A Simulation Study to Examine the Information Content in Phylogenomic Data Sets under the Multispecies Coalescent...

Area of Science:

• Computational Biology
• Evolutionary Genetics
• Phylogenomics

Background:

• The multispecies coalescent model is crucial for understanding species evolution.
• Accurate inference of evolutionary parameters requires sufficient genetic data.
• Genomic data presents opportunities and challenges for phylogenetic analysis.

Purpose of the Study:

• To evaluate the information content of multilocus data for evolutionary inference.
• To assess the impact of data characteristics on species tree estimation and delimitation.
• To provide guidelines for subsampling genomic data for efficient analysis.

Main Methods:

• Computer simulations were employed to model multilocus data sets.
• Inference problems included parameter estimation, species tree estimation, and species delimitation.
• Bayesian model comparison was used for species delimitation.

Main Results:

• The number of genetic loci significantly influences most inference tasks.
• Sequence data per species is critical for species delimitation but not species tree estimation.
• Increasing locus sites and mutation rates enhance parameter estimation, with sequence length being more impactful than mutation rate for species trees.

Conclusions:

• The number of loci is the primary driver of inference accuracy in evolutionary studies.
• Data characteristics like sequence length and number of sites impact different phylogenetic analyses variably.
• Subsampling strategies are essential for managing computational costs in large-scale genomic analyses.