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Screening for epistatic selection signatures: A simulation study.

S Id-Lahoucine1,2, A Molina3, A Cánovas4

  • 1Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, N1G 2W1, ON, Canada. sidlahou@uoguelph.ca.

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This study simulated genomic data to detect epistatic selection (ES). D

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Area of Science:

  • Population Genetics
  • Evolutionary Genomics
  • Bioinformatics

Background:

  • Epistasis (gene-gene interactions) is crucial for understanding evolution.
  • Detecting epistatic selection (ES) signatures in genomic data is challenging.
  • Divergent selection between subpopulations can reveal complex genetic interactions.

Purpose of the Study:

  • To simulate genomic data under divergent epistatic selection (ES).
  • To evaluate the efficacy of D'IS² and FST statistics in detecting ES.
  • To explore the potential of these statistics in identifying specific ES types.

Main Methods:

  • Genome-wide scan using D'IS² and FST statistics on simulated population data.
  • Focus on detecting additive-by-additive ES (ESaa) and other ES types.
  • Analysis of statistical departures and allele frequency patterns.

Main Results:

  • D'IS² successfully identified loci under additive-by-additive ES (ESaa) with high divergence.
  • Other ES types were not as readily detected by the tested statistics.
  • Distinguishing ESaa from single-locus selection (SS) remained a significant challenge.

Conclusions:

  • D'IS² shows promise for detecting specific types of epistatic selection (ESaa).
  • Limitations exist in differentiating ES from SS and other evolutionary events.
  • Further development of statistical methods is needed for comprehensive ES detection.