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Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination
Citation:
REIF, D., A. A. Motsinger-Reif, B. A. McKinney, M. T. Rock, J. E. Crowe Jr, AND J. H. Moore. Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination. Genes and Immunity. Nature Publishing Group, London, Uk, 10(2):112-9, (2009).
Impact/Purpose:
Combining information from previous studies on AEs related to smallpox vaccination with the genetic and proteomic attributes identified by RF, we built a comprehensive model of AE development that includes the cytokines intercellular adhesion molecule-1 (ICAM-1 or CD54), interleukin-10 (IL-10), and colony stimulating factor-3 (CSF-3 or G-CSF) and a genetic polymorphism in the cyokine gene interleukin-4 (IL4). The biological factors included in the model support our hypothesized mechanism for the development of AEs involving prolonged stimulation of inflammatory pathways and an imbalance of normal tissue damage repair pathways. This study shows the utility of RF for such analytical tasks, while both enhancing and reinforcing our working model of AE development after smallpox vaccination
Description:
Complex clinical outcomes, such as adverse reaction to vaccination, arise from the concerted interactions among the myriad components of a biological system. Therefore, comprehensive etiological models can be developed only through the integrated study of multiple types of experimental data. In this study, we apply this paradigm to high-dimensional genetic and proteomic data collected to elucidate the mechanisms underlying the development of adverse events (AEs) in patients after smallpox vaccination. As vaccination was successful in all of the patients under study, the AE outcomes reported likely represent the result of interactions among immune system components that result in excessive or prolonged immune stimulation. In this study, we examined 1442 genetic variables (single nucleotide polymorphisms) and 108 proteomic variables (serum cytokine concentrations) to model AE risk. To accomplish this daunting analytical task, we employed the Random Forests (RF) method to filter the most important attributes, then we used the selected attributes to build a final decision tree model. This strategy is well suited to integrated analysis, as relevant attributes may be selected from categorical or continuous data. Importantly, RF is a natural approach for studying the type of gene-gene, gene-protein and protein-protein interactions we hypothesize to be involved in the development of clinical AEs. RF importance scores for particular attributes take interactions into account, and there may be interactions across data types.
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Integrated Analysis of Genetic and Proteomic Data Identifies Biomarkers Associated with Adverse Events Following Smallpox Vaccination