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Toward an Information Theory of Quantitative Genetics
David J Galas1, James Kunert-Graf1, Lisa Uechi1
1Pacific Northwest Research Institute, Seattle, Washington, USA.
Summary
Quantitative genetics can be reformulated using information theory, offering new ways to analyze complex genetic data and interactions. This approach provides sensitive measures for genetic quantities like heritability and epistasis.
Area of Science:
- Genetics
- Information Theory
- Computational Biology
Background:
- Quantitative genetics has advanced significantly, but current theoretical frameworks struggle with the complexity of modern genetic data.
- Information theory offers a powerful, unbiased mathematical language for analyzing statistical dependencies.
Purpose of the Study:
- To lay the foundation for an alternative quantitative genetics framework based on information theory.
- To unify information measures and express key quantitative genetic relationships.
Main Methods:
- Utilizing information theory to measure statistical dependencies among variables.
- Applying information-based measures to genotype and phenotype data.
- Developing information-based measures for penetrance, heritability, and epistasis.
Main Results:
- A unified framework for information measures in discrete functions.
- Identification of significant genetic interactions using variable interdependency.
- Information-based quantification of genetic parameters including additive effects, epistasis, and pleiotropy.
Conclusions:
- Information theory provides a robust foundation for a new formulation of quantitative genetics.
- This approach can analyze complex genetic interactions and be adapted for population structure and linkage.
- The framework enables sensitive and unbiased inference of functional relationships in genetic data.
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