Large-scale transcriptome and epigenome association analysis across multiple traits

项目总结 精准医疗是指根据每个人的个性特点定制医疗 有耐心的。百万退伍军人计划(MVP)提供了一个在全基因组范围内进行大规模 联合研究(GWAS)和加深我们对多性状和多性状精准医学的理解 疾病。虽然功能强大的GWA已经确定了多种风险变量，但得出的结论有限 由于效应大小较小，导致复杂性状的遗传因素的发现。此外，大多数人 的常见风险变异位于基因组的非编码区内，因此， 大多数已发现的基因座仍不清楚。我们的团队和其他人已经表明，很大一部分表型 疾病风险的可变性可以用调控变异来解释，即影响表观遗传学的遗传变异 机制和基因的表达水平。直接研究基因表达和表观基因组变化 MVP样本是不可行的，因为这样的数据不可用。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 Panel和直接归因于MVP中多组织和全基因组的基因表达和表观基因组图谱 使用现有MVP基因型别的样本。作为参考面板，我们将使用大规模数据集 我们小组和其他人产生的基因分型和分子图谱，包括但不限于 CommonMind联盟、MinecenCode、AD-AMP、STARnet和GTEx。推测的MVP基因表达 表观基因组数据提供了一个强大的队列，可以将遗传发现转化为特定基因的失调 跨越多个特征的分子途径将促进药物发现。我们建议研究基因 神经精神疾病--包括精神分裂症、双相情感障碍、后遗症--的表达和表观基因组紊乱 创伤应激障碍，酗酒，反复发作的抑郁和自杀念头--以及心脏新陈代谢-- 包括2型糖尿病、高血压、高脂血症、冠心病、心肌梗塞病史 和血液工作-量化(葡萄糖，Hb1Ac和脂谱)-特征。这些与疾病相关的签名可以 在利用特定的分子网络进行浓缩方面将进一步探索。我们建议构建组织 特定加权的基因-基因相互作用和因果概率网络，并用 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 数据生成及其与预测模型的集成将为其他疾病提供丰富的数据 除了本提案的直接目标有可能增加我们对Precision的理解之外 医学。

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.