddy JS;De Jager PL;Ertekin-Taner N;Mangravite LM;Logsdon B;Estrada K;Haycock PC;Hemani G;Runz H;Smith GD;Gaunt TR;AMP-AD eQTL working group
发现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标表达的遗传变体来评估治疗干预的功效。因此，我们采用孟德尔随机化（MR）在脑组织中测量的基因表达，以确定涉及神经和精神疾病的药物靶点。我们进行了一项双样本MR，使用来自加速阿尔茨海默病药物合作联盟（AMP-AD）和CommonMind联盟（CMC）荟萃分析研究（n = 1，286）的顺式作用脑源性表达数量性状基因座（eQTL）作为遗传工具，以预测7，137个基因对12种神经和精神疾病的影响。我们对最常见的MR结果进行了贝叶斯共定位分析（使用P<6x 10 -7作为证据阈值，Bonferroni校正了80，557次MR测试），以确认每个基因组区域中基因表达和性状之间共享相同的因果变异。然后，我们将共定位的基因与人类在线孟德尔遗传（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标的基因进行比对，以确定有希望的药物靶标。80个eQTL显示了因果效应的MR证据，我们根据与该性状的共定位优先考虑了47个基因。我们因果关系的表达23个基因与精神分裂症和一个单一的基因与厌食症，双相情感障碍和抑郁症的精神疾病和9个基因与阿尔茨海默氏病，6个基因与帕金森氏病，4个基因与多发性硬化症和两个基因与肌萎缩侧索硬化症的神经系统疾病，我们测试。从这些基因中，我们确定了五个基因（ACE，GPNMB，KCNQ 5，RERE和SUOX）作为有吸引力的药物靶点，可能需要在功能研究和临床试验中进行随访，证明了本研究设计对发现神经精神疾病药物靶点的价值。遗传关联研究已经成功地确定了许多与疾病风险相关的遗传变异，但确定这些基因的作用更具挑战性。这一点很重要，因为这些基因可能编码针对这些疾病的有效药物靶点。我们使用孟德尔随机化（MR）和共定位，这两种方法结合利用这些遗传变异来估计单个基因的因果效应。我们使用来自AMP-AD和CMC脑表达定量位点数据集的数据将这种方法应用于12种神经和精神疾病，该数据集足够大，可以为遗传变异和基因表达之间的关系提供强有力的证据。我们发现，在我们测试的12种疾病中，47个基因表达的变化与疾病风险增加之间存在因果关系。由于具有人类遗传证据的药物靶点更有可能在临床试验中获得批准，这些发现提供了一个有价值的潜在治疗靶点列表，包括ACE，GPNMB，KCNQ 5，RERE和SUOX基因。
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. Genetic association studies have been successful in identifying many genetic variants associated with disease risk, but it has been far more challenging to determine the genes through which these act. This is important, because such genes may encode effective drug targets for these diseases. We used Mendelian randomization (MR) and colocalization, two methods which in combination exploit these genetic variants to estimate the causal effects of individual genes. We applied this approach to 12 neurological and psychiatric diseases using data from the AMP-AD and CMC brain expression quantitative locus dataset, which is large enough to provide robust evidence for the relationship between genetic variants and gene expression. We found a causal relationship between the change in expression of 47 genes and increased disease risk across the 12 diseases we tested. As drug targets with human genetic evidence are far more likely to be approved in clinical trials, these findings provide a valuable list of potential therapeutic targets, including the ACE, GPNMB, KCNQ5, RERE and SUOX genes.
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影响因子：
11
作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
通讯作者：
International Obsessive Compulsive Disorder Foundation Genetics Collaborative (IOCDF-GC) and OCD Collaborative Genetics Association Studies (OCGAS)
影响因子：
6.2
作者：
Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium
通讯作者：
Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium
影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
3.7
作者：
Imbrici P;Camerino DC;Tricarico D
通讯作者：
Tricarico D