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Structuring Genetic Disease Complexity & Environmental Drivers

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Human Diseases Ontology project, www.disease-ontology.org

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Structuring Genetic Disease Complexity & Environmental Drivers

  1. 1. 1 Structuring Genetic Disease Complexity & Environmental Drivers 1 INTRODUCTION Representing the complexity of genetic etiology and environ- mental drivers of disease within the ontological structure of the Human Disease Ontology (DO, Kibbe et al. 2015) pre- sents a framework for developing a Differential Diagnosis ontology. Beyond monogenic diseases, clinical diagnosis is challenged by the complexity of etiologies for many genetic diseases. To address the challenges of representing this clinical com- plexity, the DO project has developed a complex disease model to drive the restructuring of DO knowledge. The DO’s clinical team is assessing a set of complex and environmental diseases to build the knowledgebase to be represented in the DO, through an expanded data representation captured through logical definitions to the Sequence Ontology. This work is enabled through the DO’s integration of ROBOT tools for capturing and integrating the disease to functional and/or structural sequence variant associations. Expanding the DO’s ontological structure and content will inform the development of a Differential Diagnosis DO. 2 EXPANSION FOR CLINICAL USE CASES The DO provides standardized descriptions of human disease through a controlled vocabulary of terms that improves the capture and communication of health-related data across multiple resources. As knowledge grows on how interactions between genetic and environmental factors lead to human disease, there is a need to incorporate genetic and environ- mental information into the DO. The DO clinician team has developed a conceptual complex genetic disease model (Figure 1) to identify the key types of genetic diseases to be represented in the DO. This model forms the basis for re-structuring of the DO’s genetic disease branch to represent the clinical complexity of genetic dis- eases. The pleiotropy of genetic diseases and the multi-organ impact of environmental condition further challenges the on- tological representation of complex clinical knowledge. Figure 1. The DO’s Complex Genetic Disease Model To this end, the DO team is assessing specific complex dis- eases to inform and test our model: 1) Prader-Willi syndrome, which can be a chromosomal deletion, a methylation defect or a single gene disorder, 2) alpha 1-antitrypsin deficiency, which has variable expression and critical contributions from environmental factors, 3) chromosome 22q11.2 deletion syn- drome, which has one etiology for multiple clinical diseases, but those diseases can also have other etiologies, and 4) cystic fibrosis, which involves multiple organ systems in a single disorder. DEMOCRATIZING DATA CURATION – ROBOT Utilization of the ROBOT tool for expert disease curation has greatly enhanced the DO’s capacity for data integration of manually vetted term definitions, xref updates, novel dis- ease subtypes contributed from the DO’s clinical team and BIG DATA collaborators at Wikidata and the Mouse Ge- nome Informatics database (Bello et al. 2018). REFERENCES Kibbe,WA., Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM. (2015) Disease Ontology 2015 update: an expanded and update database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Research, 43, D1071-D1078. Bello, S.M., Shimoyama M., Mitraka E., Laulederkind, S.J.F., Smith C.L., Eppig J.T., and Schriml, L.M. 2018) Disease Ontol- ogy: improving and unifying disease annotations across species. Disease Models & Mechanisms, dmm.032839 disease genetic disease monogenic disease chromosomal disease epigenetic methylation post- translational syndrome

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