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Structuring Genetic Disease Complexity & Environmental Drivers
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
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
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).
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
Human Diseases Ontology project, www.disease-ontology.org