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

摘要 大规模的全基因组关联研究（GWAS），通过基因分型或测序，已经确定了 数以千计的基因座似乎会影响复杂的性状和疾病。这一点的一个基本限制是， 然而，这种方法揭示了变异基因型和变异基因型之间的统计相关性。 表型，但不能识别功能变体。除了少数例外， 非编码区仍然是未知的，更不用说这些变异影响的机制了。 表型。目前很少有策略可用于系统地描绘分子事件， 将遗传变异与表型联系起来这一建议建立在现有的合作基础上， 斯坦福大学的统计学、基因组学和心血管流行病学研究人员和弗雷德 哈钦森癌症研究中心。利用Trans产生的独特多组学资源 精准医学组学（TOPMed）计划，本申请的目的是实施和应用 阐明慢性乙型肝炎的遗传基础和分子机制的分析策略 与心脏、肺、血液和睡眠有关的疾病。以心血管疾病（CVD）为切入点， 它已成为世界范围内发病率和死亡率的主要原因，三个具体目标是：（1） 确定与少数群体有关遗传、表观遗传、RNA、蛋白质和代谢物疾病危险因素 群体，并构建少数个体的多基因疾病风险评分;（2）识别上位性 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。我们的基本原理是，每种类型的组学数据都提供了一个定量的 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过程。我们 建议的框架是普遍适用的，并提供了一个有效的和原则性的战略，探讨 复杂疾病的遗传基础这项研究的成功完成将有助于人类生物学， 少数民族健康和临床实践。

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.