DILS: Demographic inferences with linked selection by using ABC

Area of Science:

• Population genomics
• Evolutionary biology
• Bioinformatics

Background:

• Understanding population dynamics and genetic divergence is crucial for speciation research.
• Linked selection can obscure demographic signals in genomic data.
• Existing tools may not fully integrate demographic modeling with the detection of selection-linked genomic heterogeneity.

Purpose of the Study:

• To introduce DILS, a novel statistical analysis platform for demographic inference using population genomic data.
• To enable the identification of demographic models, genomic heterogeneity due to linked selection, and barriers to gene flow.
• To facilitate collaborative speciation genomics research through a user-friendly web interface.

Main Methods:

• Approximate Bayesian Computation (ABC) framework for statistical inference.
• Hierarchical analysis of single- or two-population multilocus sequence data.
• Simulation-based performance evaluation and application to empirical Mytilus mussel data.

Main Results:

• DILS successfully identifies demographic models, including gene flow and population size changes.
• The platform detects genomic heterogeneity in effective size (Ne) and migration rate (Nm), indicative of linked selection.
• DILS pinpoints genomic regions associated with barriers to gene flow, validated through simulations and empirical data.

Conclusions:

• DILS provides a robust framework for demographic inference and detecting linked selection in population genomic data.
• The platform aids in understanding the genomic basis of speciation by identifying gene flow barriers.
• DILS is a valuable tool for collaborative research in evolutionary and speciation genomics.