Large-scale transcriptome and epigenome association analysis across multiple traits

项目总结 精确精神病学是一种新兴的方法，它考虑患者的特点来定制预防 以及严重精神疾病的治疗。百万退伍军人计划(MVP)是规模最大、规模最大的 世界上全面的生物库，目前涉及来自650,000多个祖先的基因数据 退伍军人和高密度电子健康记录信息，全面捕获 每一位参与者。鉴于我们退伍军人中严重精神疾病的高患病率，MVP提供了 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和促进精准精神病学。虽然整个基因组都有强大的动力 协会研究(GWAS)已经确定了严重精神疾病的多种风险变量，已经有 由于效应规模小、重叠，对大多数已发现的基因座的功能相关性的结论性发现有限 基因组的非编码区，以及它们的作用机制不清楚。我们的团队和其他人 已经表明，疾病风险中的很大一部分表型可变性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基因表达水平的遗传变异 基因。像这样的数据，直接研究MVP样本中的基因表达和表观基因组变化是不可行的 都不可用。为了克服这些限制，我们建议利用大规模数据集 我们和其他人在人脑组织中产生的基因分型和多尺度分子图谱 并应用机器学习方法直接归因于全基因组转录、表观基因组和 使用现有MVP基因型别的MVP样本中的蛋白质组。我们项目的主要目标有三个： 首先，mvp转录本、表观基因组和蛋白质组将被荟萃分析到单个组织特异性。 通过一种名为PolyXcan的新方法，基因失调对每个人进行评分，该方法利用数据- 驱动相关性感知的元分析框架，并执行联合多组学范围的关联研究。为 每一种严重的精神疾病、关键的基因驱动因素和分子途径都将被识别为结构化的、 可解释的深度学习方法和利用已识别的患者亚型的基因-基因交互效应 对于半监督的基于图的聚类方法，这两种方法只有在良好的- MVP中存在的个体级别的(基因和表型)数据，我们希望他们 加大基因靶标优先排序和药物发现力度。其次，每个人都被归因于基因失调 MVP中的个体将与微扰参考库(描述治疗效果)集成 化合物对基因表达的影响)，以确定化合物可以通过拮抗来治疗的程度 预测的基因失调。我们已经验证了这种方法对汇总级数据(来自GWAS)的有效性 广泛的疾病(自身免疫性、神经精神病学和新冠肺炎)。在这里，我们建议使用相同的 在个体层面上确定是否可以利用遗传学来对潜在的治疗方法和 预测那些能取得更好结果的项目。第三，数据生成的规模及其与 预测模型将提供丰富的数据，供MVP科学界使用 这项提议的直接目标之外的其他疾病，有可能增加我们的理解 精准精神病学。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外，严重的精神疾病还会增加 退伍军人的死亡率。

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.