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Computational methods for inferring demographic history from genome data are improved by a new approach using generating functions (GFs) of genealogies. This method enables likelihood calculations for larger samples, overcoming previous limitations with small sample sizes.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Genomics

Background:

  • Inferring demographic history from genomic data is computationally challenging.
  • Existing methods struggle to utilize genealogical information across the genome efficiently.
  • Previous applications of generating functions (GFs) were limited to small sample sizes.

Purpose of the Study:

  • To develop efficient computational approaches for inferring demographic history from genome data.
  • To overcome limitations of previous methods by exploiting symmetries in the coalescent.
  • To enable likelihood calculations for larger and more complex demographic models.

Main Methods:

  • Decomposition of the GF of genealogies into equivalence classes.
  • Automated blockwise likelihood calculations using Mathematica.
  • Application to demographic scenarios including population size changes, migration, divergence, and admixture.

Main Results:

  • A novel strategy for exploiting genealogical symmetries was developed.
  • Likelihood calculations are now feasible for non-trivial sample sizes.
  • The method was successfully applied to an isolation with migration (IM) model for two butterfly genomes.

Conclusions:

  • The developed method significantly enhances the computational efficiency of demographic history inference.
  • This approach allows for more complex demographic models to be analyzed.
  • The findings provide a powerful new tool for evolutionary genomics research.