Exploring the demographic history of DNA sequences using the generalized skyline plot.

Area of Science:

• Population Genetics
• Bioinformatics
• Evolutionary Biology

Background:

• Understanding population dynamics is crucial in evolutionary studies.
• Existing methods for demographic history inference have limitations with unresolved genealogies or low genetic variability.
• Accurate estimation of effective population size through time informs conservation and evolutionary insights.

Purpose of the Study:

• To introduce a novel visual framework, the generalized skyline plot, for exploring demographic history.
• To provide a nonparametric method for estimating effective population size over time.
• To enhance applicability to datasets with incomplete phylogenetic resolution and low genetic variation.

Main Methods:

• Inferred genealogies from DNA sequences.
• Developed a generalized skyline plot by grouping adjacent coalescent intervals.
• Utilized a small-sample Akaike information criterion for optimal grouping strategy selection.
• Validated the approach through simulations.

Main Results:

• The generalized skyline plot offers a robust method for demographic inference.
• The approach effectively handles datasets with unresolved trees and low variability.
• Simulations confirmed the method's performance.
• Applied successfully to HIV-1 and red panda mtDNA sequence data.

Conclusions:

• The generalized skyline plot is an intuitive and broadly applicable tool for population genetic studies.
• This method advances the non-parametric estimation of effective population size through time.
• The framework provides valuable insights into the demographic histories of various species.