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Effects of sampling close relatives on some elementary population genetics analyses.

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Excessive close relatives (ECR) in genetic samples significantly impact population genetics analyses. The likelihood approach, which accounts for ECR, provides the most accurate parameter estimates for molecular ecology studies.

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Area of Science:

  • Molecular Ecology
  • Population Genetics
  • Bioinformatics

Background:

  • Molecular ecology studies often assume random sampling, overlooking the common issue of excessive close relatives (ECR) in samples.
  • The impact of ECR on fundamental genetic analyses and methods to mitigate their effects remain underexplored.

Purpose of the Study:

  • To quantify the effects of ECR on key population genetics analyses using marker data.
  • To investigate and compare strategies for handling ECR to achieve unbiased and accurate parameter estimates.

Main Methods:

  • Quantification of ECR effects on allele frequencies, F-statistics, heterozygosity (He), allele numbers, and Hardy-Weinberg (HWE) and linkage equilibrium (LE) tests.
  • Simulation-assisted analysis to evaluate four strategies: ignoring ECR, removing ECR (bold approach), accounting for ECR (cautious approach), and a likelihood-based approach.

Main Results:

  • ECR significantly impact all tested marker analyses, leading to biased estimates and false HWE/LE rejections when ignored.
  • Removing ECR or using the cautious approach reduces bias but can decrease precision.
  • The likelihood approach effectively mitigates bias and improves accuracy in parameter estimation.

Conclusions:

  • Ignoring ECR in population genetics analyses can lead to inaccurate conclusions.
  • The likelihood approach offers a robust method for handling ECR, yielding the most reliable parameter estimates.
  • The choice of strategy depends on the specific marker analysis and desired balance between accuracy and precision.