1 Introduction

Wheat stands as one of the world’s most vital staple crops, providing a significant portion of daily calories and protein for the global population and underpinning food security worldwide. However, the increasing demand for wheat, driven by population growth and changing consumption patterns, is challenged by stagnating yield gains, climate change, and the emergence of new biotic and abiotic stresses. Conventional breeding has historically contributed to yield improvements and the development of stress-resistant varieties, but its progress is hampered by long breeding cycles, low efficiency in combining multiple desirable traits, and bottlenecks in phenotyping and genetic diversity utilization (Reynolds et al., 2012; Xi et al., 2024).

These limitations highlight the urgent need for biotechnological interventions to accelerate wheat improvement. Modern biotechnological tools-including tissue culture, genetic transformation, molecular breeding, genome editing, and high-throughput phenotyping-offer promising solutions to overcome the constraints of traditional breeding, enabling faster, more precise, and efficient development of high-yielding, resilient wheat varieties. The systematic integration of these technologies, from laboratory-based tissue culture and genetic engineering to field-level applications, has already demonstrated significant potential in enhancing yield, stress tolerance, and nutritional quality (Reynolds et al., 2012; Molero et al., 2018).

This study will provide a comprehensive overview of wheat biotechnology interventions, tracing their progress from basic tissue culture techniques to advanced field applications, including elucidating the main bottlenecks in wheat yield and breeding; critically evaluating the limitations of conventional breeding; the progress of integrated biotechnology approaches, and evaluating their impact and future prospects for sustainable wheat production. This study aims to highlight its importance in addressing global food security and climate adaptation challenges, and provide guidance for future research and policy directions in wheat improvement.

2 Establishment and Optimization of Wheat Tissue Culture Systems

2.1 Selection of explants and responsiveness differences

Immature embryos are the most widely used explants in wheat tissue culture due to their high regeneration potential and responsiveness, making them ideal for genetic transformation and micropropagation. However, their use is limited by seasonal availability and the need for precise developmental staging (Hayta et al., 2021). Mature embryos, while less responsive, offer advantages such as independence from growing season and ease of handling, and recent optimizations have improved their regeneration rates, making them suitable for transformation protocols. Endosperm and shoot tips are less commonly used; endosperm is challenging due to low totipotency, while shoot tips can be more recalcitrant and genotype-dependent, often resulting in lower regeneration frequencies.

Genotype plays a critical role in tissue culture responsiveness, affecting both callus induction and regeneration rates. Significant variation exists among wheat cultivars, with some genotypes consistently exhibiting higher callus formation and regeneration frequencies than others (Hayta et al., 2021). Adaptive strategies to overcome genotypic barriers include optimizing hormone concentrations, media composition, and employing computational models such as artificial neural networks to predict and enhance genotype-independent regeneration protocols (Hesami and Jones, 2020). Screening and selecting genotypes with inherently high regeneration capacity is also a practical approach for developing efficient transformation systems.

2.2 Optimization of key culture conditions and hormone ratios

The success of callus induction and subsequent regeneration is highly dependent on the optimization of culture media, particularly the balance of plant growth regulators and carbon sources. Auxins such as 2,4-D are critical for callus induction, with optimal concentrations varying by genotype and explant type . Cytokinins like kinetin, zeatin, and BAP are essential for shoot regeneration, and their ratios with auxins must be carefully adjusted for each genotype (Hayta et al., 2021). Additionally, the choice and concentration of carbon sources (e.g., sucrose) and the use of respiratory inhibitors can influence callus quality and embryogenic potential, although specific effects may require further empirical optimization. Advanced computational approaches, including artificial intelligence models, are increasingly used to model and optimize these complex interactions for improved tissue culture outcomes (Hesami and Jones, 2020).

Auxins such as 2,4-dichlorophenoxyacetic acid (2,4-D) are essential for inducing callus formation and promoting dedifferentiation in wheat tissue culture. Optimal concentrations of 2,4-D vary by genotype and explant, but typically range from 2 to 6 mg/L for effective callus induction (Örgeç et al., 2020). Cytokinins, including kinetin (KT) and 6-benzylaminopurine (BA/BAP), are critical for redifferentiation and shoot regeneration. The balance between auxin and cytokinin is crucial: higher auxin levels favor callus induction, while increased cytokinin concentrations promote shoot formation and plant regeneration. For example, regeneration media supplemented with 0.5 mg/L IAA, 0.3 mg/L BAP, and 1.0–1.5 mg/L KT have been shown to maximize regeneration rates in certain wheat genotypes (Malik et al., 2021). Additionally, the use of other growth regulators such as zeatin and thidiazuron (TDZ) can further enhance regeneration efficiency, depending on the specific requirements of the wheat variety and explant type (Örgeç et al., 2020).

2.3 Plant regeneration system development and genetic stability analysis

Regeneration rates in wheat tissue culture are influenced by genotype, explant type, and the precise composition of the culture medium. Optimized protocols using specific combinations of auxins and cytokinins can achieve regeneration frequencies exceeding 60% in responsive genotypes. The addition of micronutrients (e.g., CuSO4, AgNO3, and their nanoparticles) has also been shown to significantly enhance both callus induction and regeneration rates, while reducing necrosis and malformation. Strategies such as inter-culture with high-regeneration genotypes, and supplementation with arabinogalactan proteins or hydrogen peroxide, can further improve regeneration rates and reduce abnormalities. Careful adjustment of hormone ratios and culture conditions is essential to minimize malformation rates and ensure the development of healthy, true-to-type regenerants (Wang et al., 2015; Malik et al., 2021).

Somaclonal variation, including chromosomal instability and genetic changes, is a recognized risk in wheat tissue culture. Studies have shown that tissue culture can exacerbate chromosomal instability, leading to numerical and structural chromosome variations in regenerated plants, though the phenotypic effects are often moderate. The extent of somaclonal variation depends on genotype, duration of culture, and the specific tissue culture protocol used. Regular cytogenetic and molecular analyses are recommended to monitor and minimize unwanted genetic changes, ensuring the genetic fidelity of regenerated plants for breeding and research applications (Abugammie et al., 2024).

3 Wheat Genetic Transformation Techniques and Application Cases

3.1 Transformation methods and principles

Biolistic (gene gun) and Agrobacterium-mediated transformation are the two primary methods for introducing foreign genes into wheat. Biolistic transformation, which uses particle bombardment to deliver DNA into plant cells, is widely used due to its broad applicability across wheat genotypes and its ability to generate stable transgenic lines without the need for callus culture in some protocols (Hamada et al., 2017; Sparks and Doherty, 2020; Wang et al., 2022a). Agrobacterium-mediated transformation, while historically less efficient in wheat compared to other cereals, has seen significant improvements, with recent protocols achieving transformation efficiencies up to 33% in certain cultivars and being successfully applied for gene overexpression, RNAi, and CRISPR-based genome editing. Both methods have been optimized for use with immature embryos, the preferred explant for high transformation efficiency (Hayta et al., 2019; Hayta et al., 2021).

Transformation efficiency in wheat is strongly influenced by genotype dependency. Recent advances include the use of morphogenic regulator genes (such as TaWOX5, Zm-Bbm, and Zm-Wus2) to enhance regeneration and reduce genotype dependency, resulting in transformation efficiencies as high as 75% and enabling transformation across a broader range of wheat varieties (Wang et al., 2022b; Johnson et al., 2023). Additional strategies include optimizing explant selection, pre-treatments (e.g., centrifugation), and media composition, as well as employing protein fusions like GRF4-GIF1 to boost transformation rates in elite cultivars (Hayta et al., 2021; Biswal et al., 2023; Liu et al., 2023). The use of nanoparticles for transient transformation and the development of in planta methods further expand the toolkit for wheat genetic engineering (Hamada et al., 2017; She et al., 2025).

Biswal et al. (2023) systematically evaluated the effects of different vectors on callus induction, bud regeneration, and complete plant formation by comparing the tissue culture responses of different vectors during wheat transformation (Figure 1). The results showed that different vectors had significant differences in tissue development, especially pRGEB32, which was superior to other vectors in induction rate and regeneration efficiency. This achievement provides key tissue culture technology support for plant gene editing and genetic improvement.

3.2 Case studies of trait improvement in transgenic wheat

Transgenic approaches have enabled the direct introduction and editing of resistance genes against major wheat diseases such as rust and powdery mildew. For example, gene editing using CRISPR/Cas9 and other nucleases has been successfully applied to confer resistance to these pathogens in elite wheat cultivars, with high editing efficiency and stable inheritance of resistance traits (Biswal et al., 2023). Biolistic and Agrobacterium-mediated methods have both been used to introduce disease resistance genes, demonstrating the practical utility of these transformation systems (Shrawat and Armstrong, 2018; Biswal et al., 2023).

Transgenic wheat lines have been developed to enhance tolerance to abiotic stresses such as drought and salinity. Genes conferring stress tolerance have been introduced using both biolistic and Agrobacterium-mediated methods, resulting in improved performance under stress conditions in greenhouse and field trials (Wijerathna-Yapa et al., 2022). These advances are critical for adapting wheat to changing climates and ensuring stable yields.

Genetic transformation has also been employed to improve the nutritional quality of wheat, including the biofortification of grains with essential micronutrients like iron and zinc. Transgenic wheat lines with enhanced micronutrient content have been generated, demonstrating the potential of genetic engineering to address nutritional deficiencies in staple crops (Wijerathna-Yapa et al., 2022).

4 Advances in Genetic Transformation Technologies in Wheat

4.1 Comparison of gene delivery techniques

Biolistic (gene gun) and Agrobacterium-mediated transformation remain the primary methods for wheat gene delivery. Biolistic transformation is broadly applicable across genotypes and does not require callus culture, making it suitable for elite cultivars previously considered recalcitrant. Agrobacterium-mediated transformation has seen significant improvements, with optimized protocols achieving efficiencies up to 33% in hexaploid wheat and even higher when combined with morphogenic regulators or protein fusions such as GRF4-GIF1 (Hayta et al., 2019; Hayta et al., 2021; Johnson et al., 2023). In planta biolistic methods further simplify the process by bypassing tissue culture, enabling stable transformation in a wider range of cultivars (Hamada et al., 2017). The use of regeneration-related genes like TaWOX5 and morphogenic regulators (Zm-Bbm, Zm-Wus2) has dramatically increased transformation efficiency and reduced genotype dependency (Wang et al., 2022a; Johnson et al., 2023).

4.2 Applications of gene editing technologies

Gene editing technologies, particularly CRISPR/Cas9, have become powerful tools for precise genome modification in wheat. These systems allow targeted mutagenesis, gene knockouts, and trait improvement directly in elite cultivars (Hayta et al., 2019; Hayta et al., 2021). The efficiency of gene editing is closely tied to the underlying transformation system, with recent protocols enabling high editing rates and successful introduction of disease resistance and other agronomic traits (Biswal et al., 2023). Other gene editing platforms, such as zinc finger nucleases and TALENs, have also been applied, but CRISPR/Cas9 is favored for its specificity and ease of use (Shrawat and Armstrong, 2018; Wang et al., 2019).

4.3 Transformation efficiency and molecular detection technologies

Transformation efficiency in wheat has historically been low and highly genotype-dependent. The introduction of morphogenic genes (e.g., TaWOX5, Zm-Bbm, Zm-Wus2, TaLAX1) and optimized protocols have raised efficiencies to as high as 75% in some cases (Wang et al., 2022a). Key factors influencing efficiency include explant type, pre-treatments, and selection systems (Hayta et al., 2019; Hayta et al., 2021; Johnson et al., 2023). Molecular detection of transgenic events relies on PCR-based genotyping, histochemical staining (e.g., GUS assays), and protein strip assays for selectable markers. Advanced cytogenetic techniques such as FISH and GISH are used to confirm transgene integration and inheritance.

5 Trait Construction and Functional Validation via Biotechnology

5.1 Introduction and validation of stress resistance traits

Biotechnological approaches, including genome-wide association studies (GWAS), QTL mapping, and marker-assisted selection, have enabled the identification and validation of key genomic regions and candidate genes associated with stress resistance traits in wheat. For example, high-resolution GWAS and QTL mapping have uncovered loci linked to traits such as drought tolerance and the functional stay-green phenotype, which prolongs photosynthetic activity under stress and enhances grain filling (Roychowdhury et al., 2024). The development of Kompetitive Allele-Specific PCR (KASP) markers for these loci allows for rapid screening and validation in diverse genetic backgrounds, facilitating the breeding of stress-resilient wheat varieties (Rasheed et al., 2016; Ren et al., 2022).

5.2 Nutritional quality and yield improvement

Meta-analyses and integrative genomic studies have identified consensus QTLs and candidate genes for important quality traits, including grain protein content, glutenin composition, and micronutrient accumulation (Wang et al., 2020). These findings support marker-assisted and genomic selection strategies for improving both nutritional quality and processing attributes. Yield improvement efforts have focused on dissecting the genetic architecture of yield components—such as thousand kernel weight, grain number per spike, and spike architecture—using QTL mapping, GWAS, and functional genomics (Cao et al., 2020; Hu et al., 2020). The integration of high-throughput genotyping platforms, such as KASP assays, accelerates the identification and selection of superior alleles for yield and quality traits (Rasheed et al., 2016; Liu et al., 2020b).

5.3 Phenotypic evaluation and greenhouse trial systems

Advances in high-throughput phenotyping, including 3D imaging and structured light scanning, have enabled precise measurement of complex traits such as grain morphology and canopy architecture, supporting more accurate trait validation and selection (Huang et al., 2022; Roychowdhury et al., 2024). Greenhouse and field trials, combined with genomic prediction models and deep learning approaches, further enhance the evaluation of trait performance under controlled and variable environments, increasing the efficiency of breeding programs (Sandhu et al., 2021; Sandhu et al., 2022; Shahi et al., 2022).

6 From Greenhouse to Field: Evaluation and Promotion of Transgenic Materials

6.1 Multi-environment field trials and agronomic trait assessments

Comprehensive field trials are essential for evaluating the agronomic performance and adaptability of transgenic wheat. For example, the transgenic Roundup Ready wheat event 33391 was tested across 14 locations over multiple years, with assessments of maturity, plant height, disease resistance, 1000-kernel weight, and tillering. Results showed no vegetative or reproductive damage, no yield reduction, and robust herbicide tolerance, indicating strong agronomic performance comparable to non-transgenic controls (Zhou et al., 2003). Similarly, field trials of transgenic wheat lines for disease resistance and quality traits have shown that most agronomic differences are minor and can be addressed through backcrossing and selection, supporting the environmental adaptability of these lines across diverse ecological zones. Drought-tolerant transgenic wheat (e.g., HB4®) has also undergone multi-location field testing, demonstrating improved performance under water-limited conditions and gaining commercial approval in several countries (Khan et al., 2019; Gupta, 2023).

6.2 Epigenetics and multi-generational stability studies

Genetic and epigenetic stability are critical for the long-term deployment of transgenic wheat. Marker-free transgenic wheat lines generated via Agrobacterium-mediated co-transformation have shown stable inheritance of transgenes and consistent agronomic traits over T2 and T3 generations, with only minor variations in spike length and grain number (Liu et al., 2020a). Studies have also identified DNA methylation in promoter regions as a key factor in transgene silencing, highlighting the importance of monitoring epigenetic regulation (e.g., methylation, miRNAs) for trait stability in the field (Wang et al., 2016). These findings underscore the need for multi-generational studies to ensure the durability of introduced traits.

6.3 Commercialization and safety assessment requirements

Commercialization of transgenic wheat requires rigorous food and feed safety evaluations, biosafety classification, and environmental release assessments. Drought-tolerant HB4® wheat, for instance, has been approved for food, feed, and cultivation in multiple countries following comprehensive safety and environmental impact studies (Gupta, 2023). Field trials must also assess non-target impacts, such as gene flow to conventional crops, which has been shown to occur at extremely low frequencies and over short distances, supporting the feasibility of coexistence with non-GM wheat under proper management (Miroshnichenko et al., 2016). Regulatory frameworks, particularly in regions like the EU, mandate detailed risk assessments and multi-layered approval processes to ensure that transgenic crops pose no greater risk than conventional varieties (Gómez-Galera et al., 2012).

7 Challenges and Future Directions

7.1 Limitations in wheat genetic transformation and optimization needs

Wheat remains a recalcitrant species for genetic transformation due to low efficiency and strong genotype dependency. Although advances such as the use of regeneration-related genes like TaWOX5 have significantly improved transformation rates and reduced genotype limitations, many elite cultivars still pose challenges for routine transformation. Further optimization of explant types, tissue culture conditions, and delivery methods is needed to establish robust, broadly applicable systems that can be used across diverse wheat varieties (Liu et al., 2023). Additionally, the limited range of transformable tissues and issues with transgene inheritance and stability continue to hinder progress

7.2 Advances and prospects in genome editing for wheat

The emergence of genome editing technologies, particularly CRISPR/Cas systems, has enabled precise modification of wheat genomes. However, most current approaches rely on transgenic delivery systems, raising regulatory and public acceptance concerns. DNA-free gene editing—using ribonucleoprotein complexes or transient expression—offers a promising path to generate non-transgenic, precisely edited wheat lines, potentially easing regulatory hurdles and improving consumer acceptance (Rafiei et al., 2024). The integration of high-throughput genotyping, rapid generation turnover, and genomics-assisted selection is paving the way for new precision breeding paradigms that combine speed, accuracy, and flexibility (Rasheed et al., 2018; Wang et al., 2019).

7.3 Integration of emerging technologies in wheat improvement

The future of wheat improvement lies in the integration of artificial intelligence (AI), multi-omics data (genomics, transcriptomics, phenomics), and digital agriculture platforms. AI-driven breeding and multi-omics screening can accelerate the identification of key genes and regulatory networks, optimize selection strategies, and predict trait performance under diverse environments. Digital agriculture tools, including high-throughput phenotyping and environmental data integration, will further enhance the efficiency and scalability of wheat breeding programs, supporting the rapid industrialization and deployment of improved varieties (Rasheed et al., 2018; Katamadze et al., 2023; Liu et al., 2023).

8 Concluding Remarks

Wheat biotechnology has undergone a remarkable transformation, evolving from early tissue-level interventions to advanced field validation and commercial applications. This progress has been driven by breakthroughs in genetic transformation, genome editing, and the integration of multi-omics approaches, which have collectively deepened our understanding of wheat biology and enabled the precise improvement of complex traits such as yield, stress resistance, and nutritional quality.

Initial efforts in wheat biotechnology focused on overcoming the challenges of gene delivery and plant regeneration, given wheat’s complex and polyploid genome. The development of high-quality reference genomes and advanced transformation techniques has enabled the transition from laboratory-based interventions to robust field trials and, ultimately, the commercialization of improved wheat varieties. These advances have facilitated the deployment of transgenic and gene-edited wheat lines with enhanced agronomic performance and resilience to environmental stresses.

Despite significant progress, establishing a stable, efficient, and scalable technical pipeline remains a core mission for wheat biotechnology. This involves optimizing transformation systems, ensuring the stable inheritance of introduced traits, and integrating high-throughput genotyping and phenotyping platforms. The adoption of genomics-assisted selection, rapid generation turnover, and advanced analytical methods is accelerating the identification and deployment of beneficial genetic variation, supporting the development of wheat varieties that can meet future production demands.

Looking ahead, the future of wheat biotechnology will depend on multidisciplinary collaboration across genomics, bioinformatics, molecular breeding, and digital agriculture. Integrating pan-omics data, artificial intelligence, and precision breeding technologies will be essential for designing wheat varieties that are not only high-yielding and resilient but also environmentally sustainable. Such collaborative efforts are crucial to address the challenges posed by climate change, resource limitations, and evolving societal needs, ensuring that wheat biotechnology continues to contribute to global food security in a sustainable manner.

Acknowledgments

Thank you to the anonymous peer review for providing targeted revision suggestions for the manuscript.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Abugammie B., Wang R., Hu Y., Pang J., Luan Y., Liu B., Jiang L., and Lv R., 2024, Spontaneous chromosome instability and tissue culture-induced karyotypic alteration in wheat-Thinopyrum intermedium alien addition lines, Planta, 260(1): 17.

https://doi.org/10.1007/s00425-024-04450-x

Biswal A., Hernandez L., Castillo A., Debernardi J., and Dhugga K., 2023, An efficient transformation method for genome editing of elite bread wheat cultivars, Frontiers in Plant Science, 14: 1135047.

https://doi.org/10.3389/fpls.2023.1135047

Cao S., Xu D., Hanif M., Xia X., and He Z., 2020, Genetic architecture underpinning yield component traits in wheat, Theoretical and Applied Genetics, 133: 1811-1823.

https://doi.org/10.1007/s00122-020-03562-8

Gómez-Galera S., Twyman R., Sparrow P., Van Droogenbroeck B., Custers R., Capell T., and Christou P., 2012, Field trials and tribulations—making sense of the regulations for experimental field trials of transgenic crops in Europe, Plant Biotechnology Journal, 10(5): 511-523.

https://doi.org/10.1111/j.1467-7652.2012.00681.x

Gupta P., 2023, Drought-tolerant transgenic wheat HB4®: a hope for the future, Trends in Biotechnology, 42(7): 807-809.

https://doi.org/10.1016/j.tibtech.2023.12.007

Hamada H., Linghu Q., Nagira Y., Miki R., Taoka N., and Imai R., 2017, An in planta biolistic method for stable wheat transformation, Scientific Reports, 7(1): 11443.

https://doi.org/10.1038/s41598-017-11936-0

Hayta S., Smedley M., Demir S., Blundell R., Hinchliffe A., Atkinson N., and Harwood W., 2019, An efficient and reproducible Agrobacterium-mediated transformation method for hexaploid wheat (Triticum aestivum L.), Plant Methods, 15(1): 121.

https://doi.org/10.1186/s13007-019-0503-z

Hayta S., Smedley M., Clarke M., Forner M., and Harwood W., 2021, An efficient Agrobacterium-mediated transformation protocol for hexaploid and tetraploid wheat, Current Protocols, 1(3): e58.

https://doi.org/10.1002/cpz1.58

Hesami M., and Jones A., 2020, Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture, Applied Microbiology and Biotechnology, 104: 9449-9485.

https://doi.org/10.1007/s00253-020-10888-2

Huang C., Qin Z., Hua X., Zhang Z., Xiao W., Liang X., Song P., and Yang W., 2022, An intelligent analysis method for 3D wheat grain and ventral sulcus traits based on structured light imaging, Frontiers in Plant Science, 13: 840908.

https://doi.org/10.3389/fpls.2022.840908

Hu J., Wang X., Zhang G., Jiang P., Chen W., Hao Y., Xu S., Jia J., Kong L., and Wang H., 2020, QTL mapping for yield-related traits in wheat based on four RIL populations, Theoretical and Applied Genetics, 133: 917-933.

https://doi.org/10.1007/s00122-019-03515-w

Johnson K., Chu U., Anthony G., Wu E., Che P., and Jones T., 2023, Rapid and highly efficient morphogenic gene-mediated hexaploid wheat transformation, Frontiers in Plant Science, 14: 1151762.

https://doi.org/10.3389/fpls.2023.1151762

Katamadze A., Vergara-Díaz O., Uberegui E., Yoldi-Achalandabaso A., Araus J., and Vicente R., 2023, Evolution of wheat architecture, physiology, and metabolism during domestication and further cultivation: lessons for crop improvement, The Crop Journal, 11(4): 1080-1096.

https://doi.org/10.1016/j.cj.2023.06.006

Khan S., Anwar S., Yu S., Sun M., Yang Z., and Gao Z., 2019, Development of drought-tolerant transgenic wheat: achievements and limitations, International Journal of Molecular Sciences, 20(13): 3350.

https://doi.org/10.3390/ijms20133350

Liu H., Wang K., Wang J., Du L., Pei X., and Ye X., 2020a, Genetic and agronomic traits stability of marker-free transgenic wheat plants generated from Agrobacterium-mediated co-transformation in T2 and T3 generations, Journal of Integrative Agriculture, 19: 23-32.

https://doi.org/10.1016/S2095-3119(19)62601-8

Liu H., Zhang X., Xu Y., Zhang J., Cao Y., Li L., and An D., 2020b, Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum aestivum L.), BMC Plant Biology, 20(1): 529.

https://doi.org/10.1186/s12870-020-02661-4

Liu X., Bie X., Lin X., Li M., Wang H., Zhang X., Yang Y., Zhang C., Zhang X., and Xiao J., 2023, Uncovering the transcriptional regulatory network involved in boosting wheat regeneration and transformation, Nature Plants, 9(6): 908-925.

https://doi.org/10.1038/s41477-023-01406-z

Malik W., Mahmood I., Razzaq A., Afzal M., Shah G., Iqbal A., Zain M., Ditta A., Asad S., Ahmad I., Mangi N., and Ye W., 2021, Exploring potential of copper and silver nanoparticles to establish efficient callogenesis and regeneration system for wheat (Triticum aestivum L.), GM Crops & Food, 12: 564-585.

https://doi.org/10.1080/21645698.2021.1917975

Miroshnichenko D., Pushin A., and Dolgov S., 2016, Assessment of the pollen-mediated transgene flow from the plants of herbicide-resistant wheat to conventional wheat (Triticum aestivum L.), Euphytica, 209: 71-84.

https://doi.org/10.1007/s10681-016-1637-y

Molero G., Joynson R., Piñera-Chávez F., Gardiner L., Rivera-Amado C., Hall A., and Reynolds M., 2018, Elucidating the genetic basis of biomass accumulation and radiation use efficiency in spring wheat and its role in yield potential, Plant Biotechnology Journal, 17: 1276-1288.

https://doi.org/10.1111/pbi.13052

Örgeç M., Verma S., Sahin G., Zencirci N., and Gürel E., 2020, In vitro tissue culture protocol of ancient einkorn (Triticum monococcum ssp. monococcum) wheat via indirect shoot regeneration, In Vitro Cellular & Developmental Biology – Plant, 57: 143-151.

https://doi.org/10.1007/s11627-020-10122-8

Rafiei F., Wiersma J., Scofield S., Zhang C., Alizadeh H., and Mohammadi M., 2024, Facts, uncertainties, and opportunities in wheat molecular improvement, Heredity, 133: 371-380.

https://doi.org/10.1038/s41437-024-00721-1

Rasheed A., Mujeeb-Kazi A., Ogbonnaya F., He Z., and Rajaram S., 2018, Wheat genetic resources in the post-genomics era: promise and challenges, Annals of Botany, 121: 603-616.

https://doi.org/10.1093/aob/mcx148

Rasheed A., Wen W., Gao F., Zhai S., Jin H., Liu J., Guo Q., Zhang Y., Dreisigacker S., Xia X., and He Z., 2016, Development and validation of KASP assays for genes underpinning key economic traits in bread wheat, Theoretical and Applied Genetics, 129: 1843-1860.

https://doi.org/10.1007/s00122-016-2743-x

Ren T., Fan T., Chen S., Chen Y., Ou X., Jiang Q., Peng W., Ren Z., Tan F., Luo P., and Li Z., 2022, Identification and validation of quantitative trait loci for the functional stay-green trait in common wheat (Triticum aestivum L.) via high-density SNP-based genotyping, Theoretical and Applied Genetics, 135: 1429-1441.

https://doi.org/10.1007/s00122-022-04044-9

Reynolds M., Foulkes J., Furbank R., Griffiths S., King J., Murchie E., Parry M., and Slafer G., 2012, Achieving yield gains in wheat, Plant, Cell & Environment, 35(10): 1799-1823.

https://doi.org/10.1111/j.1365-3040.2012.02588.x

Roychowdhury R., Ghatak A., Kumar M., Samantara K., Weckwerth W., and Chaturvedi P., 2024, Accelerating wheat improvement through trait characterization: advances and perspectives, Physiologia Plantarum, 176(5): e14544.

https://doi.org/10.1111/ppl.14544

Sandhu K., Lozada D., Zhang Z., Pumphrey M., and Carter A., 2021, Deep learning for predicting complex traits in spring wheat breeding program, Frontiers in Plant Science, 11: 613325.

https://doi.org/10.3389/fpls.2020.613325

Sandhu K., Patil S., Aoun M., and Carter A., 2022, Multi-trait multi-environment genomic prediction for end-use quality traits in winter wheat, Frontiers in Genetics, 13: 831020.

https://doi.org/10.3389/fgene.2022.831020

Shahi D., Guo J., Pradhan S., Khan J., Avci M., Khan N., McBreen J., Bai G., Reynolds M., Foulkes J., and Babar M., 2022, Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat, BMC Genomics, 23(1): 298.

https://doi.org/10.1186/s12864-022-08487-8

She L., Cheng X., Jiang P., Shen S., Dai F., Run Y., Zhu M., Tavakoli M., Yang X., Wang X., Xiao J., Chen C., Kang Z., Huang J., and Zhang W., 2025, Modified carbon dot-mediated transient transformation for genomic and epigenomic studies in wheat, Plant Biotechnology Journal, 23: 1139-1152.

https://doi.org/10.1111/pbi.14573

Shrawat A., and Armstrong C., 2018, Development and application of genetic engineering for wheat improvement, Critical Reviews in Plant Sciences, 37: 335-421.

https://doi.org/10.1080/07352689.2018.1514718

Sparks C., and Doherty A., 2020, Genetic transformation of common wheat (Triticum aestivum L.) using biolistics, Methods in Molecular Biology, 2124: 229-250.

https://doi.org/10.1007/978-1-0716-0356-7_12

Wang D., Li F., Cao S., and Zhang K., 2020, Genomic and functional genomics analyses of gluten proteins and prospect for simultaneous improvement of end-use and health-related traits in wheat, Theoretical and Applied Genetics, 133: 1521-1539.

https://doi.org/10.1007/s00122-020-03557-5

Wang K., Gong Q., and Ye X., 2019, Recent developments and applications of genetic transformation and genome editing technologies in wheat, Theoretical and Applied Genetics, 133: 1603-1622.

https://doi.org/10.1007/s00122-019-03464-4

Wang K., Liu H., Du L., and Ye X., 2016, Generation of marker-free transgenic hexaploid wheat via an Agrobacterium-mediated co-transformation strategy in commercial Chinese wheat varieties, Plant Biotechnology Journal, 15: 614-623.

https://doi.org/10.1111/pbi.12660

Wang K., Shi L., Liang X., Zhao P., Wang W., Liu J., Chang Y., Hiei Y., Yanagihara C., Du L., Ishida Y., and Ye X., 2022a, The gene TaWOX5 overcomes genotype dependency in wheat genetic transformation, Nature Plants, 8: 110-117.

https://doi.org/10.1038/s41477-021-01085-8

Wang X., Fan R., Yin G., Wang K., Du L., Xiao L., and Ye X., 2015, Effects of inter-culture, arabinogalactan proteins, and hydrogen peroxide on the plant regeneration of wheat immature embryos, Journal of Integrative Agriculture, 14: 11-19.

https://doi.org/10.1016/S2095-3119(14)60764-4

Wang Y., Zeng J., Su P., Zhao H., Li L., Xie X., Zhang Q., Wu Y., Wang R., Zhang Y., Yu B., Chen M., Wang Y., Yang G., He G., Chang J., and Li Y., 2022b, An established protocol for generating transgenic wheat for wheat functional genomics via particle bombardment, Frontiers in Plant Science, 13: 979540.

https://doi.org/10.3389/fpls.2022.979540

Wijerathna-Yapa A., Ramtekey V., Ranawaka B., and Basnet B., 2022, Applications of in vitro tissue culture technologies in breeding and genetic improvement of wheat, Plants, 11(17): 2273.

https://doi.org/10.3390/plants11172273

Xi Y., Du Y., Wang D., Ren J., Luo W., Peng Q., Fang W., and Li F., 2024, Wheat genetic progress in biomass allocation and yield components: a global perspective, Field Crops Research, 318: 109617.

https://doi.org/10.1016/j.fcr.2024.109617

Zhou H., Berg J., Blank S., Chay C., Chen G., Eskelsen S., Fry J., Sio-Wai H., Hu T., Isakson P., Lawton M., Metz S., Rempel C., Ryerson D., Sansone A., Shook A., Starke R., Tichota J., and Valenti S., 2003, Field efficacy assessment of transgenic Roundup Ready wheat, Crop Science, 43: 1072-1075.

https://doi.org/10.2135/cropsci2003.1072