Evidence map›Paper›PMID 39184197›Full record

ArticleApplications in plant sciences

Polyploidy and the evolution of phenotypic integration: Network analysis reveals relationships among anatomy, morphology, and physiology.

Robert L Baker, Grace L Brock, Eastyn L Newsome, Meixia Zhao

Abstract read
In one paragraph

Article in Applications in plant sciences. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Robert L BakerInventory and Monitoring Division National Park Service Fort Collins 80525 Colorado USA.ORCID 0000-0001-7591-5035
Grace L BrockDepartment of Biology Miami University Oxford 45056 Ohio USA.
Eastyn L NewsomeDepartment of Botany and Plant Pathology Purdue University West Lafayette 47907 Indiana USA.
Meixia ZhaoDepartment of Microbiology and Cell Science University of Florida Gainesville 32611 Florida USA.ORCID 0000-0001-8812-8217

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Premise: Most traits are polygenic and most genes are pleiotropic, resulting in complex, integrated phenotypes. Polyploidy presents an excellent opportunity to explore the evolution of phenotypic integration as entire genomes are duplicated, allowing for new associations among traits and potentially leading to enhanced or reduced phenotypic integration. Despite the multivariate nature of phenotypic evolution, studies often rely on simplistic bivariate correlations that cannot accurately represent complex phenotypes or data reduction techniques that can obscure specific trait relationships. Methods: We apply network modeling, a common gene co-expression analysis, to the study of phenotypic integration to identify multivariate patterns of phenotypic evolution, including anatomy and morphology (structural) and physiology (functional) traits in response to whole genome duplication in the genus Results: We identify four key structural traits that are overrepresented in the evolution of phenotypic integration. Seeding networks with key traits allowed us to identify structure-function relationships not apparent from bivariate analyses. In general, allopolyploids exhibited larger, more robust networks indicative of increased phenotypic integration compared to diploids. Discussion: Phenotypic network analysis may provide important insights into the effects of selection on non-target traits, even when they lack direct correlations with the target traits. Network analysis may allow for more nuanced predictions of both natural and artificial selection.

Indexed as

Brassicaevolutionleavesnetwork analysisphenotypic integrationpolyploidywhole genome duplication

Identifiers

PMID39184197
PMCPMC11342231

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Registered trials

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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.