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Information metrics in genetic epidemiology
David L Tritchler1, Lara Sucheston, Pritam Chanda
1State University of New York at Buffalo, Buffalo, NY, USA. dtritch@rogers.com
Summary
Information-theoretic metrics offer insights into gene interactions but lack clear interpretation. This study clarifies their meaning and connection to probability models, aiding genetic epidemiology research.
Area of Science:
- Epidemiology
- Biostatistics
- Genetics
Background:
- Information-theoretic metrics are used to study gene-gene and gene-environment interactions.
- Current interpretations are often rooted in communications theory, limiting their applicability in epidemiology.
- A clearer understanding is needed for their effective use in genetic epidemiology.
Purpose of the Study:
- To clarify the interpretation of information-theoretic metrics for genetic epidemiology.
- To establish the relationship between these metrics and global properties of probability models.
- To contrast information-theoretic metrics with traditional log-linear models.
Main Methods:
- Developed methods to clarify the interpretation of information-theoretic metrics.
- Demonstrated the connection of these metrics to the global properties of probability models.
- Compared information-theoretic metrics with log-linear models for analyzing multinomial data.
Main Results:
- Provided a clear interpretation of information-theoretic metrics relevant to epidemiologists and statisticians.
- Established a clear link between information-theoretic metrics and the underlying probability models.
- Highlighted the differences and potential complementary uses compared to log-linear models.
Conclusions:
- Enhanced understanding of information-theoretic metrics promotes their acceptance and correct application in genetic epidemiology.
- The developed methods offer new avenues for model search and computation in genetic studies.
- This work bridges the gap between information theory and practical epidemiological analysis.
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