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VolcanoFinder: Genomic scans for adaptive introgression.

Derek Setter1,2,3, Sylvain Mousset1, Xiaoheng Cheng4

  • 1Department of Mathematics, University of Vienna, Vienna, Austria.

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Summary

A new method, VolcanoFinder, detects adaptive introgression using only recipient species data. This approach identifies adaptive alleles from gene flow, even when the donor species is unknown.

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Population Genetics

Background:

  • Introgression between species introduces adaptive alleles, driving evolution across taxa.
  • Detecting adaptive introgression typically requires genomic data from both recipient and donor species.
  • Donor species data is often unavailable, limiting introgression studies.

Purpose of the Study:

  • Introduce VolcanoFinder, a novel genome-scan method for detecting recent adaptive introgression.
  • Enable detection of adaptive introgression using only polymorphism data from the recipient species.
  • Address limitations of current methods requiring donor species data.

Main Methods:

  • VolcanoFinder analyzes patterns of excess intermediate-frequency polymorphism in flanking genomic regions.
  • The method identifies a characteristic 'volcano-shape' in pairwise genetic diversity.
  • A composite-likelihood test is developed based on coalescent theory predictions.

Main Results:

  • VolcanoFinder demonstrates high statistical power for detecting adaptive introgression signatures.
  • The method is effective even for older sweeps and soft sweeps involving multiple haplotypes.
  • Application to human populations identified candidate archaic introgression regions (e.g., TSHR, TCHH-RPTN).

Conclusions:

  • VolcanoFinder provides a powerful tool for studying adaptive introgression without donor species data.
  • The method has implications for understanding evolutionary processes and human population genetics.
  • Guidelines are provided for empirical application and artifact mitigation.