CRCNS: Ontology-Based Multi-Scale Integration of the Autism Phenome

随着人类基因组测序的完成，寻找易患和调节脑相关疾病的遗传变异已日益成为集体神经科学研究的重要领域。随着基因分型技术和分析方法的进步，研究人员正在寻找神经病理学的生物决定因素。然而，这些努力并不像那些非精神疾病那样迅速或成功，并且对于常见精神疾病（如精神分裂症，重度抑郁症，双相情感障碍和自闭症）中基因和复杂特征之间的因果关系仍然未知。缺乏进展的一个经常被引用的原因是中间表型或内表型的使用不足，这可以说比使用疾病类别本身提供了更高的遗传信噪比。当研究人员将内在表型纳入遗传分析时，这些类别并不是基于一套共享的，定义明确的和标准化的定义，这使得跨研究的比较和先前发现的复制成为问题。此外，目前还不清楚如何将内表型测量的类别连贯地整合到神经病理学的多尺度模型中。表型组学-在全基因组范围内对表型进行系统编目-已成为精神病遗传学中应对这一挑战的科学奋进。一个关键的限制，其进步，从而支持脑相关疾病的基因组学研究的能力，是缺乏可用的方法和工具来建模，管理和推理有关的内表型。我们建议通过开发Phenologue来克服这一主要障碍，Phenologue是一种新型的基于知识的技术，可以支持协作努力，以获得，管理和推理给定实验数据和已发表的发现的疾病表型。该项目的研究目标是：（1）开发一个内表型本体，将大脑连接、神经缺陷和遗传标记映射到一个学科领域理论中;（2）开发基于逻辑的方法，根据多尺度测量对内表型进行编码和分类;（3）创建工具，以获取新的内表型，并在在线资源（如已发表文献）中注释表型-基因型发现;以及（4）开发能够使用演绎推理来评估关于内表型的主题域理论的假设的查询启发方法。这些努力将通过精神病学遗传学、语义网技术和一阶推理的研究人员的密切合作来进行。研究小组将使用Phenologue来整合来自自闭症谱系障碍多方面研究的数据和知识。因此，在这个合作的神经科学项目中提出的活动的一个更广泛的目标是帮助研究人员发展这种异质性条件的遗传基础的一致和正式的理解。拟议的项目将建立在目前NIH资助的努力，为国家自闭症研究数据库（http：//www.example.com）创建一个自闭症本体，该方法可以提供给该资源的用户。ndar.nih.gov首席研究员将把关于在科学资源开发中使用本体论和逻辑的拟议研究工作纳入他在斯坦福大学提供的研究生生物医学信息学课程的教材。此外，调查小组将使通过拟议项目开发的软件工具直接提供给其他精神病遗传学研究团体，并将拟议的方法传播给类似的脑相关疾病的信息学合作。

DESCRIPTION (provided by applicant): Intellectual Merits of Proposed Activities With completion of the sequencing of the human genome, finding genetic variants that predispose and regulate brain-related disorders has increasingly become a significant area of collective neuroscience research. With advancements in genotyping technologies and analytic methodologies, investigators are making progress towards finding biological determinants of neuropathology. These efforts, however, have not been as rapid or as successful as those for non-mental disorders, and much still remains unknown about causal pathways between genes and complex traits in common mental disorders, such as schizophrenia, major depression, bipolar disorder and autism. An oft-cited reason for this lack of progress is the under use of intermediate phenotypes, or endophenotypes, which arguably provide higher genetic signal-to-noise ratios than the use of disease categories themselves. When researchers have incorporated endophenotypes into genetic analyses, the categories have not been based on a shared, well-defined and standardized set of definitions, making comparisons across studies and replication of prior findings problematic. Furthermore, it is unclear how categories of endophenotype measurements can be coherently integrated into multi-scale models of neuropathology. Phenomics - the systematic cataloging of phenotypes on a genome-wide scale - has emerged as a scientific endeavor within psychiatric genetics to address this challenge. A critical limitation to its advancement, and thus to its ability to support genomics studies of brain-related disease, is the lack of available methods and tools for modeling, managing, and reasoning about endophenotypes. We propose to overcome this major impediment through the development of the Phenologue, a novel knowledge-based technology that can support collaborative efforts to acquire, manage, and reason about a disease phenome given experimental data and published findings. The project's research objectives are to (1) develop an ontology of endophenotypes that maps brain connectivity, neural deficits, and genetic markers into a subject domain theory; (2) develop logic-based methods to encode and classify endophenotypes based on multi-scale measurements; (3) create tools to acquire new endophenotypes and annotate phenotype-genotype findings in online resources such as published literature; and (4) develop query-elicitation methods that can evaluate hypotheses about the subject domain theory of endophenotypes using deductive inference. These efforts will be undertaken through a close collaboration of researchers in psychiatric genetics, Semantic Web technologies, and first-order reasoning. Broader Impacts of Proposed Activities The research team will use the Phenologue to integrate data and knowledge from multiple lines of research on autism spectrum disorder. Thus, a broader objective of the activities proposed in this collaborative neuroscience project is to help investigators develop a coherent and formal understanding of the genetic underpinnings of this heterogeneous condition. The proposed project will build upon current NIH-funded efforts to create an autism ontology for the National Database for Autism Research (http://ndar.nih.gov), and the methods can be made accessible to users of this resource. The Principal Investigator will incorporate work from the proposed research on the use of ontologies and logic in scientific resource development into the teaching material of a graduate-level biomedical informatics course he offers at Stanford University. In addition, the investigative team will make software tools developed through the proposed project directly available to the other psychiatric genetics research communities, and will disseminate the proposed methods to similar informatics collaborations on brain-related disorders.