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Detection and quantification of introgression using Bayesian inference based on conjugate priors.

Bastian Pfeifer1, Durrell D Kapan2, Sereina A Herzog1

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We developed df-BF, a Bayesian method to detect and quantify introgression, the flow of genes between species. This approach accurately measures introgression and provides robust statistical evidence, improving upon existing methods.

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Area of Science:

  • Genomics
  • Evolutionary Biology
  • Bioinformatics

Background:

  • Introgression, the transfer of genetic material between species, is a significant evolutionary process.
  • Understanding introgression is key to explaining genome evolution and adaptation.
  • Existing methods for detecting introgression have limitations.

Purpose of the Study:

  • To introduce a novel Bayesian model selection approach, df-BF, for detecting and quantifying introgression.
  • To provide a statistically robust method that accounts for genomic region characteristics.
  • To offer a computationally efficient alternative to existing introgression detection methods.

Main Methods:

  • Developed the distance-based Bayes Factor (df-BF) method for introgression detection.
  • Introduced dfθ parameter for accurate quantification of introgression.
  • Utilized conjugate priors for efficient computation, avoiding Markov Chain Monte Carlo (MCMC) iterations.

Main Results:

  • The df-BF method accurately quantifies introgression and weighs evidence using Bayes Factors.
  • Comparisons with existing methods (df, fd, Dp, Patterson's D) using simulations show competitive performance.
  • Demonstrated the practical application of df-BF and dfθ using whole-genome mosquito data.

Conclusions:

  • df-BF is a versatile and efficient Bayesian approach for detecting and quantifying introgression.
  • The method enhances the analysis of genomic data, particularly in evolutionary studies.
  • df-BF is integrated into the PopGenome R package for broader accessibility in genomic research.