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Fast admixture analysis and population tree estimation for SNP and NGS data.

Jade Yu Cheng1,2,3, Thomas Mailund1, Rasmus Nielsen2,3

  • 1Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark.

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|March 24, 2017
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Summary

A new optimization algorithm enhances population genetic STRUCTURE analysis for improved ancestry component identification. This method also supports Next Generation Sequencing data and aids in accurate population tree inference.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Structure methods are widely used for classifying individuals into discrete ancestry components.
  • Accurate inference of population structure and evolutionary relationships is crucial in population genetics.

Purpose of the Study:

  • To introduce a novel optimization algorithm for the STRUCTURE model within a maximum likelihood framework.
  • To develop and validate new methods for estimating population trees from ancestry components.
  • To assess the performance of STRUCTURE-style models and Gaussian approximations in inferring ancestry and population trees.

Main Methods:

  • Developed a new maximum likelihood optimization algorithm for the classical STRUCTURE model.
  • Extended the optimization algorithm to handle genotype likelihoods for Next Generation Sequencing (NGS) data.
  • Implemented a new method for population tree estimation using a Gaussian approximation.
  • Utilized coalescence simulations to evaluate model adequacy and inference accuracy.

Main Results:

  • The new optimization method achieves higher likelihoods than state-of-the-art methods in comparable computational time.
  • The algorithm is applicable to genotype likelihoods, accommodating NGS data uncertainty.
  • Population trees are accurately inferred, though ancestry components can be influenced by sample size and admixture timing.
  • The Gaussian approximation shows limitations with highly divergent populations but preserves tree topology.

Conclusions:

  • The developed optimization algorithm offers a more efficient and effective approach to STRUCTURE analysis.
  • The new methods provide robust tools for inferring population structure and evolutionary history, even with NGS data.
  • Ohana software package integrates these methods with visualization tools for broader accessibility.