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Scalable combinatorial tools for health disparities research.
Michael A Langston1, Robert S Levine2, Barbara J Kilbourne3
1Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, TN 37996, USA. langston@eecs.utk.edu.
International Journal of Environmental Research and Public Health
|October 14, 2014
Summary
Environmental exposures, not just genes, significantly impact chronic diseases and cancer. New analytical methods are needed to understand these complex gene-environment interactions and reduce health disparities.
Area of Science:
- Population Health
- Environmental Health
- Genetics
Background:
- Genetic factors explain only a small portion of chronic disease and cancer variance.
- Environmental exposures throughout life significantly influence health outcomes.
- Traditional analysis methods struggle with large, complex datasets for gene-environment interactions.
Purpose of the Study:
- To describe applications of innovative transdisciplinary models for health disparities research.
- To identify causal mechanisms and environmental contexts underlying health disparities.
- To utilize the public health exposome as a focus for complex analysis.
Main Methods:
- Application of scalable combinatorial methods adapted from genome science.
- Utilizing complicated systems theory, ecological, place-based, and life-history perspectives.
- Employing algorithmically sophisticated, automated, and mathematically abstract analytical tools.
Main Results:
- Demonstrated utility of advanced analytical methods in population health.
- Facilitated a deeper understanding of gene x environment interactions.
- Provided a framework for identifying environmental contributors to health disparities.
Conclusions:
- Innovative transdisciplinary models are crucial for understanding health disparities.
- Advanced analytical approaches are necessary to address the complexity of environmental exposures and health.
- Focusing on the public health exposome aids in uncovering causal links to disease.
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