Evidence map›Paper›PMID 25691979›Full record

ArticleEcology and evolution2015

Quantitative genetics of learning ability and resistance to stress in Drosophila melanogaster.

Virginie Nepoux, Aurélie Babin, Christoph Haag, Tadeusz J Kawecki, Arnaud Le Rouzic

Abstract read
In one paragraph

Article in Ecology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Role ofGenetics · 2018
    Article
  3. Article
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

5 authors.

Virginie NepouxDepartment of Ecology and Evolution, University of Lausanne Lausanne, CH-1015, Switzerland.
Aurélie BabinDepartment of Ecology and Evolution, University of Lausanne Lausanne, CH-1015, Switzerland.
Christoph HaagCentre d'Écologie Fonctionnelle et Évolutive, UMR 5175, CNRS - Université de Montpellier - Université Paul-Valéry Montpellier - EPHA Montpellier 5, FR-34293, France.
Tadeusz J KaweckiDepartment of Ecology and Evolution, University of Lausanne Lausanne, CH-1015, Switzerland.
Arnaud Le RouzicLaboratoire Evolution Génome et Spéciation, UPR 9034, CNRS Gif-sur-Yvette, FR-91198, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Even though laboratory evolution experiments have demonstrated genetic variation for learning ability, we know little about the underlying genetic architecture and genetic relationships with other ecologically relevant traits. With a full diallel cross among twelve inbred lines of Drosophila melanogaster originating from a natural population (0.75 < F < 0.93), we investigated the genetic architecture of olfactory learning ability and compared it to that for another behavioral trait (unconditional preference for odors), as well as three traits quantifying the ability to deal with environmental challenges: egg-to-adult survival and developmental rate on a low-quality food, and resistance to a bacterial pathogen. Substantial additive genetic variation was detected for each trait, highlighting their potential to evolve. Genetic effects contributed more than nongenetic parental effects to variation in traits measured at the adult stage: learning, odorant perception, and resistance to infection. In contrast, the two traits quantifying larval tolerance to low-quality food were more strongly affected by parental effects. We found no evidence for genetic correlations between traits, suggesting that these traits could evolve at least to some degree independently of one another. Finally, inbreeding adversely affected all traits.

Indexed as

Additive genetic variationdiallel crossingDrosophila melanogastergenetic correlationlearning

Identifiers

PMID25691979
PMCPMC4328760

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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.