title: The ontology of genetic susceptibility factors (OGSF) and its application in modeling genetic susceptibility to vaccine adverse events.
abstract: BACKGROUND
  Due to human variations in genetic susceptibility, vaccination often triggers adverse events in a small population of vaccinees. Based on our previous work on ontological modeling of genetic susceptibility to disease, we developed an Ontology of Genetic Susceptibility Factors (OGSF), a biomedical ontology in the domain of genetic susceptibility and genetic susceptibility factors. The OGSF framework was then applied in the area of vaccine adverse events (VAEs).
  
  
  RESULTS
  OGSF aligns with the Basic Formal Ontology (BFO). OGSF defines 'genetic susceptibility' as a subclass of BFO:disposition and has a material basis 'genetic susceptibility factor'. The 'genetic susceptibility to pathological bodily process' is a subclasses of 'genetic susceptibility'. A VAE is a type of pathological bodily process. OGSF represents different types of genetic susceptibility factors including various susceptibility alleles (e.g., SNP and gene). A general OGSF design pattern was developed to represent genetic susceptibility to VAE and associated genetic susceptibility factors using experimental results in genetic association studies. To test and validate the design pattern, two case studies were populated in OGSF. In the first case study, human gene allele DBR*15:01 is susceptible to influenza vaccine Pandemrix-induced Multiple Sclerosis. The second case study reports genetic susceptibility polymorphisms associated with systemic smallpox VAEs. After the data of the Case Study 2 were represented using OGSF-based axioms, SPARQL was successfully developed to retrieve the susceptibility factors stored in the populated OGSF. A network of data from the Case Study 2 was constructed by using ontology terms and individuals as nodes and ontology relations as edges. Different social network analys is (SNA) methods were then applied to verify core OGSF terms. Interestingly, a SNA hub analysis verified all susceptibility alleles of SNPs and a SNA closeness analysis verified the susceptibility genes in Case Study 2. These results validated the proper OGSF structure identified different ontology aspects with SNA methods.
  
  
  CONCLUSIONS
  OGSF provides a verified and robust framework for representing various genetic susceptibility types and genetic susceptibility factors annotated from experimental VAE genetic association studies. The RDF/OWL formulated ontology data can be queried using SPARQL and analyzed using centrality-based network analysis methods.
authors: Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA ; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA ; Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
authors: Yu
authors: Y
authors: Lin
authors: Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA ; Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, MI 48109, USA ; Center for Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
authors: Yongqun
authors: Y
authors: He