Evolutionary sparse learning reveals the shared genetic basis of convergent traits

Area of Science:

• Evolutionary biology
• Genetics
• Bioinformatics

Background:

• Convergent evolution, where similar traits arise independently in different species, provides insights into adaptive evolution.
• Identifying the genetic basis of convergent traits is crucial for understanding evolutionary mechanisms.

Purpose of the Study:

• To develop and validate a novel computational approach, evolutionary sparse learning with paired species contrast (ESL-PSC), for identifying common genetic factors underlying convergent traits.
• To test the hypothesis that shared genes and genetic loci are involved in the independent evolution of C4 photosynthesis in grasses and echolocation in mammals.

Main Methods:

• Applied ESL-PSC to analyze genetic data from species with convergent traits.
• Benchmarked ESL-PSC performance against empirical and simulated datasets.
• Utilized machine learning and phylogenetic information to detect molecular convergence.

Main Results:

• ESL-PSC accurately predicted convergent evolution of C4 photosynthesis.
• Genes identified for echolocation convergence were significantly enriched for hearing-related functions.
• The method demonstrated higher sensitivity in detecting convergent molecular evolution compared to standard approaches.

Conclusions:

• Phylogeny-informed machine learning, like ESL-PSC, effectively identifies common genetic bases for convergent traits.
• ESL-PSC enhances the detection of molecular convergence by reducing noise from shared evolutionary history.
• This approach empowers the discovery of shared genetic mechanisms driving adaptation across species.