Evidence map›Paper›PMID 35551249›Full record

ArticleNature ecology & evolution2022

Detecting signatures of selection on gene expression.

Peter D Price, Daniela H Palmer Droguett, Jessica A Taylor, Dong Won Kim, Elsie S Place, Thea F Rogers, Judith E Mank, Christopher R Cooney, Alison E Wright

Abstract read
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In one paragraph

Article in Nature ecology & evolution, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers.

0numbers the graph read from it
0cells of the map it votes in
57citing 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

57 citing papers in PubMed.

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  7. The molecular evolution of vertebrate organs.Nature ecology & evolution · 2026
    Review
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  19. Dichotomous transcriptome divergence at the cell-type level between thebioRxiv : the preprint server for biology · 2025
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Peter D PriceEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK. pprice3@sheffield.ac.uk.ORCID http://orcid.org/0000-0002-6118-1111
Daniela H Palmer DroguettEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK.
Jessica A TaylorEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK.
Dong Won KimSolomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.ORCID http://orcid.org/0000-0001-8624-0168
Elsie S PlaceDevelopment, Regeneration and Neurophysiology, School of Biosciences, University of Sheffield, Sheffield, UK.
Thea F RogersEcology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK.
Judith E MankDepartment of Zoology, University of British Columbia, Vancouver, British Columbia, Canada.
Christopher R Cooney *Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK.ORCID http://orcid.org/0000-0002-4872-9146
Alison E Wright *Ecology and Evolutionary Biology, School of Biosciences, University of Sheffield, Sheffield, UK. a.e.wright@sheffield.ac.uk.ORCID http://orcid.org/0000-0003-2479-5250

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A substantial amount of phenotypic diversity results from changes in gene expression levels and patterns. Understanding how the transcriptome evolves is therefore a key priority in identifying mechanisms of adaptive change. However, in contrast to powerful models of sequence evolution, we lack a consensus model of gene expression evolution. Furthermore, recent work has shown that many of the comparative approaches used to study gene expression are subject to biases that can lead to false signatures of selection. Here we first outline the main approaches for describing expression evolution and their inherent biases. Next, we bridge the gap between the fields of phylogenetic comparative methods and transcriptomics to reinforce the main pitfalls of inferring selection on expression patterns and use simulation studies to show that shifts in tissue composition can heavily bias inferences of selection. We close by highlighting the multi-dimensional nature of transcriptional variation and identifying major unanswered questions in disentangling how selection acts on the transcriptome.

Indexed as

TranscriptomePhylogeny

Identifiers

PMID35551249

What OpenQuestion holds

Textmetadata
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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.