Large-scale transcriptome and epigenome association analysis across multiple traits

项目摘要 精准医疗是指针对每个人的个体特征定制医疗 病人百万退伍军人计划（MVP）提供了一个独特的机会，进行大规模的全基因组 关联研究（GWAS），并进一步了解精准医学在多个性状和 疾病虽然有力的GWAS已经确定了多种风险变体，但结论性的 研究结果的遗传因素有助于复杂的性状，由于小的影响大小。此外，大多数 的常见风险变异是在基因组的非编码区，因此， 大多数已发现的基因座仍不清楚。我们的研究小组和其他人已经表明，大部分表型 疾病风险的变异性可以用调控变异来解释，即影响表观遗传的遗传变异。 基因的表达水平。研究基因表达和表观基因组的变化直接在 MVP样品不可行，因为此类数据不可用。为了克服这些限制，我们建议 应用机器学习方法，利用现有的分子数据（与MVP无关）作为参考 MVP中的多组织和全基因组基因表达和表观基因组谱的面板和直接插补 使用现有MVP基因型的样本。作为参考面板，我们将使用大规模数据集， 基因分型和分子分析，我们的小组和其他人已经产生，包括，但不限于， CommonMind联盟、psychENCODE、AD-AMP、STARNET和GTEx。插补MVP基因表达 和表观基因组数据提供了一个强大的队列，以“翻译”遗传发现，以失调的具体 跨越多种特征的分子途径将增强药物发现。我们建议研究基因 表达和表观基因组扰动神经精神-包括精神分裂症，双相情感障碍，后， 创伤应激障碍，酗酒，复发性抑郁症和自杀意念--以及心脏代谢-- 包括2型糖尿病、高血压、高脂血症、冠心病、心肌梗死病史 和血液定量（葡萄糖，Hb 1Ac和血脂）-性状。这些与疾病相关的特征可以 在富集特定分子网络方面进行进一步探索。我们建议构建组织 特定的加权基因-基因相互作用和因果概率网络，并评估富集与 与疾病相关的特征，以确定子网络，分子过程和关键驱动因素。总体而言，规模 数据生成及其集成到预测模型中将为其他疾病提供丰富的数据 超越了本提案的直接目标，这些目标有可能增加我们对Precision的理解 药

PROJECT SUMMARY Precision Medicine refers to the customization of medical treatment to the individual characteristics of each patient. The Million Veteran Program (MVP) provides a unique opportunity to perform large-scale genome-wide association studies (GWAS) and further our understanding of Precision Medicine across multiple traits and diseases. While well powered GWAS have identified multiple risk variants, there has been limited conclusive findings on the genetic factors contributing to complex traits due to small effect sizes. In addition, the majority of common risk variants are within non-coding regions of the genome and, as such, the functional relevance of most discovered loci remains unclear. Our group and others have shown that a large portion of phenotypic variability in disease risk can be explained by regulatory variants, 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 apply a machine learning approach that leverages existing molecular data (unrelated to MVP) as a reference panel and directly impute multi-tissue and genome-wide gene expression and epigenome profiles in MVP samples using the existing MVP genotypes. As reference panel, we will use large-scale datasets with genotyping and molecular profiling that our group and others have generated, including, but not limited to, the CommonMind consortium, psychENCODE, AD-AMP, STARNET and GTEx. Imputed MVP gene expression and epigenome data provides a powerful cohort to “translate” genetic findings to dysregulation of specific molecular pathways across multiple traits that will enhance drug discovery. We propose to study gene expression and epigenome perturbations in neuropsychiatric -- including schizophrenia, bipolar disorder, post- traumatic stress disorder, alcohol abuse, recurrent depression and suicidal ideations -- and cardiometabolic -- including type 2 diabetes, hypertension, hyperlipidemia, coronary heart disease, history of myocardial infarction and bloodwork-quantified (glucose, Hb1Ac and lipid profile) -- traits. These disease-associated signatures can be further explored in terms of enrichment with specific molecular networks. We propose to construct tissue specific weighted gene-gene interaction and causal probabilistic networks and assess the enrichment with disease-associated signatures to identify subnetworks, molecular processes and key drivers. Overall, the scale of data generation and its integration into predictive models will provide a wealth of data for other diseases beyond the immediate goals of this proposal that have the potential to increase our understanding of Precision Medicine.