ImportanceLarge-scale genome-wide association studies (GWAS) should ideally inform the development of pharmacological treatments, but whether GWAS-identified mechanisms of disease liability correspond to the pathophysiological processes targeted by current pharmacological treatments is unclear.ObjectiveTo investigate whether functional information from a range of open bioinformatics datasets can elucidate the relationship between GWAS-identified genetic variation and the genes targeted by current treatments for psychiatric disorders.Design, Setting, and ParticipantsAssociations between GWAS-identified genetic variation and pharmacological treatment targets were investigated across 4 psychiatric disorders—attention-deficit/hyperactivity disorder, bipolar disorder, schizophrenia, and major depressive disorder. Using a candidate set of 2232 genes listed as targets for all approved treatments in the DrugBank database, each gene was independently assigned 2 scores for each disorder—one based on its involvement as a treatment target and the other based on the mapping between GWAS-implicated single-nucleotide variants (SNVs) and genes according to 1 of 4 bioinformatic data modalities: SNV position, gene distance on the protein-protein interaction (PPI) network, brain expression quantitative trail locus (eQTL), and gene expression patterns across the brain. Study data were analyzed from November 2023 to September 2024.Main Outcomes and MeasuresGene scores for pharmacological treatments and GWAS-implicated genes were compared using a measure of weighted similarity applying a stringent null hypothesis–testing framework that quantified the specificity of the match by comparing identified associations for a particular disorder with a randomly selected set of treatments.ResultsIncorporating information derived from functional bioinformatics data in the form of a PPI network revealed links for bipolar disorder (P permutation [P-perm] = 7 × 10−4; weighted similarity score, empirical [ρ-emp] = 0.1347; mean [SD] weighted similarity score, random [ρ-rand] = 0.0704 [0.0163]); however, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeded null expectations. Exploratory analysis assessing the overlap between the GWAS-identified genetic architecture and treatment targets across disorders identified that most disorder pairs and mapping methods did not show a significant correspondence.Conclusions and RelevanceIn this bioinformatic study, the relatively low degree of correspondence across modalities suggests that the genetic architecture driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms currently used for targeting symptom manifestations through pharmacological treatments. Novel approaches incorporating insights derived from GWAS based on refined phenotypes including treatment response may assist in mapping disorder risk genes to pharmacological treatments in the long term.

中文翻译：

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将全基因组关联研究与精神疾病的药物治疗联系起来


重要性大规模全基因组关联研究 （GWAS） 理想情况下应该为药物治疗的发展提供信息，但 GWAS 确定的疾病易感机制是否与当前药物治疗所针对的病理生理过程相对应尚不清楚。目的研究来自一系列开放生物信息学数据集的功能信息是否可以阐明 GWAS 鉴定的遗传变异与当前精神疾病治疗所针对的基因之间的关系。设计、设置和参与者在 4 种精神疾病（注意力缺陷/多动障碍、双相情感障碍、精神分裂症和重度抑郁症）中调查了 GWAS 确定的遗传变异与药物治疗靶点之间的关联。使用 DrugBank 数据库中列为所有已批准治疗靶标的 2232 个候选基因集，为每个基因为每种疾病独立分配 2 个分数——一个基于其作为治疗靶点的参与，另一个基于与 GWAS 相关的单核苷酸变异 （SNV） 和基因之间的映射根据 4 种生物信息学数据模式中的 1 种： SNV 位置、蛋白质-蛋白质相互作用 （PPI） 网络上的基因距离、大脑表达定量追踪基因座 （eQTL） 以及整个大脑的基因表达模式。主要结局和措施使用加权相似性测量方法比较药物治疗和 GWAS 相关基因的基因评分，应用严格的零假设测试框架，该框架通过将特定疾病的已识别关联与随机选择的一组治疗进行比较来量化匹配的特异性。结果以 PPI 网络的形式整合来自功能生物信息学数据的信息揭示了双相情感障碍的联系 （P 排列 [P-perm] = 7 × 10−4;加权相似性分数，经验 [ρ-EMP] = 0.1347;平均 [SD] 加权相似性分数，随机 [ρ-rand] = 0.0704 [0.0163]）;然而，精神疾病中治疗靶点与 GWAS 相关基因之间的总体对应关系很少超过零预期。评估 GWAS 确定的遗传结构和跨疾病治疗靶点之间重叠的探索性分析发现，大多数疾病对和映射方法没有显示出显着的对应关系。结论和相关性在这项生物信息学研究中，不同模式之间的相对较低的对应程度表明，驱动精神疾病风险的遗传结构可能与目前用于通过药物治疗靶向症状表现的病理生理机制不同。结合基于精细表型（包括治疗反应）的 GWAS 见解的新方法可能有助于将疾病风险基因长期映射到药物治疗。