Evidence map›Paper›PMID 36149531›Full record

ReviewPlanta2022

Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review.

C Anilkumar, N C Sunitha, Harikrishna, Narayana Bhat Devate, S Ramesh

Abstract readReview
PubMed Publisher
In one paragraph

Review in Planta, 2022. 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. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Harnessing neo-domestication of wild pigmented rice for enhanced nutrition and sustainable agriculture.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Designing future peanut: the power of genomics-assisted breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024
    Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. 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

5 authors.

C AnilkumarICAR-National Rice Research Institute, Cuttack, India.ORCID https://orcid.org/0000-0002-7359-1249
N C SunithaUniversity of Agricultural Sciences, Bangalore, India.ORCID https://orcid.org/0000-0001-9387-4695
HarikrishnaICAR-Indian Agriculture Research Institute, New Delhi, India.ORCID http://orcid.org/0000-0002-7563-8362
Narayana Bhat DevateICAR-Indian Agriculture Research Institute, New Delhi, India.ORCID http://orcid.org/0000-0003-4665-8118
S RameshUniversity of Agricultural Sciences, Bangalore, India. ramesh_uasb@rediffmail.com.ORCID http://orcid.org/0000-0001-9666-7974

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MAIN

conclusionGenomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.

Indexed as

Plant BreedingSelection, GeneticGenome, PlantGenomicsPhenotypeAccelerated crop improvementCross-validationGenetic gainIntegrated GSSOP for GS

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.