Identification of SNPs associated with variola virus virulence
1 Department of Community and Family Medicine, The Geisel School of Medicine at Dartmouth, Dartmouth College, One Medical Center Drive, Lebanon, NH 03756, USA
2 Computations/Global Security, Lawrence Livermore National Laboratory, P.O. Box 808, L-174, Livermore, CA, 94551, USA
3 Department of Genetics, Institute for Quantitative Biomedical Sciences, The Geisel School of Medicine at Dartmouth, Dartmouth College, One Medical Center Drive, Lebanon, NH 03756, USA
BioData Mining 2013, 6:3 doi:10.1186/1756-0381-6-3Published: 14 February 2013
Decades after the eradication of smallpox, its etiological agent, variola virus (VARV), remains a threat as a potential bioweapon. Outbreaks of smallpox around the time of the global eradication effort exhibited variable case fatality rates (CFRs), likely attributable in part to complex viral genetic determinants of smallpox virulence. We aimed to identify genome-wide single nucleotide polymorphisms associated with CFR. We evaluated unadjusted and outbreak geographic location-adjusted models of single SNPs and two- and three-way interactions between SNPs.
Using the data mining approach multifactor dimensionality reduction (MDR), we identified five VARV SNPs in models significantly associated with CFR. The top performing unadjusted model and adjusted models both revealed the same two-way gene-gene interaction. We discuss the biological plausibility of the influence of the SNPs identified these and other significant models on the strain-specific virulence of VARV.
We have identified genetic loci in the VARV genome that are statistically associated with VARV virulence as measured by CFR. While our ability to infer a causal relationship between the specific SNPs identified in our analysis and VARV virulence is limited, our results suggest that smallpox severity is in part associated with VARV strain variation and that VARV virulence may be determined by multiple genetic loci. This study represents the first application of MDR to the identification of pathogen gene-gene interactions for predicting infectious disease outbreak severity.