Evidence map›Paper›PMID 39829798›Full record

ArticlebioRxiv : the preprint server for biology2025

Evolutionary sparse learning with paired species contrast reveals the shared genetic basis of convergent traits.

John B Allard, Sudip Sharma, Ravi Patel, Maxwell Sanderford, Koichiro Tamura, Slobodan Vucetic, Glenn S Gerhard, Sudhir Kumar

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

John B AllardInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.
Sudip SharmaInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.
Ravi PatelInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.
Maxwell SanderfordInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.
Koichiro TamuraDepartment of Biological Sciences, Tokyo Metropolitan University, Tokyo, Japan.
Slobodan VuceticDepartment of Computer and Information Sciences, Temple University, Philadelphia PA, United States of America.
Glenn S GerhardLewis Katz School of Medicine at Temple University, Philadelphia, PA, 19140, USA.
Sudhir KumarInstitute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA 19122, USA.ORCID 0000-0002-9918-8212

Funding

Methods For Evolutionary Genomics AnalysisR35GM139540 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI Sudhir Kumar · 2021 to 2026
$2.9M
NIGMS NIH HHS R35 GM139540
6 · The paper itself

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 a novel comparative genomics approach to build genetic models that underlie the independent origins of convergent traits using evolutionary sparse learning. We test 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. These results support the involvement of sequence substitutions in many common genetic loci in the evolution of convergent traits studied. Genes contributing to genetic models for echolocation were highly enriched for functional categories related to hearing, sound perception, and deafness (

Identifiers

PMID39829798
PMCPMC11741315

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.