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Considering Genomic Scans for Selection as Coalescent Model Choice.

Rebecca B Harris1, Jeffrey D Jensen1

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Genomic scans for positive selection are crucial but often inaccurate. This study proposes using the multiple-merger coalescent model to better detect selective sweeps, improving accuracy in population genomic analysis.

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Area of Science:

  • Population genetics
  • Evolutionary biology
  • Genomics

Background:

  • Genomic scans for positive selection are widely used but face challenges with accuracy, leading to low true-positive and high false-positive rates.
  • Current methods often rely on Wright-Fisher assumptions and the Kingman coalescent, struggling with realistic demographic models.
  • Selective sweeps are better described by the multiple-merger coalescent model, offering a potential alternative for detection.

Purpose of the Study:

  • To explore the utility of the multiple-merger coalescent model for detecting selective sweeps in population genomic analysis.
  • To assess the advantages of explicitly testing the fit to the multiple-merger coalescent over rejecting the Kingman coalescent.
  • To compare the power of this new approach against existing methodologies under various demographic scenarios.

Main Methods:

  • Utilizing theoretical results demonstrating selective sweeps are characterized by the multiple-merger coalescent.
  • Developing an approach to explicitly test the relative fit of genomic windows to the multiple-merger coalescent.
  • Comparing the performance of this method against a commonly used approach in population genomics.

Main Results:

  • The proposed approach, based on the multiple-merger coalescent, shows improved power for detecting selective sweeps in certain demographic scenarios.
  • The branching structure differentiating selective and neutral models underlies the advantages of this method.
  • Limitations remain, with certain demographic parameter spaces where power is still insufficient for detection.

Conclusions:

  • Explicitly testing the fit to the multiple-merger coalescent offers a promising advancement in detecting selective sweeps.
  • This approach enhances accuracy compared to methods solely based on rejecting neutral expectations.
  • Further research is needed to address limitations in detecting sweeps across all demographic landscapes.