Fine-mapping psychiatricdisease variants that affect post-transcriptional gene regulation

精细绘制影响转录后基因调控的精神疾病变异

• 批准号: 10445082
• 负责人: William G Fairbrother
• 金额: $ 72.94万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-05 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10445082
• 关键词: Affect; Alleles; Alternative Splicing; Alzheimer' s Disease; American; Autopsy; Biological Assay; Bipolar Disorder; Brain; Candidate Disease Gene; Cell physiology; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Development; Diagnostic; Disease; Electrophysiology (science); Engineering; Etiology; Evaluation; Exhibits; Exons; FURIN gene; Functional disorder; Gene Expression; Genes; Genetic; Genetic Diseases; Genetic Engineering; Genomics; Genotype; Glutamates; Goals; Heritability; Human; Impairment; In Vitro; Individual; Induced pluripotent stem cell derived neurons; Introns; Libraries; Machine Learning; Maps; Mediating; Mental disorders; Modeling; Modification; Molecular; Mutation; Neurons; Pathogenesis; Pathogenicity; Pathway interactions; Patients; Phenotype; Post-Transcriptional Regulation; Protein Isoforms; Proteins; Quantitative Trait Loci; RNA; RNA Processing; RNA Splicing; Reporter; Research; Risk; Risk Factors; Role; Schizophrenia; Single Nucleotide Polymorphism; Spliced Genes; Structure; Susceptibility Gene; Synapses; Testing; Transcriptional Regulation; Untranslated RNA; Untranslated Regions; Variant; Work; autism spectrum disorder; base; causal variant; cell type; clinical predictors; cohort; disorder risk; falls; follow-up; genetic variant; genome wide association study; genome-wide; improved; in silico; induced pluripotent stem cell; insight; machine learning method; mutant; neuropsychiatric disorder; neuropsychiatry; novel; novel therapeutic intervention; outcome prediction; overexpression; personalized approach; predictive modeling; risk variant; standard care; stem cell biology; stem cell model; synaptic function; therapeutic target; tool; transcriptomics

PROJECT SUMMARY Neuropsychiatric disorders (NPD) such as schizophrenia (SZ), autism spectrum disorders (ASD) and bipolar disorders (BD) are remarkably common, with SZ alone affecting nearly three million Americans. Despite more than fifty years of research, no cures exist for these conditions and the standard of treatment remains unsatisfactory. Genome-wide association studies (GWAS) indicate that, in addition to highly penetrant rare mutations, NPD risk also reflects the impact of hundreds of common single nucleotide polymorphisms with small effect sizes. A major challenge in the field has been illuminating the pathways connecting these genetic variants (the vast majority of which fall in non-coding sequences) to target genes and causal cellular phenotypes. To understand how these myriad risk loci causally contribute to disease risk, it is essential to screen for putatively causal variant(s) and determine how they influence gene expression, which has been shown to be cell-type specific, as well as cellular function. Recent evidence has emerged indicating a substantial contribution of RNA splicing variation to heritability across many complex genetic diseases, including SZ. Based on our preliminary analyses and the work of others, we hypothesize that a substantial proportion of NPD GWAS loci exert their pathogenic effects on neuronal function by impacting RNA: its structure, modifications, protein interactions and splicing. To test this, we will apply novel tools and machine learning methods to predict and quantify RNA splicing in the largest SZ, ASD and BD GWAS, in order to predict splicing quantitative trait loci (sQTLs, Aim 1). To confirm true effects on exon inclusion independently in glutamatergic and GABAergic neurons (i.e., the major cell-types impacted in NPD), up to several thousand of the predicted splice variants will be tested by a massively parallel reporter assay, MaPSy (Aim 2). Finally, in order to evaluate the cell-type-specific impact of putative causal sQTLs identified in Aims 1 and 2 on neuronal maturation and synaptic function, we will use CRISPR gene editing to engineer these mutations within human induced pluripotent stem cell (hiPSC)-based models of both neural cell types (Aim 3). Our overarching goal is to map and functionally evaluate the NPD-GWAS loci that impact alternative splicing and neuronal function. Our work may impact the field by delivering new insights into the role of common variants in NPD pathophysiology, which could inform ways of improving diagnostics, predicting clinical trajectories, and developing novel therapeutic interventions.

项目摘要 神经精神障碍（NPD），如精神分裂症（SZ）、自闭症谱系障碍（ASD）和双相情感障碍（BPD） 疾病（BD）是非常常见的，仅SZ就影响了近300万美国人。尽管有更多 经过50多年的研究，这些疾病没有治愈方法，治疗标准仍然存在。 不满意。全基因组关联研究（GWAS）表明，除了高渗透率罕见的， 突变，NPD风险也反映了数百种常见的单核苷酸多态性的影响， 效果大小。该领域的一个主要挑战是阐明连接这些遗传变异的途径 (the其中绝大多数属于非编码序列）靶向基因和致病细胞表型。到 了解这些无数的风险位点是如何因果地导致疾病风险的， 因果变异，并确定它们如何影响基因表达，这已被证明是细胞型 特异性，以及细胞功能。最近的证据表明，RNA的重要贡献 剪接变异到许多复杂遗传疾病的遗传性，包括SZ。根据我们初步的 分析和其他人的工作，我们假设相当大比例的NPD GWAS基因座发挥其功能， 通过影响RNA对神经元功能的致病作用：其结构、修饰、蛋白质相互作用和 拼接为了验证这一点，我们将应用新的工具和机器学习方法来预测和量化RNA剪接。 在最大的SZ，ASD和BD GWAS中，以预测剪接数量性状位点（sQTL，Aim 1）。确认 在谷氨酸能和GABA能神经元中独立地对外显子包含的真实影响（即，主要细胞类型 受NPD影响），将通过大规模平行测试多达数千种预测的剪接变体 报告基因测定，MaPSy（Aim 2）。最后，为了评估推定的因果sQTL的细胞类型特异性影响， 在目标1和2中确定的关于神经元成熟和突触功能的基因编辑中，我们将使用CRISPR基因编辑来 在基于人诱导多能干细胞（hiPSC）的神经细胞和神经细胞模型中设计这些突变， 类型（目标3）。我们的首要目标是绘制和功能评估NPD-GWAS基因座， 选择性剪接和神经元功能。我们的工作可能会通过提供对角色的新见解来影响该领域 NPD病理生理学中的常见变异，这可以为改善诊断，预测 临床轨迹，并开发新的治疗干预措施。