Infectious bioterrorism agents such as smallpox and anthrax represent a critical public health concern. Important goals of biodefense research include the development of predictors of pathogenicity of bioterrorism agents for rapid response and the prediction of clinical outcomes such as adverse events following vaccination. Our success in these biodefense endeavors will depend critically on the bioinformatics methods and software that are available for making sense of high-dimensional data generated by technologies such as DNA microarrays and mass spectrometry. The goal of this research program is to continue the development, evaluation, distribution and support of our successful open-source Multifactor Dimensionality Reduction (MDR) software package for identifying combinations of genetic and environmental predictors of clinically important biodefense outcomes. We will first evaluate new methods from our research group and those that have been proposed by other research groups and assess the best approaches for inclusion in new versions of the MDR software (AIM 1). The inclusion of new methods such as stochastic search algorithms for genome-wide analysis and linear models for continuous endpoints will ensure that the MDR software stays on the cutting edge. Second, we propose to develop a web server that biodefense researchers can use as a source of expert knowledge in the form of gene weights that are generated from biochemical pathways, Gene Ontology (GO), chromosomal location and protein-protein interactions, for example (AIM 2). Expert knowledge files generated by the web server will be used by the MDR software to prioritize single nucleotide polymorphisms (SNPs) for interaction analysis in genome-wide association studies or GWAS. These additions will ensure that MDR is ready for application to GWAS that are now commonplace. We will then apply these methods to GWAS data from an ongoing study of adverse events following vaccination for smallpox (AIM 3). Finally, we will identify opportunities to address other important bioterrorism research questions with our software that are consistent with the research objectives of the NIAID/NIH (AIM 4). All bioinformatics methods and tools will be provided in a timely manner for free as open-source software.

Public Health Relevance

Infectious bioterrorism agents such as smallpox and anthrax represent a critical public health concern. Important goals of biodefense research include the development of predictors of pathogenicity of bioterrorism agents for rapid response and the prediction of clinical outcomes such as adverse events following vaccination. We will develop computer algorithms and software that can be used to identify biomarkers of important biodefense outcomes.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project (R01)
Project #
5R01AI059694-09
Application #
8310232
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Gezmu, Misrak
Project Start
2004-08-01
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2014-07-31
Support Year
9
Fiscal Year
2012
Total Cost
$489,904
Indirect Cost
$176,312
Name
Dartmouth College
Department
Genetics
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755
Darabos, Christian; White, Marquitta J; Graham, Britney E et al. (2014) The multiscale backbone of the human phenotype network based on biological pathways. BioData Min 7:1
Pechenick, Dov A; Payne, Joshua L; Moore, Jason H (2014) Phenotypic robustness and the assortativity signature of human transcription factor networks. PLoS Comput Biol 10:e1003780
Hu, Ting; Banzhaf, Wolfgang; Moore, Jason H (2014) The effects of recombination on phenotypic exploration and robustness in evolution. Artif Life 20:457-70
Darabos, Christian; Harmon, Samantha H; Moore, Jason H (2014) Using the bipartite human phenotype network to reveal pleiotropy and epistasis beyond the gene. Pac Symp Biocomput :188-99
Pan, Qinxin; Hu, Ting; Malley, James D et al. (2014) A system-level pathway-phenotype association analysis using synthetic feature random forest. Genet Epidemiol 38:209-19
Greene, Casey S; Tan, Jie; Ung, Matthew et al. (2014) Big data bioinformatics. J Cell Physiol 229:1896-900
Penrod, N M; Moore, J H (2014) Data science approaches to pharmacogenetics. Curr Mol Med 14:805-13
Payne, Joshua L; Moore, Jason H; Wagner, Andreas (2014) Robustness, evolvability, and the logic of genetic regulation. Artif Life 20:111-26
Hu, Ting; Chen, Yuanzhu; Kiralis, Jeff W et al. (2013) ViSEN: methodology and software for visualization of statistical epistasis networks. Genet Epidemiol 37:283-5
Collins, Ryan L; Hu, Ting; Wejse, Christian et al. (2013) Multifactor dimensionality reduction reveals a three-locus epistatic interaction associated with susceptibility to pulmonary tuberculosis. BioData Min 6:4

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