Evidence map›Paper›PMID 31595338›Full record

ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2020

Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs.

Antoine Allier, Simon Teyssèdre, Christina Lehermeier, Alain Charcosset, Laurence Moreau

Abstract read
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In one paragraph

Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Restructuring breeding programs 1: Integration of diversity.Genetics, selection, evolution : GSE · 2026
    Article
  2. Article
  3. Practical Considerations When Using Mendelian Sampling Variances for Selection Decisions in Genomic Selection Programs.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025
    Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Improving the use of plant genetic resources to sustain breeding programs' efficiency.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Genetic and molecular control of grain yield in maize.Molecular breeding : new strategies in plant improvement · 2021
    Article
  15. Genomic prediction using training population design in interspecific soybean populations.Molecular breeding : new strategies in plant improvement · 2021
    Article
  16. Review
  17. Article
  18. 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.

Antoine AllierGQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.ORCID http://orcid.org/0000-0001-6578-1715
Simon TeyssèdreRAGT2n, Genetics and Analytics Unit, 12510, Druelle, France.
Christina LehermeierRAGT2n, Genetics and Analytics Unit, 12510, Druelle, France.ORCID http://orcid.org/0000-0001-7724-0887
Alain CharcossetGQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.ORCID http://orcid.org/0000-0001-6125-503X
Laurence MoreauGQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France. laurence.moreau@inra.fr.ORCID http://orcid.org/0000-0002-7195-1327

Funding

ANRT CIFRE 2016/1281
6 · The paper itself

Abstract

key messageCollaborative diversity panels and genomic prediction seem relevant to identify and harness genetic resources for polygenic trait-specific enrichment of elite germplasms. In plant breeding, genetic diversity is important to maintain the pace of genetic gain and the ability to respond to new challenges in a context of climatic and social expectation changes. Many genetic resources are accessible to breeders but cannot all be considered for broadening the genetic diversity of elite germplasm. This study presents the use of genomic predictions trained on a collaborative diversity panel, which assembles genetic resources and elite lines, to identify resources to enrich an elite germplasm. A maize collaborative panel (386 lines) was considered to estimate genome-wide marker effects. Relevant predictive abilities (0.40-0.55) were observed on a large population of private elite materials, which supported the interest of such a collaborative panel for diversity management perspectives. Grain-yield estimated marker effects were used to select a donor that best complements an elite recipient at individual loci or haplotype segments, or that is expected to give the best-performing progeny with the elite. Among existing and new criteria that were compared, some gave more weight to the donor-elite complementarity than to the donor value, and appeared more adapted to long-term objective. We extended this approach to the selection of a set of donors complementing an elite population. We defined a crossing plan between identified donors and elite recipients. Our results illustrated how collaborative projects based on diversity panels including both public resources and elite germplasm can contribute to a better characterization of genetic resources in view of their use to enrich elite germplasm.

Indexed as

Cooperative BehaviorGenomicsPlant BreedingGenotypeHaploidyModels, GeneticQuantitative Trait LociZea mays

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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.