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

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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.