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Quantitative evaluation of nonlinear methods for population structure visualization and inference.

Jordan Ubbens1, Mitchell J Feldmann2, Ian Stavness1,3

  • 1Global Institute for Food Security (GIFS), University of Saskatchewan, Saskatoon, SKS7N 0W9, Canada.

G3 (Bethesda, Md.)
|July 28, 2022
PubMed
Summary
This summary is machine-generated.

Population structure analysis reveals differences in allele frequencies between subpopulations. Graph-based methods like t-SNE and UMAP outperform principal component analysis for visualizing population structure.

Keywords:
machine learningpopulation structurevisualization

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Area of Science:

  • Population genetics
  • Bioinformatics
  • Computational biology

Background:

  • Population structure, or genetic structure, describes systematic differences in allele frequencies between subpopulations due to nonrandom mating.
  • It is crucial for understanding genetic ancestry and acts as a confounding variable in genome-wide association studies (GWAS).
  • Numerous nonlinear dimensionality reduction techniques have been developed for visualizing population structure, but objective comparisons are lacking.

Purpose of the Study:

  • To address the absence of objective comparisons for nonlinear dimensionality reduction techniques used in population structure visualization.
  • To propose and validate a novel quantitative evaluation methodology for these techniques.
  • To identify superior methods for population structure analysis and visualization.

Main Methods:

  • Discussion of existing nonlinear dimensionality reduction techniques and their limitations.
  • Development of a quantitative evaluation methodology using populations with known pedigree (artificial selection or simulation).
  • Comparative analysis of techniques including principal component analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and neural network-based methods.

Main Results:

  • Graph-based algorithms, specifically t-SNE and UMAP, demonstrated superior performance in population structure visualization compared to principal component analysis.
  • Neural network-based methods showed lower performance in this comparative evaluation.
  • The proposed evaluation metric provides an objective basis for comparing nonlinear dimensionality reduction techniques.

Conclusions:

  • t-SNE and UMAP are effective and recommended methods for population structure visualization.
  • Principal component analysis is less effective than graph-based methods for this task.
  • The developed evaluation methodology facilitates objective assessment and selection of appropriate techniques for population genetics research.