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Identifying Single-Origin Rare Variants in Population Genomic Data.

Josh J Reynolds1, Vassiliki Koufopanou1, Austin Burt1

  • 1Department of Life Sciences, Imperial College London, Silwood Park, Ascot SL5 7PY, United Kingdom.

Molecular Biology and Evolution
|November 3, 2025
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Summary
This summary is machine-generated.

Recurrent mutations can be common in large genomic datasets, potentially skewing demographic analyses. New methods accurately identify single-origin variants, improving population genetic studies.

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Area of Science:

  • Population Genomics
  • Molecular Evolution
  • Bioinformatics

Background:

  • Genomic analyses reveal that mutations can arise independently multiple times at the same genetic locus.
  • The phenomenon of recurrent mutation is often overlooked, potentially introducing errors in demographic analyses and co-ancestry estimations.
  • Rare variants like doubletons, crucial for understanding recent population history, may also originate from multiple independent mutational events.

Purpose of the Study:

  • To develop novel computational methods for estimating the frequency of recurrent doubletons in population genomic data.
  • To establish a robust approach for identifying high-confidence, single-origin doubletons using linked rare variants and shared haplotypes.
  • To apply these methods to Anopheles gambiae population data to assess the impact of recurrent mutation on demographic inference.

Main Methods:

  • Developed a method to estimate recurrent doubleton frequency using tri-allelic sites with two distinct singleton mutations.
  • Implemented a strategy to identify single-origin doubletons by leveraging linked rare variants on shared haplotypes.
  • Applied these computational tools to population genomic data of Anopheles gambiae from African populations.

Main Results:

  • Estimated that approximately 16% of doubletons in the Anopheles gambiae dataset originated from independent mutational events.
  • Successfully identified a high-confidence subset (∼99% probability) of single-origin doubletons, comprising a significant portion of expected and observed doubletons.
  • Demonstrated the efficacy of the developed methods through subsequent data analyses and coalescent simulations.

Conclusions:

  • The developed methods effectively address the challenge of recurrent mutation in large-scale genomic datasets.
  • Accurate identification of single-origin doubletons enhances the reliability of demographic inference, especially in large populations.
  • These tools provide a valuable resource for investigating population genetic processes like recombination, selection, and isolation by distance.