Improved confidence intervals for the linkage disequilibrium method for estimating effective population size

Area of Science:

• Population Genetics
• Statistical Genetics
• Bioinformatics

Background:

• The linkage disequilibrium method is the primary single-sample estimator for genetic effective population size.
• Current software offers parametric and jackknife methods for confidence intervals, but their coverage performance is not well-understood.
• Existing methods may require improvement for accurate confidence interval estimation.

Purpose of the Study:

• To propose and evaluate two novel methods for generating confidence intervals for genetic effective population size estimates.
• To compare the performance of the new methods against established parametric and jackknife approaches.
• To assess confidence interval coverage under various simulation scenarios.

Main Methods:

• A simulation study was conducted to compare confidence interval methods.
• Two new confidence interval calculation methods were developed.
• The performance was evaluated against existing parametric and jackknife methods.

Main Results:

• The proposed confidence interval methods demonstrated conservative behavior.
• New methods outperformed existing ones in specific scenarios, especially with large numbers of single-nucleotide polymorphisms.
• Simulation results indicate potential improvements in confidence interval accuracy.

Conclusions:

• The novel confidence interval methods offer a valuable alternative for estimating genetic effective population size.
• These methods provide more reliable estimates, particularly in complex datasets.
• Further research is warranted to explore their application in diverse population genetic studies.