ReviewFunctional & integrative genomics2024
GWAS for identification of genomic regions and candidate genes in vegetable crops.
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.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed.
- Integrating genome-wide association study and transcriptomic profiling identifies candidate genes underlying soluble solids content in melon (Horticulture research · 2026Article
- Genome-Wide Association Study and Candidate Gene Mining for Plant Height and Main Stem Node Number in Soybean from Northwest China.Plants (Basel, Switzerland) · 2026Article
- Review
- GWAS and genomic prediction for resistance to soybean mosaic virus strain SC7.BMC plant biology · 2026Article
- Loci and natural alleles for leaf length and width revealed by a genome wide association study in adzuki bean.BMC plant biology · 2025Article
- Unraveling the variation, phylogeny, and taxonomy of Lactuca spp. for germplasm management and breeding.Planta · 2025Review
- The Barley Glycosyltransferase GenePlants (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Identifiers
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Registered trials
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.