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 #
5R01AI059694-08
Application #
8120463
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Gezmu, Misrak
Project Start
2004-08-01
Project End
2013-07-31
Budget Start
2011-08-01
Budget End
2012-07-31
Support Year
8
Fiscal Year
2011
Total Cost
$477,205
Indirect Cost
Name
Dartmouth College
Department
Genetics
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755
Greene, Anna C; Giffin, Kristine A; Greene, Casey S et al. (2016) Adapting bioinformatics curricula for big data. Brief Bioinform 17:43-50
Qiu, Jingya; Moore, Jason H; Darabos, Christian (2016) Studying the Genetics of Complex Disease With Ancestry-Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations. Genet Epidemiol 40:293-303
Chernikova, Diana A; Koestler, Devin C; Hoen, Anne Gatewood et al. (2016) Fetal exposures and perinatal influences on the stool microbiota of premature infants. J Matern Fetal Neonatal Med 29:99-105
Darabos, Christian; Qiu, Jingya; Moore, Jason H (2016) AN INTEGRATED NETWORK APPROACH TO IDENTIFYING BIOLOGICAL PATHWAYS AND ENVIRONMENTAL EXPOSURE INTERACTIONS IN COMPLEX DISEASES. Pac Symp Biocomput 21:9-20
Sobota, Rafal S; Stein, Catherine M; Kodaman, Nuri et al. (2016) A Locus at 5q33.3 Confers Resistance to Tuberculosis in Highly Susceptible Individuals. Am J Hum Genet 98:514-24
Darabos, Christian; Grussing, Emily D; Cricco, Maria E et al. (2015) A bipartite network approach to inferring interactions between environmental exposures and human diseases. Pac Symp Biocomput :171-82
Moore, Jason H; Andrews, Peter C (2015) Epistasis analysis using multifactor dimensionality reduction. Methods Mol Biol 1253:301-14
Moore, Jason H; Hill, Doug P (2015) Epistasis analysis using artificial intelligence. Methods Mol Biol 1253:327-46
Moore, Jason H (2015) The critical need for computational methods and software for simulating complex genetic and genomic data. Genet Epidemiol 39:1
Urbanowicz, Ryan J; Moore, Jason H (2015) ExSTraCS 2.0: Description and Evaluation of a Scalable Learning Classifier System. Evol Intell 8:89-116

Showing the most recent 10 out of 91 publications