We collaborate extensively with a variety of research groups at Dartmouth and around the world.  Our primary NIH-funded research projects are summarized below.

I. Bioinformatics Strategies for Biodefense Vaccine Research

This project is funded by a 5-year grant from the National Institute of Allergy and Infectious Disease (NIH R01 AI59694, PI - Moore) with annual direct costs of approximately $350,000/year.

The identification of biomarkers of adverse events following smallpox vaccination is important for public health and bioterrorism defense.  Fortunately, we now have the ability to measure massive amounts of genetic, genomic, and proteomic information.  Our ability to use this information to facilitate the identification of biomarkers of adverse events will largely depend on the architecture of the genotype-to-phenotype relationship.  The process of innate and adaptive immunity is a complex trait that involves many biochemical and physiological pathways and thus many interacting genes.  The complexity of the genotype-phenotype relationship suggests that we need a research strategy that embraces, rather than ignores, complexities due to gene-gene (GxG) and gene-environment (GxE) interactions.  We are developing and evaluating a comprehensive bioinformatics strategy for identifying combinations biomarkers of adverse events following vaccination for bioterrorism agents.  The bioinformatics strategy combines new and novel statistical and computational methods for detecting GxG and GxE interactions with knowledge about hierarchical biological systems. This strategy is being developed and evaluated using simulated data as well as genetic and proteomic data available from more than 100 volunteers that are part of an ongoing NIAID/NIH-sponsored trial to evaluate the Aventis Pasteur Smallpox Vaccine (APSV).  Software packages for all methods are being developed and will be made available to the research community.

II. Genetic Architecture of Plasma t-PA and PAI-1

This project was funded by a 5-year grant from the National Heart Lung and Blood Institute (NIH R01 HL65234, PI - Moore) with annual direct costs of approximately $250,000/year.

Arterial thrombosis is the proximate cause of events in patients with ischemic cardiovascular disease.  A major risk factor for thrombosis is alteration of the balance between tissue-type plasminogen activator (t-PA) and plasminogen activator inhibitor type 1 (PAI-1), two components of the fibrinolytic system.  Understanding the role of genetic variation in determining plasma t-PA and PAI-1 levels, their variability, and their co-variability will improve our ability to identify those at risk and will facilitate the development of new prevention and treatment strategies.  We have selected two genes from the fibrinolytic system (t-PA and PAI-1) and four genes from the renin-angiotensin system (angiotensinogen (AGT), renin (REN), angiotensin converting enzyme (ACE) and angiotensin II receptor type 1 (AT-1) as candidates for influencing plasma t-PA and PAI-1 levels, their variability, and their co-variability based on the biology of these genes and because our preliminary studies demonstrate an interaction between the two systems.  We have established two large population-based samples of human subjects to document the genetic architecture of plasma t-PA and PAI-1.  The first sample consists of approximately 8500 Caucasians from the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) study in Groningen, The Netherlands.  The second sample consists of more than 2500 Blacks from Ghana, Africa ascertained through our study site in the regional capital of Sunyani.  We are utilizing these two population-based resources to identify the functional variations in the fibrinolytic and renin-angiotensin system genes that influence plasma t-PA and PAI-1 levels, variability, and co-variability.

III. Genetic Basis of Trauma Recovery

This project is funded by a 5-year grant from the National Institute of Child Health and Human Development (NIH R01 HD047447, PI - Moore) with annual direct costs of approximately $370,000/year.

All people, regardless of ethnicity, education, or socioeconomic status, are susceptible to trauma.   Recovery from trauma is highly variable and there are currently no good predictors of who is likely to recover and respond to rehabilitation.  A major factor influencing recovery and rehabilitation is the development of Adult Respiratory Distress Syndrome (ARDS) or Acute Lung Injury (ALI).  The goal of this study is to identify genetic polymorphisms that predict who will develop ARDS or ALI following trauma.  We have ascertained more than 1000 subjects from Nashville, TN that have been admitted to the intensive care unit at Vanderbilt University Medical Center following traumatic injury.  We are focusing on candidate genes for ARDS/ALI that are involved in nitric oxide production, vascular resistance, cellular responses to injury, cellular signaling, and alveolar function.  This study will capitalize on expertise in trauma medicine, biochemical and molecular genetics, statistical genetics, and bioinformatics to provide a comprehensive approach to the identification of genetic predictors of trauma recovery and rehabilitation.

IV. Machine Learning Prediction of Cancer Susceptibility

This new project is funded by a 4-year grant from the National Library of Medicine (NIH R01 LM009012, PI - Moore) with annual direct costs of approximately $200,000/year.

Susceptibility to sporadic forms of cancer is determined by numerous genetic factors that interact in a nonlinear manner in the context of an individual’s age and environmental exposure.  This complex genetic architecture has important implications for the use of genome-wide association studies for identifying susceptibility genes.  The assumption of a simple architecture supports a strategy of testing each single-nucleotide polymorphism (SNP) individually using traditional univariate statistics followed by a correction for multiple tests.  However, a complex genetic architecture that is characteristic of most types of cancer requires analytical methods that specifically model combinations of SNPs and environmental exposures.  While new and novel methods are available for modeling interactions, exhaustive testing of all combinations of SNPs is not feasible on a genome-wide scale because the number of comparisons is effectively infinite.  Thus, it is critical that we develop intelligent strategies for selecting subsets of SNPs prior to combinatorial modeling.  Our objective is to develop a research strategy for the detection, characterization, and interpretation of gene-gene and gene-environment interactions in a genome-wide association study of bladder cancer susceptibility.  To accomplish this objective, we will develop and evaluate modifications and extensions to the ReliefF algorithm for selecting or filtering subsets of single-nucleotide polymorphisms (SNPs) for multifactor dimensionality reduction (MDR) analysis of gene-gene and gene-environment interactions (AIM 1).  We will develop and evaluate a stochastic wrapper or search strategy for MDR analysis of interactions that utilizes ReliefF values as a heuristic (AIM 2).  The filter approach will be statisyically compared to the wrapper approach.  The best ReliefF strategies will be provided as part of our open-source MDR software package (AIM 3).  Finally, we will apply the best ReliefF-MDR analysis strategy to the detection, characterization, and interpretation of gene-gene and gene-environment interactions in a large genome-wide association study of bladder cancer susceptibility (AIM 4).  The methods developed here will be applied to nearly 1500 haplotype tagging SNPs (tagSNPs) across approximately 500 cancer susceptibility genes measured in 542 subjects with bladder cancer and 745 healthy controls ascertained as part of a large epidemiological study from the state of New Hampshire. 

Last updated by JHM on March 2, 2008