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Characterizing bias in population genetic inferences from low-coverage sequencing data.

Eunjung Han1, Janet S Sinsheimer, John Novembre

  • 1Department of Biostatistics, University of California, Los Angeles.

Molecular Biology and Evolution
|November 30, 2013
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Summary

Estimating the site frequency spectrum (SFS) from sequencing data is crucial for population genetics. Direct SFS estimation is unbiased even at low coverage, unlike call-based methods which introduce biases affecting variant counts and downstream analyses.

Keywords:
accuracybase-calling errorsmaximum likelihoodsite frequency spectrum

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Area of Science:

  • Population Genetics
  • Genomics
  • Bioinformatics

Background:

  • The site frequency spectrum (SFS) is fundamental for population genetic inferences.
  • Genotype uncertainty from sequencing data introduces bias into SFS estimation.
  • Accurate SFS inference is vital for reliable downstream population genetic analyses.

Purpose of the Study:

  • Compare call-based and direct estimation approaches for inferring the SFS from sequencing data.
  • Evaluate the impact of genotype uncertainty and coverage on SFS accuracy.
  • Assess how SFS estimation biases affect demographic parameter estimation and selection scans.

Main Methods:

  • Inferred genotypes from sequencing reads (call-based approach).
  • Directly estimated SFS from sequencing reads using maximum likelihood.
  • Analyzed bias in SFS estimation across varying coverage levels.
  • Characterized the impact of SFS bias on demographic inference and selection scans.

Main Results:

  • Direct SFS estimation yields unbiased results, even at low sequencing coverage.
  • Call-based SFS estimation becomes biased as coverage decreases.
  • Multisample calling underestimates rare variants; single-sample calling overestimates them.
  • SFS estimation pipeline choice significantly impacts downstream population genetic conclusions.

Conclusions:

  • Direct SFS estimation is robust to low coverage and genotype uncertainty.
  • Call-based methods require careful pipeline selection to mitigate bias in SFS inference.
  • Accurate SFS estimation is critical for reliable population genetic studies, influencing demographic and selection analyses.