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Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Zhangyi He1,2, Xiaoyang Dai3, Wenyang Lyu4

  • 1Cancer Research UK Beatson Institute, Glasgow, United Kingdom.

Molecular Biology and Evolution
|January 20, 2023
PubMed
Summary
This summary is machine-generated.

Ancient DNA (aDNA) studies can now track genetic changes over time. A new Bayesian method improves the inference of selection by modeling DNA damage and fragmentation uncertainties.

Keywords:
ancient DNAdemographic historynatural selectionparticle marginal Metropolis-Hastingssampling uncertaintytwo-layer hidden Markov model

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Area of Science:

  • Population Genetics
  • Paleogenomics
  • Bioinformatics

Background:

  • Novel ancient DNA (aDNA) technologies provide temporally spaced genetic samples.
  • These genetic time series enable direct assessment of allele frequency changes and inference of selection.
  • Studying past selection is challenged by aDNA damage, fragmentation, low coverage, and small sample sizes.

Purpose of the Study:

  • To introduce a novel Bayesian framework for inferring temporally variable selection.
  • To enable modeling of sample uncertainties arising from aDNA damage and fragmentation.
  • To reconstruct population allele frequency trajectories for better understanding of selection drivers.

Main Methods:

  • Developed a Bayesian framework utilizing genotype likelihoods instead of allele frequencies.
  • Modeled uncertainties associated with damaged and fragmented aDNA molecules.
  • Applied the framework to ancient horse coat coloration loci.

Main Results:

  • The novel Bayesian framework effectively models aDNA uncertainties.
  • Reconstruction of allele frequency trajectories provides insights into selection drivers.
  • Incorporating sample uncertainties significantly improves the inference of selection.

Conclusions:

  • The developed Bayesian framework overcomes key challenges in aDNA-based selection inference.
  • This approach enhances the power to study past selection events, including domestication.
  • The method offers a robust tool for analyzing genetic time series from ancient populations.