Measuring Genetic Differentiation from Pool-seq Data.

Area of Science:

• Population genetics
• Genomics
• Bioinformatics

Background:

• High-throughput sequencing enables large-scale polymorphism analysis.
• Pooled DNA sequencing (Pool-seq) is a cost-effective alternative to individual sequencing for nonmodel species.
• Analyzing Pool-seq data presents statistical challenges for estimating genetic differentiation.

Purpose of the Study:

• To develop an unbiased method-of-moments estimator for genetic differentiation ([Formula: see text]) using Pool-seq data.
• To evaluate the performance and robustness of the new estimator against existing methods and model misspecifications.
• To re-evaluate population structure in published Pool-seq data using the improved estimator.

Main Methods:

• Development of a method-of-moments estimator for [Formula: see text] within an analysis-of-variance framework.
• Extensive simulations to assess estimator bias, performance, and robustness to sequencing errors and variable DNA contributions.
• Reanalysis of published Pool-seq data from different ecotypes of *Cottus asper*.

Main Results:

• The proposed estimator for [Formula: see text] is unbiased and outperforms previous estimators in simulations.
• The estimator demonstrates robustness to common model misspecifications in Pool-seq data.
• Reanalysis of *Cottus asper* data suggests potential reinterpretation of population structure.

Conclusions:

• The new [Formula: see text] estimator provides a more accurate and reliable method for analyzing genetic differentiation from Pool-seq data.
• This advancement can improve the understanding of population structure, especially in species where individual sequencing is prohibitive.
• The findings highlight the importance of using appropriate statistical methods for interpreting Pool-seq data.