High-recombining genomic regions affect demography inference based on ancestral recombination graphs

Area of Science:

• Population Genetics
• Genomics
• Evolutionary Biology

Background:

• Demography inference often relies on the ancestral recombination graph (ARG).
• High-recombination regions pose challenges for ARG-based inference due to limited mutation data.
• The impact of these regions on demographic inference is understudied, especially in species like birds.

Purpose of the Study:

• To investigate the impact of high-recombination regions on demographic inference using population genomic simulations.
• To assess how these regions affect estimates of effective population size and divergence times.

Main Methods:

• Population genomic simulations were employed to model genetic data.
• Simulated data from high-recombination regions were analyzed.
• Demographic inference methods based on ARGs were applied to simulated datasets.
• Empirical analysis using the Eurasian blackcap (Sylvia atricapilla) genome was conducted.

Main Results:

• Inference of effective population size and population split times is systematically biased by extensive high-recombination regions.
• Excluding high-recombination regions effectively mitigates these biases.
• Population genomic inference of recombination maps aids in identifying these regions, though local rates may be biased.

Conclusions:

• Demographic inference using ARGs requires caution in species with large high-recombination genomic segments.
• Careful consideration of genomic regions with varying recombination rates is crucial for accurate demographic inference.
• Recombination maps can guide the selection of appropriate genomic regions for analysis.