Evidence map›Paper›PMID 39470821›Full record

ReviewFunctional & integrative genomics2024

GWAS for identification of genomic regions and candidate genes in vegetable crops.

Swagata Nandi, Kishor Varotariya, Sohamkumar Luhana, Amitkumar D Kyada, Ankita Saha, Nabanita Roy, Neha Sharma, Dharavath Rambabu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Functional & integrative genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Review
  7. The Barley Glycosyltransferase GenePlants (Basel, Switzerland) · 2025
    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

8 authors.

Swagata Nandi *Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Kishor Varotariya *Division of Vegetable Science, ICAR-Indian Institute of Horticultural Research, Bengaluru, 560089, India. kishorvarotariya1234@gmail.com.
Sohamkumar LuhanaDivision of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Amitkumar D KyadaDivision of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Ankita SahaDivision of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Nabanita RoyDivision of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Neha SharmaDivision of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Dharavath RambabuDivision of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.

Indexed as

Crops, AgriculturalGenome-Wide Association StudyLinkage DisequilibriumPolymorphism, Single NucleotideGenome, PlantPhenotypeQuantitative Trait LociVegetablesGenetic linkage mappingGenome-Wide Association Studies (GWAS)Single nucleotide polymorphisms (SNPs)Trait identificationVegetable crops

Identifiers

PMID39470821

What Socratic 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 Socratic graph.