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Estimating evolutionary and demographic parameters via ARG-derived IBD.

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This study introduces a novel method for inferring genetic ancestry using identical-by-descent (IBD) segments, improving computational efficiency and accuracy in population genetics. The approach enhances evolutionary and demographic parameter estimation from genome sequences.

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Area of Science:

  • Population Genetics
  • Computational Biology
  • Genomics

Background:

  • Accurate inference of evolutionary and demographic parameters relies on identifying identical-by-descent (IBD) segments within genome sequences.
  • Current methods often require stringent conditions, such as minimum segment length and absence of recombination, limiting their applicability.

Purpose of the Study:

  • To develop a more efficient and flexible method for inferring IBD segments and demographic parameters from genome sequence data.
  • To overcome limitations of existing approaches by removing the need for IBD segment length thresholds and recombination-free requirements.

Main Methods:

  • Utilized efficient data encoding based on the ancestral recombination graph (ARG).
  • Developed an approximate Bayesian computation (ABC) algorithm for inferring IBD from real data.
  • Employed a subset of IBD segments from sequence pairs, scaling linearly with sample size, to reduce computational cost.

Main Results:

  • Demonstrated powerful inferences using simulated data with true IBD information.
  • Showed that even poorly-inferred short IBD segments improve parameter estimation when using the ABC algorithm.
  • Achieved comparable precision in mutation-rate estimation to previous methods with significantly less data.
  • Identified significant genetic differences between human populations.

Conclusions:

  • The ARG-based approach offers significant advantages in inferring IBD segments and demographic parameters.
  • The method enhances statistical efficiency and reduces computational burden in population genetics analyses.
  • The approach is robust to nuisance parameters and model misspecification, improving overall parameter inference.