Large-scale transcriptome and epigenome association analysis across multiple traits

跨多个性状的大规模转录组和表观基因组关联分析

• 批准号: 10584192
• 负责人: Panagiotis Roussos
• 金额: --
• 依托单位: JAMES J PETERS VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584192
• 关键词: Address; Affect; Area; Autoimmune; Awareness; Biological; Brain; Brain region; COVID-19; Cells; Characteristics; Chromatin; Clinical; Code; Communities; Data; Data Set; Detection; Development; Disease; Drug Targeting; Electronic Health Record; Epigenetic Process; Functional disorder; Future; Gene Expression; Gene Targeting; Generations; Genes; Genetic; Genetic Variation; Genome; Genomic approach; Genomics; Genotype; Goals; Graph; Heritability; High Prevalence; Human; Individual; Intervention; Joints; Libraries; Machine Learning; Methods; Modeling; Molecular; Molecular Profiling; Outcome; Participant; Pathway interactions; Patients; Pharmaceutical Preparations; Phenotype; Positioning Attribute; Prevention; Proteome; Proteomics; Psychiatry; Regulation; Research; Research Personnel; Resolution; Resources; Risk; Role; Sampling; Specificity; Structure; Testing; Therapeutic; Therapeutic Effect; Therapeutic Intervention; Tissues; Untranslated RNA; Variant; Veterans; biobank; brain tissue; causal variant; cell type; deep learning; disorder risk; drug development; drug discovery; drug efficacy; economic cost; epigenome; gene interaction; genetic analysis; genetic architecture; genetic variant; genome wide association study; genome-wide; histone modification; improved; indexing; individualized medicine; large scale data; molecular scale; mortality; multiple omics; neuropsychiatry; next generation; novel; novel strategies; patient subsets; phenome; precision medicine; predictive modeling; programs; psychopharmacologic; risk variant; severe mental illness; therapeutic evaluation; trait; transcriptome; transcriptomics; treatment response

PROJECT SUMMARY Precision Psychiatry is an emerging approach that considers patients’ characteristics to customize prevention and treatment for serious mental illness. The Million Veteran Program (MVP) is the largest and most comprehensive biobank in the world, currently involving multi-ancestry genetic data from more than 650,000 Veterans and highly dense electronic health record information that fully captures the clinical characteristics of each participant. Given the high prevalence of serious mental illness among our Veterans, MVP provides a unique opportunity to perform large-scale genetic discovery that will further our understanding of the pathophysiology of serious mental illness and promote Precision Psychiatry. While well-powered genome-wide association studies (GWAS) have identified multiple risk variants across serious mental illness, there have been limited conclusive findings on the functional relevance of most discovered loci due to small effect size, overlap with non-coding regions of the genome and unclear mechanisms through which they act. Our group and others have shown that a large portion of phenotypic variability in disease risk can be explained by regulatory variants with cell type specificity, i.e. genetic variants that affect epigenetic mechanisms and the expression levels of genes. Studying gene expression and epigenome changes directly in MVP samples is not feasible as such data are not available. To overcome these limitations, we propose to take advantage of large-scale datasets with genotyping and multiscale molecular profiling that our group and others have generated in human brain tissue and apply machine learning approaches to directly impute genome-wide transcriptomes, epigenomes and proteomes in MVP samples using the existing MVP genotypes. The primary goals of our project are threefold: First, imputed MVP transcriptomes, epigenomes and proteomes will be meta-analyzed to single tissue-specific gene dysregulation scores for each individual via a novel method, called PolyXcan, which leverages a data- driven correlation-aware meta-analytical framework and performs joint multi-omics-wide association studies. For each serious mental illness, key gene drivers and molecular pathways will be identified with a structured, interpretable deep learning approach and gene-gene interaction effects by leveraging patient subtypes identified with semi-supervised graph-based cluster methods; both of these approaches are only possible with well- powered individual-level (genotypic and phenotypic) data of the scale that exists in MVP and we expect them to enhance efforts for gene target prioritization and drug discovery. Second, imputed gene dysregulation for each individual in MVP will be integrated with perturbagen reference libraries (describing the effect of therapeutic compounds on gene expression) to identify the extent to which compounds could be therapeutic by antagonizing the predicted gene dysregulation. We have validated this approach to summary level data (from GWAS) in a wide range of disorders (autoimmune, neuropsychiatric and COVID-19). Here we propose to use the same approach at the individual level to determine whether genetics can be utilized to rank potential treatments and predict the ones that achieve better outcomes. Third, the scale of data generation and its integration into predictive models will provide a wealth of data that will be made available to the MVP scientific community for other diseases beyond the immediate goals of this proposal that have the potential to increase our understanding of Precision Psychiatry. Successful completion of our study would have an enormous impact on our Veterans since, in addition to the tremendous burden of suffering and economic costs, serious mental illness increases the mortality rate among Veterans.

项目摘要 精准精神病学是一种新兴的方法，它考虑患者的特征来定制预防 以及严重精神疾病的治疗百万退伍军人计划（MVP）是最大和最 世界上最全面的生物库，目前涉及超过65万个多祖先遗传数据， 退伍军人和高度密集的电子健康记录信息，充分捕捉临床特征， 每个参与者。鉴于我们的退伍军人中严重精神疾病的高患病率，MVP提供了一个 进行大规模基因发现的独特机会，这将进一步加深我们对 严重精神疾病的病理生理学，并促进精确精神病学。虽然全基因组的能量很大 关联研究（GWAS）已经确定了严重精神疾病的多种风险变体， 由于效应量小，重叠，大多数发现的基因座的功能相关性的结论性结果有限 基因组的非编码区和它们的作用机制尚不清楚。我们集团和其他 已经表明，疾病风险中的大部分表型变异可以通过调节变异来解释， 具有细胞类型特异性，即影响表观遗传机制和表达水平的遗传变异， 基因.直接研究MVP样品中的基因表达和表观基因组变化是不可行的， 不可用。为了克服这些限制，我们建议利用大规模数据集， 基因分型和多尺度分子分析，我们的团队和其他人已经在人脑组织中产生了 并应用机器学习方法直接将全基因组转录组、表观基因组和 使用现有的MVP基因型在MVP样品中的蛋白质组。我们项目的主要目标有三个方面： 首先，将对估算的MVP转录组、表观基因组和蛋白质组进行荟萃分析，以确定单个组织特异性 通过一种名为PolyXcan的新方法为每个个体进行基因失调评分，该方法利用数据- 驱动的相关性感知元分析框架，并进行联合多组学范围内的关联研究。为 每一种严重的精神疾病，关键的基因驱动和分子途径将被确定与一个结构化的， 可解释的深度学习方法和基因-基因相互作用效应，通过利用已识别的患者亚型 与半监督的基于图的聚类方法;这两种方法都是可能的， MVP中存在的量表的个人水平（基因型和表型）数据，我们希望他们 加强基因靶点优先排序和药物发现的努力。第二，每一种基因的插补基因失调 MVP中的个体将与干扰原参考文库整合（描述治疗性干扰原的作用）。 化合物对基因表达的影响）来鉴定化合物通过拮抗 预测的基因失调我们已经验证了这种方法的摘要级数据（来自GWAS）， 广泛的疾病（自身免疫，神经精神和COVID-19）。在这里，我们建议使用相同的 在个体水平上的方法，以确定是否可以利用遗传学来排名潜在的治疗， 预测那些取得更好结果的人。第三，数据生成的规模及其融入 预测模型将为MVP科学界提供丰富的数据， 本提案的直接目标之外的其他疾病，这些疾病有可能增加我们的了解， 精确精神病学成功完成我们的研究将对我们的退伍军人产生巨大的影响 因为除了巨大的痛苦负担和经济成本之外，严重的精神疾病增加了 退伍军人的死亡率。