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

跨组学阐明心血管和 HLBS 疾病的遗传结构

基本信息

批准号：
9895848
负责人：
Charles L Kooperberg
金额：
$ 52.95万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9895848
关键词：
Affect Area Biological Biological Process Blood Cardiovascular Diagnostic Techniques Cardiovascular Diseases Chronic Clinical Clinical Research Collaborations Comb animal structure Complement Complex Coronary heart disease Correlation Studies Data Diagnostic Disease Epidemiology Epigenetic Process Ethnic Origin Event Fred Hutchinson Cancer Research Center Gene Expression Genes Genetic Genetic Diseases Genetic Research Genetic Transcription Genetic Variation Genome Genotype Heart Heart Diseases Hematological Disease Human Biology Individual Knowledge Light Link Lung Lung diseases Maps Measures Methylation Minority Modeling Molecular Molecular Profiling Morbidity - disease rate Multiomic Data Pathogenesis Phase Phenotype Population Proteins Proteome Public Health RNA Race Research Research Personnel Resources Risk Risk Factors Sleep Sleep Disorders Stroke Technology Testing Tissues Trans-Omics for Precision Medicine Universities Untranslated RNA Variant Venous base burden of illness cardiovascular disorder epidemiology cardiovascular disorder risk cardiovascular risk factor clinical phenotype clinical practice cohort disease phenotype disorder risk disorder subtype drug development experience gene interaction genetic architecture genetic epidemiology genetic variant genome wide association study genome-wide genomic epidemiology genomic locus health practice insight metabolome methylome minority health molecular phenotype mortality multiple omics novel phenotypic biomarker pleiotropism polygenic risk score precision medicine predictive signature programs protein expression protein metabolite statistics tool development trait transcriptome transcriptomics whole genome

项目摘要

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.

抽象的大规模基因组关联研究（GWAS）通过基因分型或测序已经确定似乎影响复杂特征和疾病的数千个基因座。这是一个基本限制然而，方法是，它揭示了在变体处的基因型与表型，但不能识别功能变体。除少数例外，精确的功能变体非编码区域仍然未知，更不用说这些变体影响的机制表型。目前很少有策略可以系统地描述分子事件将遗传变体连接到表型。该提案建立在现有的合作基础上斯坦福大学和弗雷德的统计，基因组学和心血管流行病学研究人员哈钦森癌症研究中心。利用Trans生成的独特多摩斯资源 Precision Medicine（TopMed）计划的OMICS，本应用程序的目的是实施和应用阐明遗传基础和分子机制的分析策略与心脏，肺，血液和睡眠有关的条件。使用心血管疾病（CVD）作为入口点，这已成为全球发病率和死亡率的主要原因，这三个具体目标是（1）鉴定遗传，表观遗传，RNA，蛋白质和代谢物的疾病风险因素人口，并为少数民族构建多基因疾病风险评分；（2）识别上任疾病风险的相互作用；（3）构建多摩斯分子特征，以预测疾病风险为以及定义疾病亚型。我们的理由是，每种类型的OMIC数据都提供了定量连接基因组和疾病表型的中间表型；因此共同建模多个 OMICS数据可能使我们能够重建与疾病发病机理有关的关键生物学过程。我们的提议的框架通常适用，并提供了一种有效而有原则的策略来调查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。