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From typical sequences to typical genotypes.

Omri Tal1, Tat Dat Tran1, Jacobus Portegies1

  • 1Max-Planck-Institute for Mathematics in the Sciences, Inselstrasse 22, D-04103 Leipzig, Germany.

Journal of Theoretical Biology
|February 17, 2017
PubMed
Summary
This summary is machine-generated.

Information theory concepts, like typical sequences, are applied to population genetics. This reveals insights into typical genotypes and population entropy, aiding in robust classifier development.

Keywords:
ClassificationPopulation cross entropy ratePopulation entropy rateTypical genotypesTypical sequences

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Area of Science:

  • Genetics
  • Information Theory
  • Statistical Learning

Background:

  • Population genetics data analysis often faces challenges with high dimensionality and noise.
  • Information theory provides powerful tools for understanding data compression and transmission, with concepts like typical sequences offering a novel perspective.

Purpose of the Study:

  • To apply core information theory concepts, specifically typical sequences and their properties, to the analysis of population genetic data.
  • To introduce and explore the concepts of typical genotypes and population entropy/cross-entropy rate within a population genetics framework.

Main Methods:

  • Utilizing the asymptotic equipartition property (AEP) for nonstationary discrete-time sources producing independent symbols.
  • Analyzing typical genotypes from set, geometric, and statistical learning perspectives.
  • Developing typical-set based classifiers for population genetic data.

Main Results:

  • Demonstrated the application of information theory's typical sequences to population genetic data analysis.
  • Introduced and defined typical genotypes, population entropy, and cross-entropy rate.
  • Showcased the resilience of typical-set based classifiers to noise from small population samples.

Conclusions:

  • Information theory offers a valuable framework for analyzing population genetic data.
  • Typical-set based classifiers show promise for robust genetic analysis, even with limited data.
  • The study highlights potential for integrating inference and communication theories in population genetics.