Evidence map›Paper›PMID 33144977›Full record

ArticleEcology and evolution2020

Unidirectional response to bidirectional selection on body size II. Quantitative genetics.

Arnaud Le Rouzic, Clémentine Renneville, Alexis Millot, Simon Agostini, David Carmignac, Éric Édeline

Abstract read
In one paragraph

Article in Ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. A method to predict the response to directional selection using a Kalman filter.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
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  5. 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

6 authors.

Arnaud Le RouzicLaboratoire Évolution, Génomes, Comportement, Écologie CNRS, IRD, Université Paris-Saclay Gif-sur-Yvette France.ORCID https://orcid.org/0000-0002-2158-3458
Clémentine RennevilleInstitut d'Écologie et des Sciences de l'Environnement de Paris (iEES-Paris) Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRD Paris France.
Alexis MillotCentre de Recherche en Écologie Expérimentale et Prédictive (CEREEP-Ecotron Ile-de-France), UMS 3194 École normale supérieure, PSL Research University, CNRS Saint-Pierre-lès-Nemours France.
Simon AgostiniCentre de Recherche en Écologie Expérimentale et Prédictive (CEREEP-Ecotron Ile-de-France), UMS 3194 École normale supérieure, PSL Research University, CNRS Saint-Pierre-lès-Nemours France.
David CarmignacInstitut d'Écologie et des Sciences de l'Environnement de Paris (iEES-Paris) Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRD Paris France.
Éric ÉdelineInstitut d'Écologie et des Sciences de l'Environnement de Paris (iEES-Paris) Sorbonne Université, Université Paris Diderot, UPEC, CNRS, INRAE, IRD Paris France.ORCID https://orcid.org/0000-0003-4963-4453

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Anticipating the genetic and phenotypic changes induced by natural or artificial selection requires reliable estimates of trait evolvabilities (genetic variances and covariances). However, whether or not multivariate quantitative genetics models are able to predict precisely the evolution of traits of interest, especially fitness-related, life history traits, remains an open empirical question. Here, we assessed to what extent the response to bivariate artificial selection on both body size and maturity in the medaka

Indexed as

animal modelartificial selectionasymmetric responseBayesian mixed modelsbivariate selectionevolvabilityG matrix

Identifiers

PMID33144977
PMCPMC7593195

What OpenQuestion holds

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Registered trials

None linked

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.