Evidence map›Paper›PMID 28083016›Full record

ReviewFrontiers in genetics2016

Genomic Selection in the

Javaid A Bhat, Sajad Ali, Romesh K Salgotra, Zahoor A Mir, Sutapa Dutta, Vasudha Jadon, Anshika Tyagi, Muntazir Mushtaq, Neelu Jain, Pradeep K Singh and 2 more

Abstract readReview
In one paragraph

Review in Frontiers in genetics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 159 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
159citing papers in PubMed, 1 pooled it
–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

159 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Genetic Advances inPlants (Basel, Switzerland) · 2026
    Review
  8. Article
  9. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
    Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Uncovering natural variation inFrontiers in plant science · 2026
    Article
  17. Review
  18. Article
  19. Article
  20. Review

99 more citing papers are in PubMed but not listed here.

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

12 authors.

Javaid A BhatDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
Sajad AliNational Research Centre for Plant Biotechnology New Delhi, India.
Romesh K SalgotraSchool of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu Chatha, India.
Zahoor A MirNational Research Centre for Plant Biotechnology New Delhi, India.
Sutapa DuttaDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
Vasudha JadonDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
Anshika TyagiNational Research Centre for Plant Biotechnology New Delhi, India.
Muntazir MushtaqSchool of Biotechnology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu Chatha, India.
Neelu JainDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
Pradeep K SinghDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
Gyanendra P SinghDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.
K V PrabhuDivision of Genetics, Indian Agricultural Research Institute New Delhi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genomic selection (GS) is a promising approach exploiting molecular genetic markers to design novel breeding programs and to develop new markers-based models for genetic evaluation. In plant breeding, it provides opportunities to increase genetic gain of complex traits per unit time and cost. The cost-benefit balance was an important consideration for GS to work in crop plants. Availability of genome-wide high-throughput, cost-effective and flexible markers, having low ascertainment bias, suitable for large population size as well for both model and non-model crop species with or without the reference genome sequence was the most important factor for its successful and effective implementation in crop species. These factors were the major limitations to earlier marker systems viz., SSR and array-based, and was unimaginable before the availability of next-generation sequencing (NGS) technologies which have provided novel SNP genotyping platforms especially the genotyping by sequencing. These marker technologies have changed the entire scenario of marker applications and made the use of GS a routine work for crop improvement in both model and non-model crop species. The NGS-based genotyping have increased genomic-estimated breeding value prediction accuracies over other established marker platform in cereals and other crop species, and made the dream of GS true in crop breeding. But to harness the true benefits from GS, these marker technologies will be combined with high-throughput phenotyping for achieving the valuable genetic gain from complex traits. Moreover, the continuous decline in sequencing cost will make the WGS feasible and cost effective for GS in near future. Till that time matures the targeted sequencing seems to be more cost-effective option for large scale marker discovery and GS, particularly in case of large and un-decoded genomes.

Indexed as

complex traitscrop improvementGBSGEBVsgenomic selection

Identifiers

PMID28083016
PMCPMC5186759

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.