Deciphering the genetic basis of wheat germination under ZnO nano priming and drought stress through integrated QTL mapping and network analyses

Abstract

Abstract Background Wheat (Triticum aestivum L.) is one of the most important staple crops worldwide. However, its productivity, grain quality, and nutritional value are increasingly threatened by climate change, particularly drought stress. Rapid global population growth, coupled with these climatic challenges, is expected to intensify malnutrition and food insecurity, thereby increasing the risk of famine in vulnerable regions. Nanotechnology‑based approaches, especially zinc oxide nanoparticle (ZnO-NPs) seed nano priming, have emerged as promising strategies to enhance seed germination, early seedling growth, and drought tolerance in wheat. Results This study phenotypically evaluated 113 doubled haploid (DH) wheat genotypes, along with two parental lines, for 22 germination‑ and seedling‑related traits under optimal (C; 0% PEG 6000) and drought stress (D; 18% PEG 6000) conditions, with and without ZnO nanoparticles (ZnO-NPs) priming. Subsequently, to identify genomic regions and candidate genes associated with drought‑responsive phenotypes, quantitative trait loci (QTL) mapping was performed using Inclusive Composite Interval Mapping (ICIM‑ADD) on a subset of 98 DH genotypes, following exclusion of 15 lines due to low quality marker data. All measured traits were significantly affected by drought stress; however, under both control and drought conditions, ZnO-NP nano priming consistently improved seed germination and seedling establishment. Overall, 51 QTLs associated with 22 traits were detected across the four treatments, and 86 candidate genes were located within the confidence intervals of the identified QTLs. Functional annotation indicated that these genes encode metal-binding proteins, transcription factors, ion transporters, enzymes, and zinc‑binding proteins involved in stress tolerance. Notably, both primed and unprimed conditions revealed stable and reliable QTLs associated with drought tolerance. Functional connectivity, key hub genes, and protein clusters were further revealed through network analysis. Conclusion More effective wheat breeding programs can be achieved through the integration of molecular breeding approaches with nanotechnology. Furthermore, the incorporation of gene–gene, gene–protein, and protein–protein interaction network analyses provide deeper insights into the regulatory modules and functional connectivity underlying drought tolerance. These network‑based approaches facilitate the identification of key hub genes and protein clusters that coordinate stress signaling and metabolic pathways, thereby offering powerful and promising targets for molecular breeding and crop improvement strategies.