Contemporary N_e estimation using temporally spaced data with linked loci.

Area of Science:

• Population genetics
• Conservation science
• Genomics

Background:

• Estimating effective population size ($$) is crucial for population genetics, conservation, and pest management.
• Current methods often assume independence among genetic loci, limiting their application with linked loci.
• Linked loci can result in unreliable, overly narrow confidence intervals (CI) for $$ estimates.

Purpose of the Study:

• To extend existing methods for estimating contemporary effective population size ($$) to effectively utilize linked genetic loci.
• To develop a method that produces precise $$ estimates with reliable confidence interval width and coverage, even with linked loci.
• To address the issue of unreliable confidence intervals caused by genetic linkage in $$ estimation.

Main Methods:

• Derived the covariance of allele frequency changes at linked loci, accounting for recombination and sampling errors.
• Developed a method to properly handle the extra sampling variance caused by between-locus correlations.
• Validated the new method through extensive simulations.

Main Results:

• The new method enables the use of numerous linked loci for precise $$ estimation.
• The method preserves targeted confidence interval width and coverage.
• Applied to Anopheles mosquitoes, the method yielded $$ estimates of 9,242 for A. coluzzii and 4,826 for A. gambiae.

Conclusions:

• The developed method accurately estimates effective population size ($$) using linked loci.
• This advancement allows for more reliable genetic data analysis in population genetics and conservation.
• The findings provide robust $$ estimates for Anopheles mosquito populations, informing pest management strategies.