Center for Integrated Approaches to Undiagnosed Diseases

描述（由申请人提供）：未确诊疾病可能由遗传、环境和发育因素决定。虽然一些未确诊的疾病将代表具有单基因或少基因病因的新型罕见遗传综合征，而其他疾病将反映已知疾病的罕见表现，但许多疾病可能是多种遗传、环境和发育因素在分析上更具挑战性的组合。全外显子组和全基因组测序是确定未诊断疾病的遗传贡献的有力工具；然而，这些方法本身不太可能阐明许多（如果不是大多数的话）未确诊疾病的基础。因此，本提案的中心统一假设是，一个综合的、基于网络的系统生物学方法，不仅包括遗传变异数据，还包括基因表达、代谢组学、蛋白质组学和暴露组学数据，以及仔细的深度表型，将被证明是识别许多未确诊疾病的途径和机制的最有效方法。为了解决潜在的假设，我们建议为哈佛未确诊疾病项目临床站点（Harvard UDP-CS）建立一个综合的跨学科研究计划，包括三个具体目标：1)确定和临床特征-我们将执行

DESCRIPTION (provided by applicant): Undiagnosed diseases are likely to be determined by genetic, environmental, and developmental factors. While some undiagnosed diseases will represent novel rare genetic syndromes with monogenic or oligogenic etiologies and others will reflect rare manifestations of known diseases, many are likely to result from a more analytically challenging combination of multiple genetic, environmental, and developmental factors. Whole exome and whole genome sequencing are powerful tools with which to ascertain the genetic contributions to undiagnosed diseases; however, these methods alone are unlikely to elucidate the basis for many, if not most, undiagnosed diseases. The central unifying hypothesis of this proposal is, therefore, that an integrated, network-based, systems biology approach that incorporates not only genetic variation data, but also gene expression, metabolomic, proteomic, and exposomic data, along with careful deep phenotyping, will prove to be the most effective way to identify the pathways and mechanisms responsible for many undiagnosed diseases. To address the underlying hypothesis, we propose to establish an integrated interdisciplinary research plan for the Harvard Undiagnosed Disease Program Clinical Site (Harvard UDP-CS) that involves three specific aims: 1) ascertainment and clinical characterization - we will perform case ascertainment and phenotypic characterization for selected rare and undiagnosed disease states in adults and children; 2) genetic assessments - we will use patient and family member-derived DNA sequence, transcriptomic data (when available), and clinical phenotype information to identify potentially causal DNA sequence variants, gene expression variation, and potentially causative pathway derangements; and 3) network approach to disease diagnosis - we will integrate other -omic data, including metabolomic, proteomic, and exposomic data, along with the candidate genetic variants into the comprehensive interactome, and thereby identify diseases or disease pathways in network proximity to the involved genes that may help identify potential pathobiological modules relevant to the etiology of the undiagnosed disease. We will work toward meeting these specific aims and addressing the overall hypothesis in close collaboration with the coordinating center and other clinical sites in the UDN in order to develop and implement assessment protocols of phenotype, environment, and genotype that will ultimately define the etiology and treatment of undiagnosed diseases.