ArticleMolecular genetics and genomics : MGG2026
Multi-model GWAS reveals genetic determinants of grain hardness, size and weight in bread wheat (Triticum aestivum L.).
Article in Molecular genetics and genomics : MGG, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Grain quality traits such as kernel hardness index (KHI), kernel diameter (KD), and thousand grain weight (TGW) are pivotal determinants of processing quality and yield potential in bread wheat (Triticum aestivum L.). In this study, we performed a comprehensive genome-wide association study (GWAS) using eight advanced multi-locus models implemented in GAPIT v3.0 to dissect the genetic architecture underlying these traits across 225 spring wheat genotypes evaluated under two distinct environments. A total of 739 significant quantitative trait nucleotides (QTNs) were identified across all datasets using eight different models, with the most QTNs detected by the BLINK and FarmCPU models. Thirty-six common QTNs were detected by all models across traits, including stable QTNs for KHI, KD, and TGW, with several showing pleiotropic effects. Notably, 13 QTNs for KHI were consistently detected across environments, underscoring their potential for marker-assisted selection (MAS). Comparative analysis revealed that 8 QTNs overlapped with previously reported QTLs, particularly for KHI and TGW, thereby validating their reliability. In contrast, no overlaps were observed for KD, suggesting novel loci for this trait. Five highly promising QTNs were prioritized based on stability, multi-trait associations, and detection consistency. Candidate gene analysis revealed 1,916 genes associated with KHI, KD, and TGW, with functional annotation indicating enrichment of domains related to lipid metabolism (GDSL esterases), signalling (protein kinases), assimilate transport (CRAL-TRIO and sucrose transporters), and cell-wall modification (expansins). Differential in-silico expression across grain-related tissues supported their functional relevance. Analysis of 36 common MTAs refined a subset of 95 candidate genes representing key regulatory pathways underlying grain quality traits. The stable QTNs and biologically relevant candidate genes identified in this study provide valuable resources for fine-mapping, MAS, and functional validation, which may support the development of high-yielding wheat cultivars with improved grain quality.
Indexed as
Identifiers
41653313What OpenQuestion holds
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.