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PSMC-FAC: Automated Optimization of False-Negative Rate Corrections for Low-Coverage PSMC-Based Demographic

Francisco Iglesias-Santos1,2,3, Alba Nieto4, Sònia Casillas1,5

  • 1Institut de Biotecnologia i Biomedicina (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain.

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Summary
This summary is machine-generated.

Low-coverage genomes can now reliably inform population history. PSMC-FAC corrects false-negative rates, improving demographic inference from whole-genome data, especially in conservation genomics.

Keywords:
Fréchet distanceHausdorff distancePSMCSequential Markovian Coalescentdemographic inferencelow-coverage genome

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Area of Science:

  • Evolutionary genomics
  • Conservation genomics
  • Population genetics

Background:

  • Demographic history inference from whole-genome data is crucial.
  • The Pairwise Sequentially Markovian Coalescent (PSMC) method is sensitive to low sequencing coverage, causing biased effective population size estimates.
  • Existing correction methods for low-coverage data can be subjective.

Purpose of the Study:

  • To develop an automated method (PSMC-FAC) for optimizing false-negative rate correction in low-coverage genomes.
  • To improve the accuracy of demographic inference from whole-genome sequence data.
  • To provide a reproducible and mathematically grounded alternative to subjective correction approaches.

Main Methods:

  • Developed PSMC-FAC to minimize geometric distances between false-negative rate-corrected low-coverage and high-coverage genomic trajectories.
  • Downsampled whole-genome datasets (humans, gray wolves, cattle) to various coverage levels.
  • Modeled optimal correction factors as a function of sequencing depth using polynomial regression and compared trajectories using Hausdorff and Fréchet distances.

Main Results:

  • PSMC-FAC significantly enhanced concordance between low- and high-coverage genomic trajectories across species.
  • Demonstrated highly predictable, coverage-dependent correction patterns.
  • Showcased reliable demographic inference from moderate-coverage genomes.

Conclusions:

  • PSMC-FAC offers a robust solution for demographic inference from low- to moderate-coverage whole-genome data.
  • Enables broader population-scale genomic analyses by improving data reliability.
  • Facilitates more accurate evolutionary and conservation genomics studies.