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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.

David A Rasmussen1,2, Madeline G Bursell2, Frank Burkhart2

  • 1Dept. of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27607, United States.

Genetics
|November 11, 2025
PubMed
Summary

This study introduces a new framework using Markov decision processes to optimize genomic sampling strategies. It helps predict information gain and identify efficient sampling plans for population genomics and epidemiology.

Keywords:
Markov decision processdemographic inferencegenomic epidemiologypopulation genomicssampling theory

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Area of Science:

  • Population genomics
  • Genomic epidemiology
  • Phylodynamics
  • Phylogeography

Background:

  • Genomic data offers insights into population history and epidemic dynamics.
  • Predicting information gain and the impact of sampling strategies on inferences is challenging.
  • Lack of theory guides optimal individual sampling for genomic sequencing.

Purpose of the Study:

  • To develop a theoretical framework for optimizing genomic sampling strategies.
  • To model the interaction between sampling and demographic history.
  • To predict the informational value of sampling and identify optimal strategies.

Main Methods:

  • Utilized a sequential decision-making framework based on Markov decision processes (MDPs).
  • Modeled how sampling influences ancestral/genealogical relationships.
  • Applied MDPs to demographic and epidemiological inference problems.

Main Results:

  • MDPs predict the expected value of sampling in terms of information gained.
  • Efficiently identified optimal sampling strategies, considering dependencies between sampling events.
  • Demonstrated application in estimating population growth, transmission distance, and migration rates.

Conclusions:

  • The MDP framework provides a method to guide optimal genomic sampling.
  • Maximizes information gain from genomic data while minimizing sampling costs.
  • Enhances decision-making for population genomics and epidemiological studies.