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Factor analysis of ancient population genomic samples.

Olivier François1, Flora Jay2

  • 1Université Grenoble-Alpes, Centre National de la Recherche Scientifique, Grenoble INP, Laboratoire TIMC-IMAG UMR 5525, 38000, Grenoble, France. olivier.francois@univ-grenoble-alpes.fr.

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|September 17, 2020
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Summary

A new factor analysis (FA) method improves ancient DNA analysis by correcting for allele frequency drift over time. This method offers more accurate representations of ancient samples compared to principal component analysis (PCA).

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Area of Science:

  • Population Genetics
  • Paleogenomics
  • Bioinformatics

Background:

  • Ancient DNA studies increasingly use Principal Component Analysis (PCA) to investigate evolutionary questions.
  • Standard PCA methods ignore temporal information in samples, leading to inaccurate representations in analyses.

Purpose of the Study:

  • To develop a novel Factor Analysis (FA) method that corrects for allele frequency drift in temporal ancient DNA samples.
  • To improve the accuracy of geometric representations and admixture analyses of ancient DNA data.

Main Methods:

  • Developed an FA method with exact solutions for corrected factor estimates.
  • Created a fast algorithm for computing corrected factors.
  • Compared FA with PCA and ancestry estimation programs using simulations and ancient European DNA.

Main Results:

  • FA provided more accurate admixture estimates than PCA and other programs.
  • FA results agreed with established tree-based statistics.
  • FA enhances descriptive analyses of ancient DNA without needing outgroup or present-day samples.

Conclusions:

  • The proposed FA method offers a significant improvement for analyzing temporal ancient DNA datasets.
  • FA provides a more accurate and robust approach to understanding population dynamics and evolutionary history from ancient DNA.