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<p>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 (<i>Triticum aestivum</i> 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.</p>

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Multi-model GWAS reveals genetic determinants of grain hardness, size and weight in bread wheat (Triticum aestivum L.)

  • 期刊论文

  • 发表日期：

    2026年2月7日

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  • Deepa Bhadana,
  • Jitendra Kumar,
  • Shoeb Ahmed,
  • Anita Kumari,
  • Monika Garg,
  • Joy Roy,
  • Rahul Kumar

摘要

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.