Evolutionary sparse learning reveals the shared genetic basis of convergent traits

John B. Allard, Sudip Sharma, Ravi Patel, Maxwell Sanderford, Koichiro Tamura, Slobodan Vucetic, Glenn S. Gerhard () and Sudhir Kumar ()
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John B. Allard: Temple University
Sudip Sharma: Temple University
Ravi Patel: Temple University
Maxwell Sanderford: Temple University
Koichiro Tamura: Tokyo Metropolitan University
Slobodan Vucetic: Temple University
Glenn S. Gerhard: Lewis Katz School of Medicine at Temple University
Sudhir Kumar: Temple University

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Cases abound in which nearly identical traits have appeared in distant species facing similar environments. These unmistakable examples of adaptive evolution offer opportunities to gain insight into their genetic origins and mechanisms through comparative analyses. Here, we present an approach to build genetic models that underlie the independent origins of convergent traits using evolutionary sparse learning with paired species contrast (ESL-PSC). We tested the hypothesis that common genes and sites are involved in the convergent evolution of two key traits: C4 photosynthesis in grasses and echolocation in mammals. Genetic models were highly predictive of independent cases of convergent evolution of C4 photosynthesis. Genes contributing to genetic models for echolocation were highly enriched for functional categories related to hearing, sound perception, and deafness, a pattern that has eluded previous efforts applying standard molecular evolutionary approaches. These results support the involvement of sequence substitutions at common genetic loci in the evolution of convergent traits. Benchmarking on empirical and simulated datasets showed that ESL-PSC could be more sensitive in proteome-scale analyses to detect genes with convergent molecular evolution associated with the acquisition of convergent traits. We conclude that phylogeny-informed machine learning naturally excludes apparent molecular convergences due to shared species history, enhances the signal-to-noise ratio for detecting molecular convergence, and empowers the discovery of common genetic bases of trait convergences.

Date: 2025
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DOI: 10.1038/s41467-025-58428-8

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