Trans-omics elucidation of genetic architecture underlying cardiovascular and HLBS diseases

摘要 通过基因分型或测序，大规模全基因组关联研究(GWAS)已经确定 数千个似乎影响复杂性状和疾病的基因座。这是一个根本性的限制 然而，方法是它揭示了变异体的基因型和 表型，但不识别功能变异体。除了几个例外， 非编码区仍然未知，更不用说这些变体影响的机制了 表型。目前几乎没有可用的策略来系统地描述 将遗传变异与表型联系起来。这项提议建立在两国之间现有的合作基础上 斯坦福大学和弗雷德大学统计学、基因组学和心血管流行病学的研究人员 哈钦森癌症研究中心。利用Trans产生的独特的多组学资源 精准医学组学(TOPMed)计划，此应用程序的目标是实施和应用 阐明慢性阻塞性肺疾病遗传基础和分子机制的分析策略 与心脏、肺、血液和睡眠有关的情况。以心血管疾病(CVD)为切入点， 它已成为全球发病率和死亡率的主要原因，三个具体目标是：(1) 确定与少数民族相关的基于遗传、表观遗传、RNA、蛋白质和代谢物的疾病风险因素 建立少数民族个体多基因疾病风险评分；(2)确定上位性 疾病风险的相互作用；以及(3)构建预测疾病风险的多组学分子标记 以及定义疾病亚型。我们的理论基础是，每种类型的组学数据都提供了一个量化的 连接基因组和疾病表型的中间表型；因此联合建模多个 组学数据可能使我们能够重建与疾病发病机制相关的关键生物学过程。我们的 拟议的框架是普遍适用的，并提供了一个有效和原则性的战略来探讨 复杂疾病的遗传基础。这项研究的成功完成将对人类生物学做出贡献， 少数民族健康与临床实践。

Abstract Large-scale genome-wide association studies (GWAS), through genotyping or sequencing, have identified thousands of loci that appear to influence complex traits and diseases. A fundamental limitation of this approach, however, is that it reveals statistical correlation between the genotype at a variant and the phenotype, but does not identify functional variants. With a few exceptions, the precise functional variants in non-coding regions remain unknown, much less the mechanism through which these variants affect phenotype. Few strategies are currently available for systematically delineating the molecular events that connect genetic variants to phenotype. This proposal builds upon an existing collaboration between researchers in statistics, genomics and cardiovascular epidemiology at Stanford University and Fred Hutchinson Cancer Research Center. Leveraging the unique multi-omics resources generated by Trans Omics for Precision Medicine (TOPMed) program, the objective of this application is to implement and apply analytic strategies for elucidating the genetic basis and molecular mechanisms underlying chronic conditions related to heart, lung, blood and sleep. Using cardiovascular diseases (CVD) as an entry point, which has become a leading cause of morbidity and mortality worldwide, the three Specific Aims are (1) to identify genetic-, epigenetic-, RNA-, protein- and metabolite-based disease risk factors relevant to minority populations, and to construct polygenic disease risk scores for minority individuals; (2) to identify epistatic interaction of disease risk; and (3) to construct multi-omics molecular signatures that predict disease risk as well as define disease subtypes. Our rationale is that each type of omics data offers a quantitative intermediate phenotype linking the genome and the disease phenotype; hence jointly modeling multiple omics data may enable us to reconstruct key biological processes related to disease pathogenesis. Our proposed framework is generally applicable, and offers an efficient and principled strategy to probe into the genetic basis of complex diseases. Successful completion of this research will contribute to human biology, minority health and clinical practice.