Genetic Basis for Adverse Events after Smallpox Vaccination

Computational Genetics Laboratory, Dartmouth Medical School, Lebanon, New Hampshire, USA.
The Journal of Infectious Diseases (Impact Factor: 6). 07/2008; 198(1):16-22. DOI: 10.1086/588670
Source: PubMed


Identifying genetic factors associated with the development of adverse events might allow screening before vaccinia virus administration. Two independent clinical trials of the smallpox vaccine (Aventis Pasteur) were conducted in healthy, vaccinia virus-naive adult volunteers. Volunteers were assessed repeatedly for local and systemic adverse events (AEs) associated with the receipt of vaccine and underwent genotyping for 1,442 singlenucleotide polymorphisms (SNPs). In the first study, 36 SNPs in 26 genes were associated with systemic AEs (P <or= .05); these 26 genes were tested in the second study. In the final analysis, 3 SNPs were consistently associated with AEs in both studies. The presence of a nonsynonymous SNP in the methylenetetrahydrofolate reductase (MTHFR)gene was associated with the risk ofAEin both trials (odds ratio [OR], 2.3 [95% confidence interval {CI}, 1.1-5.2] [P = .04] and OR, 4.1 [95% CI, 1.4 -11.4] [P<.01]). Two SNPs in the interferon regulatory factor-1 (IRF1) gene were associated with the risk of AE in both sample sets (OR, 3.2 [95% CI, 1.1-9.8] [P = .03] andOR, 3.0 [95% CI, 1.1- 8.3] [P = .03]). Genetic polymorphisms in genes expressing an enzyme previously associated with adverse reactions to a variety of pharmacologic agents (MTHFR) and an immunological transcription factor (IRF1) were associated with AEs after smallpox vaccination in 2 independent study samples.

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    • "The human genetic susceptibility factor will become a key participant in the vaccine adverse event process. Two use cases were studied: one relating to the human gene allele DBR1*15:01 as a genetic susceptibility factor that has been found to be a cause of multiple sclerosis in association with the influenza vaccine Pandemrix [42]; the other analyzing genetic polymorphisms associated with smallpox vaccine adverse events [43]. The OGSF modeling of these VAE specific cases requires the importing of many adverse event terms from OAE [37]. "
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    ABSTRACT: Background A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data. For example, using the data extracted from the Vaccine Adverse Event Reporting System (VAERS), OAE was used to analyse vaccine adverse events associated with the administrations of different types of influenza vaccines. OAE has also been used to represent and classify the vaccine adverse events cited in package inserts of FDA-licensed human vaccines in the USA. Conclusion OAE is a biomedical ontology that logically defines and classifies various adverse events occurring after medical interventions. OAE has successfully been applied in several adverse event studies. The OAE ontological framework provides a platform for systematic representation and analysis of adverse events and of the factors (e.g., vaccinee age) important for determining their clinical outcomes.
    Journal of Biomedical Semantics 07/2014; 5(1). DOI:10.1186/2041-1480-5-29 · 2.26 Impact Factor
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    • "The instance of ‘DBR1*15:01’ is a part of the specific patient in the case study. Based on this and many other case reports [34-36], we have generated the OGSF representation at the class level: "
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    ABSTRACT: BackgroundDue 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).ResultsOGSF 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.ConclusionsOGSF 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.
    Journal of Biomedical Semantics 04/2014; 5(1):19. DOI:10.1186/2041-1480-5-19 · 2.26 Impact Factor
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    • "A subsequent blood profiling study of 15 normal individuals with samples collected at multiple time points also showed high variability of IFN-inducible genes and those expressing higher baseline levels had lower response to IFN in vitro (Radich et al. 2004). Recent gene expression and pharmacogenomic studies demonstrated that genes associated with IFN pathways could determine susceptibility to disease or pathogen and response to certain treatments (Assassi et al. 2010; Everitt et al. 2012; Hambleton et al. 2011; Reif et al. 2008; van Baarsen et al. 2008; Zaas et al. 2009). "
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    ABSTRACT: The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.
    Current topics in microbiology and immunology 08/2012; 363. DOI:10.1007/82_2012_252 · 4.10 Impact Factor
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