An HMM-based comparative genomic framework for detecting introgression in eukaryotes.

Area of Science:

• Genomics
• Evolutionary Biology
• Bioinformatics

Background:

• Interspecific hybridization leads to introgression, where genetic material transfers between species.
• Hybridization and introgression play significant roles in eukaryotic evolution and adaptation.
• Existing methods may not fully capture complex evolutionary histories including incomplete lineage sorting.

Purpose of the Study:

• To introduce PhyloNet-HMM, a novel comparative genomic framework for detecting introgression.
• To simultaneously model evolutionary history and genomic dependencies, including incomplete lineage sorting and inter-locus dependence.
• To provide a powerful tool for systematic analysis of introgression across genomes.

Main Methods:

• PhyloNet-HMM integrates phylogenetic networks with hidden Markov models (HMMs).
• The framework accounts for reticulate evolutionary histories and intra-genomic dependencies.
• It incorporates incomplete lineage sorting and dependence across loci for enhanced accuracy.

Main Results:

• PhyloNet-HMM successfully detected adaptive introgression of the Vkorc1 gene in the mouse genome.
• Analysis revealed approximately 9% of sites on mouse chromosome 7 are of introgressive origin, spanning over 300 genes.
• The model showed high accuracy on simulated datasets and correctly identified no introgression in a negative control dataset.

Conclusions:

• PhyloNet-HMM offers a robust framework for the systematic detection and analysis of genomic introgression.
• The tool effectively models complex evolutionary processes including recombination, mutation, and ancestral polymorphism.
• This advancement facilitates a deeper understanding of species evolution driven by hybridization and introgression.