A Next Generation of Hierarchical Bayesian Analyses of Hybrid Zones Enables Model-Based Quantification of Variation...

Area of Science:

• Population genomics
• Speciation genetics
• Evolutionary biology

Background:

• Hybrid zones are critical for understanding species divergence and speciation processes.
• Existing software for analyzing genomic clines has limitations in efficiency and data handling.
• Accurate estimation of ancestry proportions and introgression is essential for evolutionary studies.

Purpose of the Study:

• Introduce bgchm, a novel R package for advanced population genomic analyses in hybrid zones.
• Provide efficient and flexible Bayesian methods for inferring genomic clines and ancestry.
• Facilitate the quantification of genome-wide introgression patterns to study reproductive isolation.

Main Methods:

• Developed bgchm, an R package extending existing Bayesian cline and ancestry estimation software.
• Implemented Hamiltonian Monte Carlo sampling for enhanced computational efficiency.
• Integrated genotype likelihoods and a hierarchical Bayesian framework for diverse genetic data.

Main Results:

• bgchm provides accurate parameter estimates, particularly with ancestry-informative genetic loci.
• The package enables robust estimation of genome-wide variability in genomic clines.
• Demonstrated the utility of bgchm in distinguishing contributions of selection and drift to introgression.

Conclusions:

• bgchm is a powerful tool for population genomic analyses in hybrid zones, offering improved efficiency and flexibility.
• The package advances the study of introgression patterns and their drivers across the genome.
• bgchm holds significant promise for future research in speciation genetics and evolutionary processes.