Evidence map›Paper›PMID 19916003›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2010

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis.

Julie B Fiévet, Christine Dillmann, Dominique de Vienne

Abstract read
In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Heterosis Breeding in Eggplant (Plants (Basel, Switzerland) · 2020
    Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Omics-based hybrid prediction in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017
    Article
  17. Article
  18. Article
  19. Only one health, and so many omics.Cancer cell international · 2015
    Article
  20. Review
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

3 authors.

Julie B FiévetAgroParisTech, UMR 0320/UMR 8120 Génétique Végétale, 91190 Gif-sur-Yvette, France.
Christine Dillmann
Dominique de Vienne

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype-phenotype relationship. From the generalization of Kacser and Burns' biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we "crossed" virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype-phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis.

Indexed as

Epistasis, GeneticModels, GeneticComputer SimulationEnzymesGenotypeGlycolysisHybrid VigorKineticsEnzymes

Identifiers

PMID19916003
PMCPMC2793392

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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.