IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-58428-8.html
   My bibliography  Save this article

Evolutionary sparse learning reveals the shared genetic basis of convergent traits

Author

Listed:
  • John B. Allard

    (Temple University
    Temple University)

  • Sudip Sharma

    (Temple University
    Temple University)

  • Ravi Patel

    (Temple University
    Temple University)

  • Maxwell Sanderford

    (Temple University
    Temple University)

  • Koichiro Tamura

    (Tokyo Metropolitan University
    Tokyo Metropolitan University)

  • Slobodan Vucetic

    (Temple University)

  • Glenn S. Gerhard

    (Lewis Katz School of Medicine at Temple University)

  • Sudhir Kumar

    (Temple University
    Temple University)

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.

Suggested Citation

  • John B. Allard & Sudip Sharma & Ravi Patel & Maxwell Sanderford & Koichiro Tamura & Slobodan Vucetic & Glenn S. Gerhard & Sudhir Kumar, 2025. "Evolutionary sparse learning reveals the shared genetic basis of convergent traits," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58428-8
    DOI: 10.1038/s41467-025-58428-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-58428-8
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-58428-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58428-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.