A Complex Disease Genetics Knowledge Provider for Biomedical Data Translator

生物医学数据转换器的复杂疾病遗传学知识提供者

• 批准号: 10333494
• 负责人: Jason Flannick
• 金额: $ 49.33万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-23 至 2022-01-22
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10333494
• 关键词: Address; Aloral; Architecture; Bioinformatics; Biological; Catalogs; Cell physiology; Chromatin; ClinVar; Collection; Complex; Computational Biology; Computational algorithm; Data; Data Set; Data Sources; Databases; Disease; Disease susceptibility; Feasibility Studies; Foundations; Genes; Genetic; Genetic Diseases; Genomic Segment; Goals; Gold; Graph; Human; Human Genetics; Interview; Knowledge; Knowledge Portal; Link; Mediating; Methodology; Methods; Modeling; Molecular; Online Mendelian Inheritance In Man; Pathogenesis; Pathway interactions; Positioning Attribute; Protocols documentation; Provider; Rare Diseases; Resources; Risk; Running; Science; Services; Source; Specific qualifier value; Susceptibility Gene; System; Techniques; Testing; Tissues; Translating; Translations; United States National Institutes of Health; Untranslated RNA; Vision; base; causal variant; complex data; computational pipelines; computer science; data analysis pipeline; data harmonization; data integration; data translator; disease classification; disorder risk; epigenomics; functional genomics; genetic association; genetic variant; genomic data; human disease; improved; insight; open source; programs; tool; web portal

A major goal of the Biomedical Data Translator Program is to facilitate disease classification based on molecular and cellular abnormalities. While many experimental approaches exist to interrogate molecular or cellular processes, few can discern which among a host of potential abnormalities are relevant to disease in the human system. Genetic variants associated with disease are unique in providing molecular alterations causally related to human disease risk. There are two types of genetic associations. Rare disease associations can (usually) be clearly linked to a gene and are well represented by catalogs such as ClinVar, OMIM, and Monarch. Complex disease associations are harder to interpret because they (a) are statistical rather than qualitative and (b) usually lie in noncoding genomic regions that cannot be immediately translated to molecular or cellular abnormalities. Many complementary resources to help in the biological translation of complex disease associations have recently emerged, broadly classifiable as either “functional genomic” datasets (e.g. from epigenomic profiling or chromatin capture) or predictive bioinformatic methods (e.g. that integrate various genetic and functional genomic datasets to predict disease-susceptibility genes or pathways). These resources require expertise to curate and interpret, and there is as yet no knowledge source that integrates them to interpret complex disease associations. Furthermore, techniques for harmonizing heterogeneous functional genomic datasets with respect to one another are not yet established, most predictive bioinformatic methods specify complex data-processing pipelines that have not yet been scaled to run across many diseases, and there are few if any “gold standards” to evaluate the molecular or cellular abnormalities identified by these resources. The goal of our proposed project is to address these gaps within a complex disease genetics Knowledge Provider for Translator. We are experts in complex disease genetics and maintain the Knowledge Portal Network (KPN), a collection of open source web portals and Smart APIs that make integrated genetic and genomic datasets publicly accessible for >180 complex diseases. We have built the KPN by developing a protocol for working with disease experts to aggregate and curate high-confidence genetic datasets, building computational pipelines to harmonize these data and apply predictive bioinformatic methods upon them, and extracting relationships mined from these data into a Neo4J graph database. We propose to use the KPN as a foundation to implement a Translator Knowledge Provider of high-confidence complex disease associations and predicted disease-relevant molecular and cellular abnormalities. We will implement this Knowledge Provider by (a) expanding the data sources, data types, and bioinformatic methods integrated within the KPN; (b) developing new computational algorithms to improve the ability of genetic data to identify molecular and cellular abnormalities underlying complex disease; (c) maintaining REST services provisioning Translator with these resources; and (d) developing methodologies for evaluating the accuracy and internal consistency of these data, further curating them, and defining use cases of them within Translator. In so doing, we will enable Translator users to address questions such as: • What genes are causally linked to complex disease [X], and with what confidence? • What is the increase in risk for complex disease [X] when gene [Y] is perturbed? • What pathways are enriched for associations with complex disease [X]? • What tissues mediate the pathogenesis of complex disease [X]? • What other diseases are genetically correlated with complex disease [X]? We participated in the Translator feasibility study and contributed important insights to the project vision including (a) a unifying architectural model of Translator (based on interviews with each Translator team) closely followed by OTA-19-009; (b) the concept of Translator as a tool to augment (rather than replace) human reasoning; and (c) the idea of a “Turing test” to evaluate Translator capabilities. Our expertise in human genetics and hypothesis-driven science, but also computer science and computational biology, ideally positions us to collaborate with NIH staff and other awardees to help guide Translator data integration in a scientifically rigorous manner.

生物医学数据翻译程序的一个主要目标是促进疾病分类 基于分子和细胞异常虽然存在许多实验方法， 询问分子或细胞过程，很少有人能分辨出在许多潜在过程中 异常与人类系统中的疾病有关。遗传变异与 疾病是独特的，提供与人类疾病风险因果相关的分子改变。 有两种类型的遗传关联。罕见疾病协会可以（通常） 明确地与一个基因相关联，并由ClinVar、OMIM和 君主复杂的疾病关联更难解释，因为它们（a）是统计学上的 而不是定性的和（B）通常位于非编码基因组区域，不能被 立即转化为分子或细胞异常。许多补充资源， 最近出现了对复杂疾病关联的生物学翻译的帮助， 可广泛分类为“功能基因组”数据集（例如来自表观基因组谱分析或 染色质捕获）或预测性生物信息学方法（例如，整合各种遗传和 预测疾病易感基因或途径的功能基因组数据集）。这些 资源需要专门知识来管理和解释，目前还没有知识来源 整合它们来解释复杂的疾病关联。此外，用于 协调异质功能基因组数据集相对于彼此还没有 已建立的大多数预测性生物信息学方法指定了复杂的数据处理管道 还没有被扩展到许多疾病上，而且几乎没有任何“黄金”， 标准”来评估这些资源鉴定的分子或细胞异常。 我们提出的项目的目标是在一个复杂的解决这些差距 疾病遗传学翻译知识提供者。我们是复杂疾病的专家 遗传学和维护知识门户网络（KPN），一个开源网站的集合 门户网站和智能API，使集成的遗传和基因组数据集公开访问 180多种复杂疾病。我们已经建立了KPN通过开发一个协议， 疾病专家聚集和策划高置信度的遗传数据集， 协调这些数据并应用预测性生物信息学方法的计算管道 然后，将从这些数据中挖掘出的关系提取到Neo4J图形数据库中。 我们建议使用KPN作为基础来实现翻译知识提供者， 高置信度复杂疾病关联和预测的疾病相关分子和 细胞异常我们将通过以下方式实现此知识提供程序：（a）扩展数据 来源、数据类型和生物信息学方法集成在KPN中;（B）开发新的 提高遗传数据识别分子和细胞的能力的计算算法 复杂疾病的潜在异常;（c）维持REST服务供应 （d）制定评估翻译准确性的方法 这些数据的内部一致性，进一步管理它们，并定义它们的用例 在翻译者。通过这样做，我们将使Translator用户能够解决以下问题： ·哪些基因与复杂疾病有因果联系，有多大的把握？ ·当基因[Y]受到干扰时，复杂疾病[X]的风险会增加多少？ ·哪些途径与复杂疾病相关[X]？ ·什么组织介导复杂疾病的发病机制[X]？ ·哪些其他疾病与复杂疾病有遗传相关性[X]？ 我们参与了Translator项目的可行性研究，并为项目的实施提供了重要的见解。 项目愿景包括：（a）统一的Translator架构模型（基于与 （B）翻译员作为工具的概念， 增强（而不是取代）人类推理;（c）“图灵测试”的想法，以评估 翻译能力。我们在人类遗传学和假设驱动科学方面的专业知识， 计算机科学和计算生物学，使我们能够与NIH的工作人员合作， 和其他获奖者，以科学严谨的方式帮助指导Translator数据集成。