Resolving and understanding the genomic basis of heterogeneous complex traits and disease

解决和理解异质复杂性状和疾病的基因组基础

• 批准号: 10226291
• 负责人: Arjun Krishnan
• 金额: $ 36.01万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10226291
• 关键词: Address; Age; Animal Model; Cardiomyopathies; Catalogs; Cell physiology; Clinical; Collaborations; Collection; Complex; Coronary Arteriosclerosis; Data; Data Collection; Disease; Eating Disorders; Female; Gastrointestinal Diseases; Genes; Genomics; Genotype; Heterogeneity; Human; Individual; Intervention; Knowledge; Life; Link; Methods; Modeling; Outcome; Pathway interactions; Phenotype; Physiological; Physiology; Population; Prevalence; Sex Bias; System; Testing; Tissues; Variant; advanced disease; analog; autism spectrum disorder; clinical phenotype; computational suite; computer framework; disease diagnosis; disorder risk; disorder subtype; genetic variant; genomic data; genomic profiles; genomic signature; genomic variation; human disease; in vivo; insight; male; novel; precision medicine; pregnancy disorder; sex; trait

ABSTRACT Over the last decade, numerous large-scale biomedical studies have helped catalog hundreds of genomic variants and physiological-clinical phenotypes associated with a range of complex traits and diseases. These catalogs are now exposing wide chasms in our understanding of the mechanistic relationships between genomic variation, cellular processes, tissue function, and trait variation – knowledge that is crucial for advancing disease diagnosis and intervention. We develop and apply computational data-driven approaches to bridge these gaps and help resolve, understand, and tackle the heterogeneity of complex traits and diseases. We are specifically focusing on three key questions: 1) Each disease is not a single well-defined condition. Can we deconvolve complex disorders into subtypes defined by shared functional dysregulations, and characterize novel genes/mechanisms underlying each subtype? 2) Most diseases vary in prevalence and impact between males and females, and across life stages. Can we delineate the genomic basis of differences in tissue physiology and disease between sexes and across ages? 3) Choosing the right in vivo system to study human diseases is hard due to murky relationships between phenotypes/genes in humans and model species. Can we systematically identify functionally `analogous' genes, phenotypes, and conditions in model organisms for studying specific facets of complex traits/diseases? To address these critical questions across diseases, we will develop a suite of computational frameworks that integrate genomic data collections, fragmented prior knowledge, and individual-/population-level genotypes-phenotypes. We will use this approach to systematically unravel genomic signatures, pathways, and networks that help characterize mechanistic subtypes, age/sex biases, and cross-species analogs of a wide range of diseases. We have established collaborations for experimentally following-up our predictions for specific test cases including autism, gastrointestinal disorder, coronary artery disease, cardiomyopathies, abnormal pregnancy, and eating disorders. Together, this concerted effort will help us gain insights into the multi-scale mechanisms underlying heterogeneous traits and diseases. In the long-term, our frameworks and mechanistic insights will enable us to link an individual's genomic profiles to a precise assessment of her/his physiological traits, disease risks, and clinical outcomes.

抽象的 在过去的十年中，许多大规模的生物医学研究有助于对数百种基因组进行分类 与一系列复杂性状和疾病相关的变体和物理临床表型。这些 目录现在在我们对我们对机械关系之间的机械关系中的广泛关注 基因组变异，细胞过程，组织功能和性状变异 - 知道这对于至关重要 推进疾病的诊断和干预。我们开发并应用计算数据驱动的方法 弥合这些差距，并有助于解决，理解和解决复杂性状和疾病的异质性。 我们特别关注三个关键问题：1）每种疾病不是一个明确定义的疾病。能 我们将复杂疾病解析为由共享功能失调定义的子类型，并表征 每个亚型的新基因/机制？ 2）大多数疾病的患病率和影响 男性和女性，跨越生活阶段。我们可以描述组织差异的基因组基础吗 性别与跨年龄之间的生理和疾病？ 3）选择正确的体内系统来研究人类 由于人类与模型物种的表型/基因之间的模糊关系，疾病很难。能 我们系统地识别功能“类似”基因，表型和模型生物的条件 研究复杂性状/疾病的特定方面？为了解决跨疾病的这些关键问题，我们 将开发一套计算框架，这些框架整合基因组数据收集，零散的先验 知识和个体/人群水平的基因型 - 表型。我们将使用这种方法来系统地 拆开基因组特征，途径和网络，有助于表征机械性亚型，年龄/性别 各种疾病的偏见和跨物种类似物。我们已经建立了合作 实验遵循我们对特定测试案例的预测，包括自闭症，胃肠道疾病， 冠状动脉疾病，心肌病，异常妊娠和饮食失调。一起，这个 一致的努力将有助于我们深入了解异质特征的多尺度机制和 疾病。从长远来看，我们的框架和机械见解将使我们能够联系个人的 基因组特征对其身体特征，疾病风险和临床结果进行精确评估。