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MASCOT: parameter and state inference under the marginal structured coalescent approximation.

Nicola F Müller1,2, David Rasmussen1,2,3,4, Tanja Stadler1,2

  • 1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.

Bioinformatics (Oxford, England)
|May 24, 2018
PubMed
Summary
This summary is machine-generated.

We developed an improved structured coalescent approximation for analyzing population genetics data. This method offers unbiased estimates of migration rates and population sizes, even with large datasets and many sub-populations.

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Area of Science:

  • Population genetics
  • Computational phylogenetics
  • Bioinformatics

Background:

  • Structured coalescent models are crucial for inferring population demography and migration from genetic data.
  • Existing methods face computational limitations with large datasets or suffer from biases due to strong approximations.

Purpose of the Study:

  • To enhance the structured coalescent approximation for more accurate and efficient analysis of genetic sequence data.
  • To improve the inference of sub-population structure and dynamics from complex datasets.

Main Methods:

  • Developed a novel algorithm to incorporate full phylogenetic tree information for internal node state probability calculation.
  • Implemented improved integration techniques for enhanced computational efficiency.
  • Extended a previously introduced approximation with weaker assumptions to the structured coalescent.

Main Results:

  • The new algorithm increases the accuracy of assigning nodes to their true sub-population.
  • The enhanced method allows for the analysis of larger genetic datasets, demonstrated with an H3N2 influenza dataset.
  • Unbiased estimates of migration rates and population sizes are achieved across a wide parameter range.

Conclusions:

  • The improved structured coalescent approximation provides a computationally efficient and accurate tool for population genetic inference.
  • This method overcomes limitations of previous approaches, enabling analysis of larger and more complex datasets.
  • The MASCOT package in BEAST2 implements these advancements for broader accessibility in population genetic studies.