Evidence map›Paper›PMID 40965825›Full record

ReviewJournal of applied genetics2026

Genome-wide association study bridging genomics-phenomics gap in natural plant populations.

Sarbani Roy, Hari Shankar Gadri, Vikas Sharma, Md Asif Chowdhary, Rohini Dwivedi, Pankaj Bhardwaj

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of applied genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Sarbani RoyMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India.ORCID http://orcid.org/0000-0001-9861-0947
Hari Shankar GadriMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India.ORCID http://orcid.org/0000-0002-1447-9804
Vikas SharmaMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India.ORCID http://orcid.org/0000-0001-8775-9420
Md Asif ChowdharyMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India.ORCID http://orcid.org/0000-0003-1583-3235
Rohini DwivediMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India.ORCID http://orcid.org/0000-0001-6330-3664
Pankaj BhardwajMolecular Genetics Lab, Department of Botany, Central University of Punjab, Bathinda, India. pankajihbt@gmail.com.ORCID http://orcid.org/0000-0002-9201-1609

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The planet hosts half a million plant species exhibiting a spectacular diversity of plant forms with genomes driving phenotypic variations. The genome information exists for less than 1% of species, limiting quantitative genomic studies in natural populations. This review explores how recent advances in cutting-edge genomic and phenomic techniques extended genome-wide association studies (GWAS) to wild, non-model species and other natural populations. We also discuss the incorporation of diverse bioinformatic tools into comprehensive in-silico pipelines and recommend implementing machine learning algorithms to address methodological challenges. The critical literature synthesis highlights several scopes of GWAS, bringing natural populations into the spotlight of genomic research. Thus, the study presents GWAS as a cornerstone for advancing quantitative genomics in natural populations. This shift holds great promise for understanding adaptation, trait evolution, and conservation genetics across diverse plant germplasm.

Indexed as

Genome, PlantGenome-Wide Association StudyGenomicsPhenomicsPlantsComputational BiologyGenetics, PopulationPhenotypeQuantitative Trait LociAssociation mappingGWASMachine learningNatural populationQuantitative genomics

Identifiers

PMID40965825

What OpenQuestion holds

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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.