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

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 (H_e), 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.