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DOI 10.1186/s12870-026-09622-3

Медицинская библиотека · BMC plant biology · 2026

Deciphering the dynamic genetic architecture of adult-plant salinity tolerance in bread wheat using multi-model GWAS and genomic prediction.

Sadaat Sayed Mujtaba, Bihamta Mohammadreza, Mohammadi Valiollah, Mahlooji Mehrdad

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Аннотация

BACKGROUND

Soil salinity is a major constraint to wheat productivity, necessitating the identification of robust genetic loci for breeding. This study aims to dissect the genetic architecture of adult-plant salinity tolerance in a diverse panel of 298 Iranian wheat accessions. A multi-model GWAS framework (MLM, FarmCPU, and BLINK) was deployed under moderate (6 dS/m) and severe (12 dS/m) salinity stress. High-confidence consensus marker-trait associations (MTAs) were validated through comparative synteny analysis with the rice genome. Furthermore, the genomic predictive ability of gBLUP was evaluated across stress regimes.

RESULTS

We identified 45 consensus MTAs, revealing a dynamic "Genetic Handoff": constitutive growth-promoting loci predominated under moderate stress, while adaptive ion-homeostasis and "escape" loci emerged under severe stress. A major biomass-associated locus on chromosome 5B (TraesCS5B02G000100) was identified as a strict 1:1 ortholog to rice Os12g0641400 (83.53% identity), indicating high evolutionary conservation of sucrose partitioning. Under severe stress, genomic prediction accuracies for K+/Na+ ratio surged to 0.65, although yield prediction declined due to non-additive effects.

CONCLUSION

Our findings advocate for a Haplotype-Informed Genomic Selection strategy, combining MAS for conserved high-effect loci like TaSUC4 with GS for polygenic backgrounds. This integrated approach provides a robust roadmap for developing salinity-resilient wheat cultivars.

Для цитирования: Sadaat Sayed Mujtaba, Bihamta Mohammadreza, Mohammadi Valiollah, Mahlooji Mehrdad. Deciphering the dynamic genetic architecture of adult-plant salinity tolerance in bread wheat using multi-model GWAS and genomic prediction.. BMC plant biology. 2026. DOI: 10.1186/s12870-026-09622-3.

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