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We developed SURFDAWave, a novel method to detect natural selection in genomic data by transforming diversity measures into functional data. This approach identifies complex selection events, including adaptive introgression, and predicts selection parameters.

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Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Identifying positive selection in genomic data is a persistent challenge in population genetics.
  • Current methods often rely on comparing summary statistics within genomic windows.
  • Detecting complex selection modes like adaptive introgression remains difficult.

Purpose of the Study:

  • To introduce SURFDAWave, a new computational approach for detecting natural selection.
  • To translate genomic diversity measures into functional data for improved selection detection.
  • To identify complex selection patterns and predict associated evolutionary parameters.

Main Methods:

  • Developed SURFDAWave, which converts discrete genetic diversity data into continuous functional data.
  • Applied functional data analysis to identify features indicative of natural selection.
  • Modeled selection parameters influencing inferred evolutionary events.

Main Results:

  • Successfully identified known regions of positive selection (selective sweeps) in human genomic data, such as the OCA2 gene in Europeans.
  • Predicted that the beneficial OCA2 mutation reached a frequency of 0.02 and swept 1,802 generations ago.
  • Identified the BNC2 gene in Europeans as a target of adaptive introgression, likely from archaic humans.

Conclusions:

  • SURFDAWave offers a powerful new framework for detecting diverse modes of natural selection, including adaptive introgression.
  • The method accurately identifies selection events and provides insights into their evolutionary dynamics.
  • Findings highlight the utility of SURFDAWave in uncovering complex evolutionary histories in human populations.