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

摘要 在过去的十年里，许多大规模的生物医学研究已经帮助编目了数百个基因组 与一系列复杂性状和疾病相关的变异和生理-临床表型。这些 目录现在暴露了我们对机械关系的理解中的巨大鸿沟， 基因组变异、细胞过程、组织功能和性状变异--这些知识对于 推进疾病诊断和干预。我们开发和应用计算数据驱动的方法， 弥合这些差距，帮助解决，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）每种疾病都不是一种定义明确的疾病。可以 我们将复杂的疾病分解为由共同的功能失调定义的亚型， 每个亚型的新基因/机制？2)大多数疾病的流行率和影响各不相同， 男性和女性，以及不同的生命阶段。我们能描绘出组织差异的基因组基础吗 生理学和疾病之间的性别和跨年龄？3)选择合适的体内系统进行人体研究 由于人类和模式物种的表型/基因之间的关系模糊，疾病很难治疗。可以 我们系统地鉴定了功能上类似的基因、表型和模式生物的条件， 研究复杂性状/疾病的特定方面？为了解决这些跨疾病的关键问题，我们 将开发一套计算框架，整合基因组数据收集，碎片化的先前 知识和个体/群体水平的基因型-表型。我们将使用这种方法系统地 解开有助于表征机械亚型、年龄/性别的基因组特征、途径和网络 偏见，以及各种疾病的跨物种类似物。我们建立了合作关系， 实验性地跟踪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。在一起，这 协同努力将有助于我们深入了解异质性特征背后的多尺度机制， 疾病从长远来看，我们的框架和机械的见解将使我们能够将个人的 基因组图谱，以精确评估她/他的生理特征，疾病风险和临床结果。

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.