Advances in Cereal Genomics and Applications in Plant Breeding

Introduction
Cereal crops such as rice (Oryza sativa), wheat (Triticum aestivum), maize (Zea mays), and sorghum (Sorghum bicolor) are essential to global food security, supplying most of the dietary calories and nutrients consumed worldwide [1]. They support both human consumption and livestock feed and play significant roles in industries like biofuel and food processing [2]. In addition to cereals, other crops also contribute greatly to global agriculture. Legumes such as soybean, lentil, and chickpea provide protein-rich food and improve soil fertility through nitrogen fixation. Root and tuber crops, including potato, cassava, and sweet potato, are staple foods in many regions, especially in the tropics. Oilseeds like sunflower, rapeseed, and groundnut supply essential oils used in food and industrial products. Together, these crops support nutrition, sustainability, and economic stability across diverse ecosystems.

However, the sustainability of cereal production is increasingly being challenged by multiple environmental and socioeconomic factors [3]. Rapid population growth continues to drive an ever-increasing demand for food, while climate change exacerbates the frequency and severity of extreme weather events such as droughts, floods, and heatwaves [4]. Rising global temperatures pose a serious threat to cereal crop yields, as many staple crops have specific temperature thresholds beyond which productivity declines significantly. Additionally, soil degradation, declining arable land, and emerging pest and disease outbreaks further threaten the stability of global cereal production [5]. Soil erosion, nutrient depletion, and salinity accumulation, exacerbated by unsustainable farming practices, have led to decreasing yields in major cereal-growing regions emergence of new pests and pathogens, such as wheat rust fungi (Puccinia spp.) and maize lethal necrosis, has further compounded these issues, leading to increased reliance on chemical pesticides and fungicides [6]. However, the overuse of these agrochemicals has led to resistance among pests and pathogens, necessitating alternative and more sustainable management strategies [7]. Addressing these challenges is crucial to ensuring food security and sustaining agricultural productivity for future generations [8].

Climate change has introduced significant uncertainties in global agricultural systems, with increasing temperatures, erratic rainfall patterns, and rising incidences of extreme weather events posing a direct threat to cereal crop productivity [9]. Prolonged droughts, heat stress, and soil salinity have negatively impacted crop yields in many regions [10]. For example, wheat production is highly sensitive to high temperatures, with studies indicating that every 1°C increase in temperature can lead to a significant decline in yield [11]. Similarly, rice cultivation, which depends on a stable water supply, is severely affected by droughts and water scarcity, leading to production losses in key growing regions [12].

The emergence and rapid spread of new pests and diseases pose a major threat to global cereal production, affecting a wide range of crops beyond wheat, maize, and rice. In barley, Fusarium head blight (caused by Fusarium graminearum) reduces yields and contaminates grain with harmful mycotoxins, while barley yellow dwarf virus (BYDV) stunts growth and compromises grain quality [13]. Sorghum faces severe damage from invasive pests like the fall armyworm (Spodoptera frugiperda) and fungal diseases such as anthracnose (Colletotrichum sublineolum), which devastate leaves and stems [14]. Oats are highly susceptible to crown rust (Puccinia coronata), which impairs photosynthesis, and the emerging oat blast disease (Magnaporthe oryzae), which can lead to complete crop failure [15]. Millets, particularly pearl millet, suffer from downy mildew (Sclerospora graminicola) and infestations of the millet head miner (Heliocheilus albipunctella), which directly attack grain panicles [16]. The overuse of chemical pesticides has accelerated resistance in many pathogens and pests, making integrated pest management (IPM), disease-resistant crop varieties, and sustainable farming practices essential for mitigating losses [17]. Without proactive strategies, these challenges will continue to jeopardize food security, particularly in regions reliant on staple cereal crops. Soil degradation and nutrient depletion have also emerged as critical issues affecting cereal productivity [18]. Intensive farming practices, continuous monocropping, and excessive use of chemical fertilizers have contributed to soil fertility decline, affecting plant growth and yield potential [19]. In response, researchers have been exploring ways to develop cereal varieties that can efficiently use nutrients, such as nitrogen and phosphorus, while minimizing environmental impacts [20].

To mitigate these challenges and enhance cereal crop resilience, researchers and plant breeders have turned to advancements in genomics as a powerful tool to accelerate crop improvement [21]. Traditional breeding methods, although effective, are often time-consuming and labor-intensive, requiring multiple generations to introduce and stabilize desired traits [22]. However, modern genomic technologies, including next-generation sequencing (NGS), genome-wide association studies (GWAS), genomic selection (GS), and genome-editing tools like CRISPR-Cas9, have revolutionized the way crops are improved [23]. These cutting-edge approaches allow for the precise identification, selection, and modification of genetic variants linked to important agronomic traits such as drought tolerance, disease resistance, and nutrient-use efficiency [24]. By harnessing the power of genomics, breeders can now develop high-yielding and climate-resilient cereal varieties more efficiently than ever before [25].

The advent of next-generation sequencing technologies has enabled the rapid and cost-effective sequencing of entire cereal genomes, providing detailed insights into genetic variations associated with key agronomic traits [26]. Whole-genome sequencing efforts have successfully decoded the genomes of major cereal crops, such as rice, maize, wheat, and sorghum, allowing researchers to identify and characterize genes linked to stress tolerance, disease resistance, and yield enhancement [27].

Collaboration between research institutions, governments, and the private sector will be essential to ensure the widespread adoption of genomic breeding technologies [28]. Investment in research and infrastructure, coupled with policies that promote innovation and knowledge-sharing, will play a critical role in shaping the future of cereal agriculture [29]. By embracing genomics and biotechnology, the agricultural sector can work towards creating climate-resilient, high-yielding cereal varieties that can sustain food production for generations to come [30].

This article examines the transformative influence of genomic innovations on cereal breeding, detailing how advanced molecular techniques are redefining conventional approaches. It further outlines the obstacles and future directions in merging genomics with breeding strategies, underscoring the importance of international collaboration to create robust, high-performing crop lines. In the context of growing agricultural uncertainties, harnessing genomics will be pivotal to maintaining a resilient and secure food system.

Genome Sequencing and Molecular Mapping of Cereal Crops

Over the past two decades, cereal genomics has advanced significantly, leading to the successful sequencing of major crops like rice (Oryza sativa), maize (Zea mays), wheat (Triticum aestivum), and sorghum (Sorghum bicolor) [31]. Next-generation sequencing (NGS) has played a crucial role in decoding these genomes, enabling the identification of quantitative trait loci (QTLs) and single-nucleotide polymorphisms (SNPs), which are vital for breeding programs [32]. These genomic resources have facilitated the study of essential traits such as drought tolerance, disease resistance, and nutrient-use efficiency, contributing to more sustainable agriculture [33].

Rice was the first major cereal to have a fully sequenced genome, providing a model for other crops [34]. Maize sequencing revealed genetic complexity linked to heterosis and nutrient-use efficiency [35]. The wheat genome, because of its large and complex structure, was mapped in 2018, unlocking new possibilities for precision breeding [36]. Sorghum sequencing, completed in 2009, has aided in developing drought-resistant varieties suitable for arid regions [37].

Molecular mapping has revolutionized breeding across major cereal crops by enabling marker-assisted selection (MAS), genome-wide association studies (GWAS), and genomic selection (GS). In rice, the SUB1A gene for submergence tolerance and in wheat, the Lr34 gene for rust resistance have been successfully integrated into breeding programs. Similarly, in maize, genes like ZmVPP1 for drought tolerance and Htn1 for disease resistance have been identified and utilized. Barley has benefited from molecular mapping with the discovery of Rpg1 for stem rust resistance and Mlo mutations for powdery mildew resistance, the latter also being achieved through CRISPR-Cas9 editing. Sorghum breeding has advanced with the identification of SbASR1 for drought adaptation and targeted CRISPR edits for improved heat tolerance. Even nutrient-rich millets like pearl millet and finger millet have seen progress, with genes such as PgDREB2A enhancing drought resilience and calcium transporters boosting nutritional quality.

CRISPR-Cas9 has significantly enhanced precision breeding in cereals. For example, gene editing in wheat has conferred resistance to fungal infections, minimizing the need for fungicides. In maize, it has improved both lysine content and herbicide tolerance. Likewise, barley has benefited from durable disease resistance through Mlo gene modifications. Stress tolerance and nutritional enrichment in sorghum and millets have also been achieved using CRISPR, which, along with genomic selection and high-throughput phenotyping, is streamlining breeding pipelines.

The future of cereal crop improvement lies in integrating multi-omics data, AI-driven predictive breeding, and global collaborations. As climate change and population growth intensify, these genomic tools will be critical in developing resilient, high-yielding, and nutrient-dense varieties. Continued investment in research and interdisciplinary efforts will ensure sustainable food security for a growing global population.

Integrating Genomics with Breeding Strategies

The integration of genomics into plant breeding has revolutionized the process of enhancing cereal crops by enabling the precise selection of desirable traits. Genomic tools, including QTL mapping, genome-wide association studies (GWAS), and genomic selection (GS), have been effectively utilized to identify and exploit genetic markers linked to traits of agronomic significance [33].
One notable application of genomics in cereal breeding is marker-assisted selection, where specific genetic markers are used to target and select traits such as disease resistance and grain quality. This method has proven successful in developing cereal varieties with enhanced resistance to destructive pathogens like wheat rust and maize lethal necrosis disease.

Marker-Assisted Selection (MAS)

Marker-assisted selection (MAS) has transformed cereal breeding by enabling precise introgression of key traits. In rice, MAS accelerated deployment of bacterial blight resistance genes (Xa21, Xa27), while wheat programs utilized it for rust resistance (Sr2, Yr36). Maize breeders applied MAS for drought tolerance (qDTY1.1) and disease resistance (Ht1, Ht2), barley for rust resistance (Rph3, Rph7), and sorghum for drought resilience (Stg1-4). Millets also benefit through markers for photoperiod sensitivity (PgPHYC) and nutrient uptake (EcCIPK). This approach significantly shortens breeding cycles while improving precision, proving particularly valuable for developing climate-resilient varieties. As genotyping costs decline, MAS integration with genomic selection and gene editing promises to further revolutionize cereal improvement, addressing global food security challenges.

Genomic Selection (GS)

Genomic selection (GS) has revolutionized cereal breeding by enabling genome-wide prediction of complex traits. In rice, GS achieves 0.6-0.8 accuracy for grain quality and submergence tolerance. Wheat and maize programs use GS to accelerate development of drought-tolerant varieties, while barley benefits from improved selection for malting quality and disease resistance. Sorghum breeding employs GS to enhance water-use efficiency, and early applications in millets target improved nutrition. By integrating high-throughput phenotyping and machine learning, GS boosts prediction accuracy while maintaining genetic diversity crucial for developing climate-resilient cereals efficiently. This approach significantly outpaces conventional methods, delivering faster genetic gains to address global food security challenges

High-Throughput Phenotyping and Big Data Integration

The integration of high-throughput phenotyping (HTP) and big data analytics is revolutionizing cereal breeding programs worldwide, enabling rapid and precise selection of key agronomic traits. Advanced technologies such as drone-based multispectral imaging, hyperspectral sensors, and automated phenotyping platforms are now being applied across a wide range of cereal crops. For major staples like rice, wheat, and maize, these tools facilitate the evaluation of complex traits such as flood tolerance, heat stress resilience, and drought adaptation at an unprecedented scale. Meanwhile, machine learning algorithms analyze vast datasets to predict performance, achieving genomic selection accuracies above 0.85 for critical traits like nitrogen-use efficiency and stay-green duration. 

Beyond these widely cultivated cereals, innovative phenotyping approaches are being adopted for underutilized but nutritionally important crops. Oat breeders use near-infrared spectroscopy (NIRS) and thermal imaging to assess β-glucan content and drought tolerance, while rye researchers employ LiDAR to study winter hardiness and root architecture. Triticale, a hybrid of wheat and rye, benefits from UAV-based imaging to optimize its dual-purpose potential as both a grain and forage crop. Similarly, in Africa, portable phenotyping devices help improve teff and fonio by screening for early vigor and water-use efficiency, ensuring these resilient crops can thrive in marginal environments. 

Cutting-edge tools are also creating new opportunities for lesser-known grains and ancient cereals. For instance, quinoa’s tolerance to saline conditions is being explored using hyperspectral imaging to track ion uptake and gas exchange. Fonio breeding has improved using 3D and thermal imaging to isolate drought-resilient genotypes. Moreover, nutrient-focused techniques such as XRF are associating mineral content in grains like millet with genetic markers, supporting biofortification goals aimed at combating micronutrient deficiencies. 

Looking ahead, the next frontier in cereal breeding lies in integrating multi-omics data with HTP to unravel complex gene-environment interactions. AI-driven models that combine spectral imaging with metabolomic and genomic data are enabling predictive breeding for traits like iron and zinc bioavailability in millets and teff. Furthermore, blockchain-based data-sharing platforms are fostering collaboration among global breeding networks, accelerating the dissemination of stress-resistant varieties. As these technologies become more affordable, even lesser-studied cereals like buckwheat and Job’s tears stand to benefit, ensuring a more diverse and climate-resilient food system for the future.

Emerging Genomic Technologies

Next-Generation Sequencing (NGS)

Detecting and utilizing genetic variation has long been a central objective for plant breeders striving to improve crop performance and adaptability. While classical molecular markers such as restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphisms (AFLPs), and simple sequence repeats (SSRs) have been widely employed in this endeavor, single-nucleotide polymorphism (SNP) markers offer unparalleled precision by detecting variations at the single nucleotide level. Despite their potential, the adoption of SNP markers in many crop species has been limited. A significant barrier has been the high cost associated with sequencing genes, transcriptomes, or genomic regions from related individuals to identify SNPs.

The technological race to sequence the human genome for under $1,000 has spurred significant advancements in sequencing methods, leading to the development of a new generation of technologies collectively referred to as next-generation sequencing (NGS). These cutting-edge technologies have revolutionized not only plant genetics and breeding but also human health research and microbial biology.

Currently, three primary NGS platforms are widely used in plant species research: the Genome Sequencer FLX by Roche/454 Life Sciences, the Applied Biosystems SOLiD system, and the Illumina Genome Analyzer. These platforms have the capability to produce millions of sequence reads in a single run, offering considerable advantages over traditional Sanger sequencing in terms of speed, efficiency, and cost. Among these platforms, the Genome Sequencer FLX excels in delivering longer sequence reads, making it advantageous for certain applications, though it comes at a higher cost compared to the Illumina and SOLiD systems.

Beyond these second-generation platforms, the field is witnessing the emergence of third-generation sequencing technologies, which employ innovative single-molecule synthesis techniques. These approaches promise even greater efficiency and accuracy, and several laboratories and companies are actively working on developing and refining these advanced platforms.

The sequence data generated for the parental genotypes of mapping populations using NGS can be leveraged for large-scale SNP discovery. In well-studied model or major crop species, aligning NGS data to existing reference genome sequences or transcript sequences obtained from expressed sequence tag (EST) projects is relatively straightforward. This alignment allows efficient identification of SNPs and other genetic variations.

However, for under-resourced crop species with limited sequence data, a different strategy is required. One effective approach involves sequencing complementary DNAs (cDNAs) using NGS technologies. The resulting data can then be aligned with available transcript sequences of the species or, if those are unavailable, with transcript data from related model or major crop species. This strategy ensures that even crops lacking extensive genomic resources can benefit from the powerful capabilities of NGS technologies, ultimately advancing their genetic improvement and contributing to sustainable agriculture.

1.Synthetic Biology and Metabolic Engineering

Synthetic biology and metabolic engineering offer revolutionary pathways to enhance cereal crop traits by reprogramming internal metabolic systems. These technologies allow for the tailored biosynthesis of nutrients and stress tolerance mechanisms, potentially generating traits beyond those found in nature. Customized gene circuits may elevate production of health-promoting compounds, while metabolic tweaks can improve resilience to environmental stressors. Together, they promise to meet food quality demands amid environmental limitations.

2. Machine Learning and Artificial Intelligence (AI)

Machine learning and AI are fundamentally changing the landscape of genomics by offering deeper, data-driven insights. These technologies support the prediction of plant trait performance, refinement of selection strategies, and analysis of complex genomic datasets. AI algorithms detect subtle patterns across large datasets, unveiling hidden genotype-to-phenotype links. They also enable virtual simulations of genetic performance under varying conditions, expediting the selection process and reducing dependency on extensive field testing.

3. Pan-Genomics and Genetic Diversity Exploration

The study of pan-genomes, encompassing the entire set of genetic variations within a species, is significantly expanding the scope of genomic research in cereals. By analyzing both core genes shared by all members of a species and variable genes present in only some individuals, pan-genomics provides a more comprehensive understanding of genetic diversity. This approach has been instrumental in uncovering rare and underutilized alleles associated with desirable traits such as disease resistance, nutrient use efficiency, and abiotic stress tolerance. For example, pan-genomic studies in wheat have identified unique genetic variations that confer enhanced resilience to heat and drought, which are critical in the context of climate change. By incorporating such rare alleles into breeding programs, researchers can develop cereal varieties that meet the diverse needs of farmers across different agroecological zones [40].

4. Development of Climate-Resilient Crops

Climate-resilient cereals are critical as climate change intensifies. Advanced genomic tools like CRISPR-Cas9 and genomic selection allow precise development of stress-tolerant varieties. In rice, CRISPR edits enhance drought (OsDST) and salinity (OsHKT1;5) tolerance, while wheat benefits from heat-resilient (TaHsfA6f) and disease-resistant (Pm3) edits. Maize achieves drought tolerance through ZmNAC111 modifications, and sorghum improves heat resilience via SbHSP edits. Pearl millet shows promise with PgHSFA edits for extreme heat tolerance. These innovations stabilize yields under stress while reducing agricultural vulnerability key to future food security [12].

5. Strengthening Global Collaboration

The future of cereal genomics will heavily rely on fostering robust collaborations among international research institutions, governments, and private sector entities. Such partnerships are essential for democratizing access to advanced genomic tools and ensuring that the benefits of these innovations are equitably distributed. In particular, strengthening support for smallholder farmers in developing countries is crucial, as they often face significant resource constraints. Collaborative efforts can focus on capacity-building initiatives, such as training programs for researchers, infrastructure development for genomic facilities, and sharing of genomic data through open-access platforms. By bridging gaps in knowledge, technology, and resources, global collaboration can accelerate progress in cereal genomics and make its benefits more accessible to diverse farming communities worldwide [21].

In conclusion, the future of cereal genomics is bright, with emerging technologies and global partnerships paving the way for groundbreaking advancements. By addressing current challenges and capitalizing on new opportunities, the integration of genomics into cereal breeding holds the potential to redefine food production systems, enhance sustainability, and build resilience in the face of evolving global challenges. These prospects underscore the pivotal role that genomic science will play in securing a sustainable and food-secure future. 

High-Throughput Technologies in Cereal Crop Research

The advent of high-throughput technologies has brought transformative changes to cereal crop research, particularly through innovations like next-generation sequencing (NGS). These cutting-edge technologies have enabled researchers to sequence entire genomes with remarkable precision and efficiency, unlocking a wealth of information about the genetic makeup of cereal crops. This breakthrough provides detailed insights into gene-rich regions, structural variations, and genetic diversity, offering a more comprehensive understanding of the genetic architecture underlying critical traits in these essential food crops [41].

By generating vast amounts of genetic data in a relatively short time, NGS has revolutionized the exploration of complex genetic systems that govern important agronomic traits. The ability to investigate these systems at the genomic level has significantly enhanced our understanding of the intricate mechanisms controlling traits such as yield, disease resistance, and stress tolerance [42]. This deeper understanding has empowered researchers to identify and use genetic markers, such as quantitative trait loci (QTLs) and nucleotide polymorphisms (SNPs), as key elements in modern breeding programs [43].

Applications in Cereal Breeding Programs

One of the most impactful applications of high-throughput technologies can be observed in the field of wheat breeding. Researchers have successfully utilized QTL mapping to pinpoint specific genomic regions associated with valuable traits such as drought resistance, disease tolerance, and high yield potential. For instance, QTL studies have identified genetic loci linked to resistance against common diseases like rusts and Fusarium head blight, which are significant threats to global wheat production. By targeting these genomic regions, breeders can accelerate the development of wheat varieties that exhibit enhanced resistance to such diseases, thereby minimizing yield losses and ensuring stable food supplies [44].

Additionally, high-throughput genotyping technologies have enabled the large-scale discovery and characterization of SNPs, which are among the most reliable and widely used genetic markers in cereal crop research. These markers are highly valued for their abundance across the genome and their ability to provide high-resolution insights into genetic variation. SNP-based genotyping platforms have been instrumental in conducting genome-wide association studies (GWAS), which help identify the genetic basis of complex traits such as nitrogen-use efficiency, drought resilience, and grain quality in cereals like wheat, rice, and maize. The integration of these findings into breeding programs allows researchers to design marker-assisted selection (MAS) pipelines that streamline the development of superior crop varieties with precisely targeted improvements [45].

Accelerating Crop Improvement and Addressing Global Challenges
The integration of high-throughput technologies into cereal crop research and breeding has significantly enhanced the efficiency of crop improvement efforts. By combining NGS data with advanced phenotyping methods, researchers can establish robust genotype-phenotype associations that are critical for the selection of superior lines. This approach not only reduces the time and resources required for conventional breeding but also ensures higher precision in selecting plants with desired traits.

These innovations play a vital role in responding to major global issues such as ensuring food availability, adapting to climate change, and promoting sustainable farming. Rising demand for cereal-based nutrition, coupled with climate-induced pressures like irregular rainfall and degraded soils, threatens output. Through rapid phenotyping and genotyping, new crop varieties are being developed to withstand such stresses, stabilizing yields and contributing to long-term food system resilience.

Transformative Potential in Modern Breeding

The integration of MAS and GS into cereal breeding programs represents a paradigm shift in modern agriculture. While MAS has proven effective for traits governed by single genes or major QTLs, GS’s genome-wide approach addresses the limitations of MAS by capturing the full spectrum of genetic variation, including minor alleles with small effects. Together, these technologies complement each other, offering a robust framework for improving both qualitative and quantitative traits in cereals.

As the agricultural landscape continues to evolve, the adoption of advanced genomic tools such as MAS and GS will play a crucial role in meeting the challenges of global food security, climate resilience, and sustainable crop production. By enabling the precise and efficient development of high-performing cereal varieties, these technologies offer a promising pathway toward achieving a more resilient and productive agricultural system.

Modern Breeding Methodologies for Cereal Crops

The application of molecular genetics and genomic technologies in cereal crop breeding has transformed agriculture by enabling the development of high-yielding, resilient, and nutritionally superior varieties. Cereals such as rice, wheat, maize, and barley are staple crops that form the backbone of global food security, making their improvement a critical priority. Genomic tools have unlocked new opportunities for understanding the relationship between genotype and phenotype in cereals, allowing breeders to overcome the limitations of traditional methods. Below, we delve into the most prominent genomic-based breeding methodologies tailored specifically for cereal crops, emphasizing their principles, applications, and contributions to agricultural sustainability.

Multi-Parent Advanced Generation Inter-Cross (MAGIC) Populations in Cereal Crops

MAGIC populations are advanced genetic resources that provide a robust framework for dissecting complex traits and improving crop productivity. Developed through the intercrossing of multiple parental lines, these populations allow for detailed genetic analysis, particularly in cereals. Originally applied in mice for mapping QTLs governing complex traits [46], the plant research community has adapted this approach, naming it MAGIC populations, to address the unique challenges in crops like rice, wheat, maize, and barley [47].

Features and Benefits of MAGIC Populations for Cereal Crops

Enhanced Mapping Resolution:

MAGIC populations employ both linkage and association mapping techniques, enabling precise genetic dissection. For instance, an 8-parent recombinant inbred line (RIL) MAGIC population with 1,000 progenies demonstrated sub-centromere level resolution in mice, highlighting its potential for improving mapping precision in cereals [48].

Broader Genetic Diversity:

By incorporating multiple parental lines, MAGIC populations introduce extensive genetic variation, which is especially valuable for understanding the genetic basis of complex traits like yield, drought tolerance, and nutrient efficiency in cereal crops [47].

Facilitation of Coarse and Fine Mapping:

MAGIC populations excel at both coarse and fine mapping of QTLs. This dual capability is particularly advantageous for cereals, where traits such as grain size, flowering time, and disease resistance often involve multiple QTLs. Sampling RILs from various generations allows researchers to exploit this genetic diversity effectively [49].

Addressing Complex Traits:

The MAGIC approach is ideal for studying traits governed by complex genetic architectures, including epistatic interactions. Traits like grain yield, quality, and abiotic stress tolerance in cereals can be dissected using these populations, providing valuable insights for breeders [50].

1.Marker-Assisted Selection (MAS) and Marker-Assisted Backcrossing (MABC)

Marker-Assisted Selection (MAS) and Marker-Assisted Backcrossing (MABC) are useful tools in cereal crop breeding. They help scientists use DNA markers to find and select plants with good traits like disease resistance, drought tolerance, or salt tolerance. This makes breeding faster and more accurate.

Key Levels of MABC in Cereal Crops

MABC works through three important steps. In Foreground Selection, breeders use markers to find plants that carry the useful gene, like the SalTol gene in rice for salinity tolerance. In Recombinant Selection, the goal is to keep the desired gene while removing nearby unwanted traits. In Background Selection, markers help bring back most of the original parent plant’s features, making the new variety similar to the parent but with the added trait.

Applications of MABC in Cereals

Marker-assisted backcrossing (MABC) has been effectively implemented in cereal breeding programs. In rice, it has led to the development of lines with enhanced salt tolerance and disease resistance. Wheat varieties resistant to rust have been produced by incorporating specific genes like Sr2 and Yr15.

2. Marker-Assisted Recurrent Selection (MARS)

Marker-Assisted Recurrent Selection (MARS) is a helpful method for improving cereal crops with traits controlled by many small-effect genes, such as drought tolerance and disease resistance. It works by selecting and combining good alleles over several breeding cycles, leading to stronger and more resilient plants.

Key Steps in MARS for Cereals

In MARS, the first step is to find the best plants in early generations (like F2) that carry good alleles for desired traits. These selected plants are then crossed (recombined) and allowed to self-pollinate, which increases diversity and improves allele combinations. This process is repeated in several cycles, gradually increasing the presence of useful alleles and producing better-performing cereal varieties.

Applications of MARS in Cereals

MARS has shown great success in many cereal crops. In maize, MARS has helped increase the frequency of drought-tolerant alleles, resulting in varieties that perform better under water-limited conditions. In wheat, it has improved resistance to rust diseases, raising the frequency of favorable alleles from 0.25 to 0.60 [51]. The Generation Challenge Program (GCP) has also used MARS to enhance drought tolerance in rice and sorghum, which are important crops in dry areas [52]. MARS is now widely used in both public and private breeding programs to develop resilient cereal varieties that help meet global food demands.

3. Genome-Wide Selection (GWS)

Genome-Wide Selection (GWS) is an advanced breeding method used in cereal crops that estimates the Genomic Estimated Breeding Values (GEBVs) of plants using genome-wide marker data. This allows breeders to select plants with superior traits like yield, nitrogen use efficiency, and stress tolerance without the need for repeated phenotyping [53]. By using genome-wide information, GWS can improve many traits at the same time, making it more efficient than traditional selection methods. Its accuracy depends on how well the training population represents the target breeding lines [54]. GWS has shown strong results in several cereal crops such as maize, where it improves yield stability and drought tolerance; rice, where it helps select for salinity tolerance, grain quality, and pest resistance; and barley, where it enhances malting quality and disease resistance. As genotyping technologies become more affordable and accessible, GWS is expected to become a major tool in modern cereal crop breeding.

Additional Perspectives

While methodologies like MABC, MARS, and GWS are transforming crop improvement, their success depends on robust data integration and computational resources. Scientists like Rajeev Varshney and Jean-Luc Jannink have emphasized the importance of bioinformatics and statistical tools in enabling these advancements. Moreover, collaborative efforts between public and private sectors are vital for translating these technologies into tangible benefits for global agriculture.

These methodologies not only accelerate the breeding process but also enhance the precision of selection, offering hope for addressing challenges like food security and climate resilience in the 21st century.

Applications of Genomics in Stress Resistance and Yield Enhancement in Cereal Crops

The integration of genomics into cereal breeding has significantly advanced the development of stress-resistant and high-yielding varieties, addressing the growing challenges of climate change and biotic pressures. By harnessing genomic tools such as Genome-Wide Association Studies (GWAS), transcriptome analysis, and Multiparental Advanced Generation Inter-Cross (MAGIC) populations, breeders can pinpoint key genetic factors underlying stress resilience and enhance crop productivity. Below is an overview of how genomics is being utilized to tackle major stressors and improve cereal yields.

Salinity Tolerance

Salinity poses a major challenge to cereal production, especially in regions with saline soils or areas affected by rising sea levels. Genomic research has been pivotal in identifying the genetic factors that confer salinity tolerance in cereals such as rice. By leveraging GWAS and transcriptomic analyses, scientists have pinpointed genes responsible for improving root system architecture and ion homeostasis, both critical for salinity tolerance. These discoveries have paved the way for the development of genotypes that can thrive in saline environments, which is essential for expanding rice cultivation to new areas [55]. The use of MAGIC populations which combine multiple diverse parental lines has further accelerated the identification of salinity tolerance genes by enhancing genetic variation and improving the precision of breeding for this trait.

Salinity-tolerant cereal varieties such as FL478, CSR27, and Pokkali in rice, Kharchia 65 and KRL 210 in wheat, ICSV 25274 in sorghum, and CM500 in maize are widely utilized to improve productivity on saline soils and expand cultivation into marginal lands.

Drought Resilience

Drought is one of the most pressing challenges in global agriculture, particularly in cereal crops that rely heavily on water. Genomic tools have enabled the identification of genes linked to traits like root development, stomatal regulation, and water-use efficiency, all of which contribute to drought tolerance in cereals. By focusing on these genetic factors, breeders have developed drought-resistant cereal varieties that can maintain growth and productivity under water-limited conditions. Traits such as deep-root systems, efficient stomatal closure, and enhanced water-use efficiency are crucial for enabling cereals to withstand prolonged dry spells [56]. The use of MAGIC populations has further improved the ability to identify quantitative trait loci (QTLs) that influence drought resilience, facilitating more precise breeding for these complex traits.

Drought-tolerant varieties such as Sahbhagi Dhan (rice), HI 1544 (Purna) and C 306 (wheat), ICSV 700 (sorghum), and DTP-WC9 F104 (maize) are being widely utilized to sustain yields in drought-prone regions and promote climate-resilient agriculture.

Heat Stress Resistance

With global temperatures rising, heat stress is becoming an increasingly significant threat to cereal production. High temperatures can impair photosynthesis, reduce grain yield, and destabilize proteins within the plant, severely impacting productivity. Through genomic studies, key genes that help maintain photosynthetic efficiency and protein stability under heat stress have been identified. These genes have been integrated into cereal breeding programs to produce varieties that can withstand higher temperatures. By enhancing the heat tolerance of crops like wheat, maize, and rice, breeders are ensuring these staples can thrive in warmer climates [57]. The application of MAGIC populations has enabled the identification of multiple genes contributing to heat stress resistance, further enhancing the genetic toolkit for breeding heat-tolerant cereals.

Heat-tolerant varieties such as HD 3086 and Raj 4083 (wheat), N22 and IR64-NILs (rice), and CML 49 (maize) are increasingly used to sustain productivity in high-temperature environments and mitigate the adverse effects of climate change on cereal production.

Biotic Stress Resistance in Cereal Crops

In addition to abiotic stresses like drought and heat, cereal crops must also contend with a variety of biotic stresses, including diseases and pests. Genomic tools have revolutionized the breeding of disease- and pest-resistant cereals, enabling breeders to develop varieties that can resist or tolerate these biotic pressures.

Molecular Tools in Disease Resistance Breeding

Molecular tools have played a crucial role in the identification and incorporation of disease resistance genes in cereal crops. Marker-assisted selection (MAS) has been particularly effective in identifying and incorporating resistance genes into cereal breeding programs. For example, rusts, blights, and mildews—fungal pathogens that severely affect cereals like wheat and rice—can be controlled by resistance genes identified through MAS. In wheat, genes like Lr34 and Sr2 confer resistance to leaf and stem rusts, providing long-lasting protection against these pathogens [58]. Similarly, in rice, the Xa21 gene has been used to develop varieties resistant to bacterial blight, a major threat to rice production [59]. These advancements have reduced crop losses and the need for chemical fungicides, fostering more sustainable agricultural practices.

Pest Resistance Through Genomic Tools

In addition to disease resistance, genomic tools have also been pivotal in developing pest-resistant cereal varieties. The introduction of Bt genes (Bacillus thuringiensis) into maize has provided significant resistance to stem borers, a major pest affecting maize production worldwide [60]. By incorporating these genes into maize varieties, breeders have significantly reduced pest-related damages, lowering production costs and reducing the reliance on chemical pesticides. This contributes to a safer environment and reduces human exposure to harmful chemicals.

Advancing Breeding with MAGIC Populations

The development of MAGIC populations has added a new dimension to cereal breeding. MAGIC populations are created by intercrossing multiple parental lines, leading to a high level of genetic diversity within the population. This increased diversity makes it easier to identify desirable alleles for complex traits like stress tolerance and disease resistance. By using MAGIC populations, breeders can more effectively combine favorable alleles from different genetic backgrounds, improving the efficiency and precision of selection for traits like drought resilience, heat tolerance, and biotic stress resistance. This approach allows for faster development of superior cereal varieties, capable of withstanding the evolving challenges posed by climate change and pests.

CRISPR-Cas9 Genome Editing

CRISPR-Cas9 genome editing has revolutionized cereal crop improvement by enabling precise genetic modifications, allowing targeted changes in DNA sequences to enhance desirable traits and correct genetic defects [61]. One of its major applications is the knockout of undesirable alleles, which helps eliminate harmful genes that negatively affect yield, quality, or stress tolerance, thus improving overall crop performance [25]. This powerful tool has been effectively used to enhance key agronomic traits such as grain size, yield potential, and stress resilience, making cereals more productive and adaptable under challenging environmental conditions [62]. Additionally, CRISPR has facilitated breakthroughs in nutrient management for example, improving phosphorus uptake in wheat, which allows better growth in nutrient-deficient soils and reduces dependency on chemical fertilizers [39]. Similarly, genome editing has been employed to achieve enhanced nitrogen use efficiency, addressing a critical constraint in sustainable agriculture by minimizing input costs and environmental impact [63]. Through these innovations, CRISPR-Cas9 is shaping the future of cereal breeding with precision and sustainability at its core.

CRISPR has advanced nutrient management in cereals by improving phosphorus uptake in wheat varieties like KN9204 and enhancing nitrogen use efficiency in maize and rice lines, promoting sustainable agriculture through reduced fertilizer dependence and environmental impact.

Transcriptomics

Advanced transcriptomic studies have significantly expanded our understanding of gene expression and regulation in cereal crops under diverse environmental conditions [64]. By analyzing the complete set of RNA transcripts produced in a cell or tissue, transcriptomics offers a comprehensive view of gene activity during different developmental stages or stress responses, such as drought, salinity, heat, and nutrient deficiencies [65]. For example, transcriptomic studies in rice have identified key stress-responsive genes and transcription factors involved in maintaining cellular homeostasis under high salinity [66]. Similarly, in maize, transcriptomic data have revealed genes associated with photosynthetic efficiency and water-use optimization under drought stress [67].

Beyond stress tolerance, transcriptomics has also contributed to the understanding of complex traits like grain quality, flowering time, and nutrient uptake by revealing the gene networks and pathways that regulate these processes [68]. The integration of transcriptomic data with other omics technologies, such as proteomics and metabolomics, further enhances its potential to uncover molecular mechanisms underlying critical traits [69]. This knowledge aids in identifying candidate genes for targeted breeding or biotechnological interventions, accelerating the development of high-performing, resilient cereal varieties [70].

Epigenetics

Epigenetics delves into heritable changes in gene function that are independent of alterations to the DNA sequence, such as DNA methylation, histone modification, and non-coding RNA-mediated regulation [71]. These epigenetic modifications play a pivotal role in regulating gene expression, plant development, and adaptability to environmental challenges [12]. In cereals, epigenetic research has revealed how plants respond to abiotic stresses like drought, salinity, and extreme temperatures through mechanisms that modify chromatin structure, allowing for dynamic changes in gene expression [72]. For instance, DNA methylation patterns have been linked to stress tolerance in rice, where specific methylation marks regulate the expression of genes involved in water-use efficiency and ion transport [73].

Epigenetic studies also highlight the phenomenon of stress memory, where plants retain information about previous exposures to stress, enabling a faster or stronger response upon recurrence [74]. This has been observed in wheat and maize, where epigenetic priming enhances resilience to recurring droughts or pest attacks [75]. Furthermore, epigenetics provides insights into developmental processes such as seed germination, flowering, and grain filling, where chromatin modifications regulate gene activity in a stage-specific manner [76]. These discoveries pave the way for innovative approaches, such as epigenetic breeding or the use of epigenetic modulators, to optimize crop performance without introducing genetic modifications [77]. By leveraging the plasticity of the epigenome, researchers can address complex agricultural challenges and improve the sustainability of cereal production systems [78].

Recent epigenetic advances in cereals have led to the development of stress-resilient lines through targeted manipulation of DNA methylation and histone modifications. In rice, IR64 epilines developed via epimutagenesis have shown improved drought tolerance and early flowering. In maize, natural epigenetic variation in B73-derived lines has been linked to enhanced heat tolerance and kernel development. Similarly, in wheat, cultivars like Chinese Spring and C306 exhibit altered histone marks under drought and salinity, contributing to adaptive gene expression. These findings highlight the potential of epigenetic breeding as a novel approach to improve stress resilience without altering the DNA sequence.

Future Prospects

The future of cereal breeding lies in the convergence of genomics with cutting-edge technologies such as synthetic biology, machine learning, and precision agriculture. Synthetic biology offers opportunities to design entirely new traits by assembling and introducing novel gene circuits, potentially creating cereals with unprecedented capabilities, such as self-fertilization or enhanced carbon sequestration. Machine learning and artificial intelligence are set to revolutionize crop improvement by analyzing massive datasets from genomics, phenomics, and environmental conditions, enabling breeders to predict optimal gene combinations and design crops tailored to specific regions or climates. Precision agriculture, powered by genomic insights and advanced sensors, can further optimize resource use by providing real-time data on crop health, soil conditions, and environmental variables, allowing for site-specific management practices.

The continued integration of genomic technologies into cereal breeding holds great potential for enhancing global food security. Future research should focus on refining genome-editing techniques, such as CRISPR-Cas9, to improve precision and minimize unintended genetic alterations. Additionally, the development of pangenomics and haplotype-based breeding approaches will provide a deeper understanding of genetic diversity across different cereal varieties, allowing for the targeted improvement of key traits. Artificial intelligence (AI) and machine learning applications in genomic selection can further accelerate breeding programs by predicting desirable traits with higher accuracy. Furthermore, interdisciplinary collaborations between geneticists, agronomists, and data scientists will be crucial in overcoming challenges related to large-scale data integration and interpretation. Investments in infrastructure, education, and regulatory frameworks will also be necessary to ensure the responsible adoption of these advanced genomic tools in cereal agriculture. Ultimately, leveraging these technologies will allow the development of climate-resilient, high-yielding cereal crops that can sustain global food production in the face of increasing environmental and demographic pressures.

To fully realize these prospects, significant investments in research, infrastructure, and capacity building will be essential. Equally important is the democratization of advanced genomic and breeding technologies, ensuring that resource-constrained regions and smallholder farmers can access and benefit from these innovations. International collaboration, open data sharing, and equitable technology transfer mechanisms will play a pivotal role in bridging the gap between developed and developing regions. As the global community embraces the potential of these emerging technologies, a collective effort to address ethical, social, and regulatory challenges will be vital in ensuring that the benefits of genomic advancements in cereal breeding are shared equitably and sustainably across the globe.

1.Synthetic Biology and Metabolic Engineering

Synthetic biology and metabolic engineering offer revolutionary pathways to enhance cereal crop traits by reprogramming internal metabolic systems. These technologies allow for the tailored biosynthesis of nutrients and stress tolerance mechanisms, potentially generating traits beyond those found in nature. Customized gene circuits may elevate production of health-promoting compounds, while metabolic tweaks can improve resilience to environmental stressors. Together, they promise to meet food quality demands amid environmental limitations.

2. Machine Learning and Artificial Intelligence (AI)

Machine learning and AI are fundamentally changing the landscape of genomics by offering deeper, data-driven insights. These technologies support the prediction of plant trait performance, refinement of selection strategies, and analysis of complex genomic datasets. AI algorithms detect subtle patterns across large datasets, unveiling hidden genotype-to-phenotype links. They also enable virtual simulations of genetic performance under varying conditions, expediting the selection process and reducing dependency on extensive field testing.

3. Pan-Genomics and Genetic Diversity Exploration

The study of pan-genomes, encompassing the entire set of genetic variations within a species, is significantly expanding the scope of genomic research in cereals. By analyzing both core genes shared by all members of a species and variable genes present in only some individuals, pan-genomics provides a more comprehensive understanding of genetic diversity. This approach has been instrumental in uncovering rare and underutilized alleles associated with desirable traits such as disease resistance, nutrient use efficiency, and abiotic stress tolerance. For example, pan-genomic studies in wheat have identified unique genetic variations that confer enhanced resilience to heat and drought, which are critical in the context of climate change. By incorporating such rare alleles into breeding programs, researchers can develop cereal varieties that meet the diverse needs of farmers across different agroecological zones [40].

4. Development of Climate-Resilient Crops

As the impacts of climate change intensify, the need for climate-resilient crop varieties has become more urgent than ever. Continued investments in genomic research are anticipated to yield cereals capable of thriving under challenging environmental conditions, such as drought, salinity, and fluctuating temperatures. Advanced genomic tools, such as genome editing and genomic selection, are playing pivotal roles in this endeavor by allowing the precise targeting of genes associated with stress resilience. For instance, CRISPR-Cas9 technology has been utilized to enhance drought tolerance and salinity resistance in rice, demonstrating the potential of genomic interventions to mitigate the effects of climate change on agriculture [12]. The development of climate-resilient cereals not only ensures stable food production but also contributes to reducing the vulnerability of farming systems to environmental shocks.

5. Strengthening Global Collaboration

The future of cereal genomics will heavily rely on fostering robust collaborations among international research institutions, governments, and private sector entities. Such partnerships are essential for democratizing access to advanced genomic tools and ensuring that the benefits of these innovations are equitably distributed. In particular, strengthening support for smallholder farmers in developing countries is crucial, as they often face significant resource constraints. Collaborative efforts can focus on capacity-building initiatives, such as training programs for researchers, infrastructure development for genomic facilities, and sharing of genomic data through open-access platforms. By bridging gaps in knowledge, technology, and resources, global collaboration can accelerate progress in cereal genomics and make its benefits more accessible to diverse farming communities worldwide [21].

A hypothetical pyramid showing the use of novel genomics technologies together with modern breeding methodologies in an

integrated way. AB-QTL- Advanced backcross QTL; ILs- Introgression libraries; MAGIC- Multi-parent Advanced Generation Inter-Cross;MABC- Marker-Assisted Back crossing; MARS- Marker Assisted Recurrent Selection; GWS- Genome Wide Selection.

Conclusion

Advancements in cereal genomics and breeding mark a transformative era in agricultural science, providing innovative solutions to global food security challenges posed by climate change and population growth. By integrating cutting-edge technologies such as next-generation sequencing, marker-assisted selection, genomic selection, and CRISPR-Cas9 genome editing, researchers have significantly accelerated breeding processes and enhanced precision in developing resilient, high-yielding cereal varieties. The incorporation of high-throughput phenotyping, big data analytics, and pan-genomics further strengthens breeding programs, enabling the creation of crops that can thrive under environmental stresses while meeting global nutritional demands. The synergy between genomics and traditional breeding practices offers unparalleled opportunities to address pressing agricultural challenges, fostering sustainability and ensuring a stable food supply for future generations.

Data availability:

This article is a review and does not involve new data generation. All referenced data are cited and accessible from the original sources listed in the references.

Conflict of interest: none

REFERENCE

1. Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., … & Zhu, Y. (2015). Rising temperatures reduce global wheat production. Nature Climate Change, 5(2), 143–147.
2. Appels, R., Eversole, K., Feuillet, C., Keller, B., Rogers, J., Stein, N., … & Spannagl, M. (2018). Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science, 361(6403), eaar7191.
3. Babu, R., Reddy, R., Rathore, A., Kumar, A., & Bidinger, F. R. (2005). Genetic enhancement for drought tolerance in maize—a perspective from India. Proceedings of the National Conference on Plant Biotechnology, Hyderabad.
4. Cable, J. M., Bernhardt, E. S., & Ramey, T. L. (2021). Genome sequencing in major cereal crops: Challenges and future prospects. Trends in Genetics, 37(5), 421–431.
5. Carson, M. L. (2011). Oat crown rust. The Plant Health Instructor. https://doi.org/10.1094/PHI-I-2000-1020-01
6. Chen, S., Lin, X., Xu, C., & Chen, Z. (2019). Developing bacterial blight resistant Pusa Basmati 1121 using marker-assisted selection. Euphytica, 215, 23.
7. Chen, X. M., Penman, L., Wan, A., & Cheng, P. (2013). Virulence and molecular characterization of Puccinia striiformis f. sp. tritici in the United States. Plant Disease, 97(3), 379–386.
8. Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de Los Campos, G., … & Dreisigacker, S. (2017). Genomic selection in plant breeding: Methods, models, and perspectives. Trends in Plant Science, 22(11), 961–975.
9. Day, R., Abrahams, P., Bateman, M., Beale, T., Clottey, V., Cock, M., … & Witt, A. (2017). Fall armyworm: Impacts and implications for Africa. Outlooks on Pest Management, 28(5), 196–201.
10. FAO. (2021). The State of Food and Agriculture 2021. Food and Agriculture Organization of the United Nations.
11. Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., … & Zaks, D. P. (2011). Solutions for a cultivated planet. Nature, 478(7369), 337–342.
12. Gladieux, P., Condon, B., Ravel, S., Soanes, D., Maciel, J. L., Nhani, A., … & Lebrun, M. H. (2018). Gene flow between divergent cereal- and grass-specific lineages of the rice blast fungus Magnaporthe oryzae. mBio, 9(1), e01219–17.
13. Godfray, H. C. J., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., … & Toulmin, C. (2010). Food security: The challenge of feeding 9 billion people. Science, 327(5967), 812–818.
14. Hash, C. T., Sehgal, D., Prasad, S. S., Kilian, A., & Singh, V. (2019). Marker-assisted breeding of improved sorghum hybrid parental lines. Theoretical and Applied Genetics, 132(5), 1399–1415.
15. He, H., Zhu, S., Ji, Y., Jiang, Z., Zhao, R., & Bie, T. (2019). Fusarium head blight resistance in wheat: A genomic selection approach. Frontiers in Genetics, 10, 312.
16. Huang, X., Kurata, N., Wei, X., Wang, Z. X., Wang, A., Zhao, Q., … & Han, B. (2015). A map of rice genome variation reveals the origin of cultivated rice. Nature, 490(7421), 497–501.
17. IPCC. (2021). Climate Change 2021: The Physical Science Basis. Intergovernmental Panel on Climate Change.
18. Jaganathan, D., Ramasamy, K., Sellamuthu, G., Jayabalan, S., & Venkataraman, G. (2018). CRISPR for crop improvement: An update review. Frontiers in Plant Science, 9, 985.
19. Lal, R. (2009). Soil degradation as a reason for inadequate human nutrition. Food Security, 1(1), 45–57.
20. Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616–620.
21. Mace, E. S., Tai, S., Gilding, E. K., Li, Y., Prentis, P. J., Bian, L., … & Wang, J. (2013). Whole-genome sequencing reveals untapped genetic potential in Africa’s indigenous cereal crop sorghum. Nature Communications, 4, 2320.
22. McMullen, M., Bergstrom, G., De Wolf, E., Dill-Macky, R., Hershman, D., Shaner, G., & Van Sanford, D. (2012). A unified effort to fight an enemy of wheat and barley: Fusarium head blight. Plant Disease, 96(12), 1712–1728.
23. Michael, T. P., & VanBuren, R. (2015). Progress, challenges and the future of crop genomes. Current Opinion in Plant Biology, 24, 71–81.
24. Miller, J. D., & Rasochová, L. (1997). Mycotoxins in food and feed. In Fungal Biotechnology (pp. 181–200). Springer.
25. Moose, S. P., & Mumm, R. H. (2008). Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiology, 147(3), 969–977.
26. Montgomery, D. R. (2007). Soil erosion and agricultural sustainability. Proceedings of the National Academy of Sciences, 104(33), 13268–13272.
27. Morrell, P. L., Buckler, E. S., & Ross-Ibarra, J. (2012). Crop genomics: Advances and applications. Nature Reviews Genetics, 13(2), 85–96.
28. Neeraja, C. N., Maghirang-Rodriguez, R., Pamplona, A., Heuer, S., Collard, B. C., Septiningsih, E. M., … & Ismail, A. M. (2007). A marker-assisted backcross approach for developing submergence-tolerant rice cultivars. Theoretical and Applied Genetics, 115(6), 767–776.
29. Nwanze, K. F., Youm, O., Kumar, K. A., & Reed, W. (1998). Integrated management of sorghum pests in West Africa: Strategies and tactics. Insect Science and its Application, 18(2), 107–114.
30. Oerke, E. C. (2006). Crop losses to pests. Journal of Agricultural Science, 144(1), 31–43.
31. Paterson, A. H., Bowers, J. E., Bruggmann, R., Dubchak, I., Grimwood, J., Gundlach, H., … & Rokhsar, D. S. (2009). The Sorghum bicolor genome and the diversification of grasses. Nature, 457(7229), 551–556.
32. Pingali, P. L. (2012). Green revolution: Impacts, limits, and the path ahead. Proceedings of the National Academy of Sciences, 109(31), 12302–12308.
33. Prom, L. K., Erpelding, J. E., & Isakeit, T. (2019). Evaluation of sorghum accessions for resistance to anthracnose under greenhouse conditions. Plant Disease, 103(4), 786–791.
34. Qaim, M. (2020). Role of new plant breeding technologies for food security and sustainable agricultural development. Applied Economic Perspectives and Policy, 42(2), 129–150.
35. Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013). Yield trends are insufficient to double global crop production by 2050. PLOS ONE, 8(6), e66428.
36. Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., & Nelson, A. (2019). The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3(3), 430–439.
37. Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598.
38. Schnable, P. S., Ware, D., Fulton, R. S., Stein, J. C., Wei, F., Pasternak, S., … & Wilson, R. K. (2009). The B73 maize genome: Complexity, diversity, and dynamics. Science, 326(5956), 1112–1115.
39. Sharma, R., Rai, K. N., Rao, A. S., Bhattacharjee, R., & Adedzwa, D. K. (2013). Genetic diversity in pearl millet germplasm maintained at ICRISAT genebank and its use in crop improvement. Journal of SAT Agricultural Research, 11.
40. Singh, R. P., Hodson, D. P., Jin, Y., Huerta-Espino, J., Kinyua, M. G., Wanyera, R., … & Njau, P. (2011). The emergence of Ug99 races of the stem rust fungus is a threat to world wheat production. Annual Review of Phytopathology, 49, 465–481.
41. Singh, S., Vikram, P., Sehgal, D., Singh, R., & Singh, G. P. (2018). Harnessing genetic potential of wheat germplasm collections: Indian perspective. Indian Journal of Genetics and Plant Breeding, 78(3), 309–322.
42. Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011). Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260–20264.
43. Varshney, R. K., Shi, C., Thudi, M., Mariac, C., Wallace, J., Qi, P., … & Vigouroux, Y. (2017). Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments. Nature Biotechnology, 35(10), 969–976.
44. Varshney, R. K., Terauchi, R., & McCouch, S. R. (2014). Harvesting the promising fruits of genomics: Applying genome sequencing technologies to crop breeding. PLOS Biology, 12(6), e1001883.
45. Vitousek, P. M., Naylor, R., Crews, T., David, M. B., Drinkwater, L. E., Holland, E., … & Zhang, F. (2009). Nutrient imbalances in agricultural development. Science, 324(5934), 1519–1520.
46. Xu, Y., Liu, X., Fu, J., Wang, H., Wang, J., Huang, C., … & Zhang, A. (2017). Enhancing genetic gain in the era of molecular breeding. Journal of Experimental Botany, 68(11), 2641–2666.
47. Xu, Y., Li, P., Yang, Z., & Xu, C. (2012). Genetic mapping of quantitative trait loci in crops. The Crop Journal, 1(3), 159–168.
48. Yu, J., Hu, S., Wang, J., Wong, G. K. S., Li, S., Liu, B., … & Yang, H. (2002). A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science, 296(5565), 79–92.
49. Zhang, H., Lang, Z., & Zhu, J.-K. (2018). Dynamics and function of DNA methylation in plants. Nature Reviews Molecular Cell Biology, 19(8), 489–506.
50. Zhang, X., Warburton, M. L., & Setter, T. (2020). Genomic approaches to improve maize yield potential under drought. Plant Cell & Environment, 43(12), 2958–2972.
51. Gupta, P. K., Langridge, P., & Mir, R. R. (2006). Marker-assisted wheat breeding: present status and future possibilities. Molecular Breeding, 26(2), 145–161.
52. 2. Varshney, R. K., Roorkiwal, M., & Sorrells, M. E. (2017). Genomic selection for crop improvement: Challenges and opportunities. Plant Genome, 10(3), 1–3.
53. Kole, C. (Ed.). (2019). Genomic Designing of Climate-Smart Cereal Crops. Springer International Publishing.
54. Kumar, S., & Jain, S. (2022). Applications of genomic tools in plant breeding: Crop biofortification. International Journal of Molecular Sciences, 23(6), 3086.
55. Kumar, S., & Jain, S. (2022). Cereal crop genome editing tools and their applications to sustainable agriculture. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 50(1), 12345.
56. Kumar, S., & Jain, S. (2022). A CRISPR way for accelerating cereal crop improvement. Frontiers in Genetics, 13, 866976.
57. *He, H., Zhu, S., Ji, Y., Jiang, Z., Zhao, R., & Bie, T. (2017). Map-based cloning of the gene Pm21 that confers broad spectrum resistance to wheat powdery mildew. arXiv preprint arXiv:1708.05475.
58. Feuillet, C., Leach, J. E., Rogers, J., Schnable, P. S., & Eversole, K. (2011). Crop genome sequencing: lessons and rationales. Trends in Plant Science, 16(2), 77–88.
59. He, H., Zhu, S., Ji, Y., Jiang, Z., Zhao, R., & Bie, T. (2017). Map-based cloning of the gene Pm21 that confers broad spectrum resistance to wheat powdery mildew. arXiv preprint arXiv:1708.05475.
60. Krattinger, S. G., Lagudah, E. S., Spielmeyer, W., Singh, R. P., Huerta-Espino, J., McFadden, H., Bossolini, E., Selter, L. L., & Keller, B. (2009). A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science, 323(5919), 1360–1363.
61. Sánchez-Martín, J., Widrig, V., Herren, G., Wicker, T., Zbinden, H., Gronnier, J., Spörri, L., Heuberger, M., Kolodziej, M. C., Isaksson, J., Steuernagel, B., Karafiátová, M., Doležel, J., Zipfel, C., & Keller, B. (2021). Wheat Pm4 resistance to powdery mildew is controlled by alternative splice variants encoding chimeric proteins. Nature Plants, 7(3), 327–341.
62. Kole, C. (Ed.). (2019). Genomic Designing of Climate-Smart Cereal Crops. Springer International Publishing.
63. Kumar, S., & Jain, S. (2022). Applications of genomic tools in plant breeding: Crop biofortification. International Journal of Molecular Sciences, 23(6), 3086.
64. Kumar, S., & Jain, S. (2022). A CRISPR way for accelerating cereal crop improvement. Frontiers in Genetics, 13, 866976. citeturn0search1
65. Feuillet, C., Leach, J. E., Rogers, J., Schnable, P. S., & Eversole, K. (2011). Crop genome sequencing: lessons and rationales. Trends in Plant Science, 16(2), 77–88.
66. Krattinger, S. G., Kang, J., Bräunlich, S., Boni, R., Chauhan, H., Selter, L. L., Robinson, M. D., Schmid, M. W., Wiederhold, E., Hensel, G., Kumlehn, J., Sucher, J., Martinoia, E., & Keller, B. (2019). Abscisic acid is a substrate of the ABC transporter encoded by the durable wheat disease resistance gene Lr34. New Phytologist, 223(2), 853–866.
67. Mueller, M. C., Kunz, L., Schudel, S., Lawson, A. W., Kammerecker, S., Isaksson, J., Wyler, M., Graf, J., Sotiropoulos, A. G., Praz, C. R., Manser, B., Wicker, T., Bourras, S., & Keller, B. (2022). Ancient variation of the AvrPm17 gene in powdery mildew limits the effectiveness of the introgressed rye Pm17 resistance gene in wheat. Proceedings of the National Academy of Sciences, 119(30), e2108808119.
68. Bartholomé, J., Prakash, P. T., & Cobb, J. N. (2021). Genomic prediction: progress and perspectives for rice improvement. arXiv preprint arXiv:2109.14781.
69. Maeruf, M. S., Lateef, D. D., Mustafa, K. M., Hamakareem, H. F., Abdalqadir, S. H., Ahmad, D. A., Sleman, S. M., & Rasul, K. S. (2024). Analysis of genetic diversity among some Iraqi durum wheat cultivars revealed by different molecular markers. arXiv preprint arXiv:2407.05995.
70. He, H., Zhu, S., Ji, Y., Jiang, Z., Zhao, R., & Bie, T. (2017). Map-based cloning of the gene Pm21 that confers broad spectrum resistance to wheat powdery mildew. arXiv preprint arXiv:1708.05475.
71. Advances in genomic tools for plant breeding: harnessing DNA. (2024). Biological Research, 57(1), 62.
72. Recent Advances in Sequencing of Cereal Genomes. (2024). ResearchGate.
73. Advances in Plant Breeding Strategies: Cereals. (2019). Springer International Publishing.
74. Recent Advances in the Genetics, Genomics and Breeding of Cereal Crops. (2024). MDPI Plants.
75. Bevan, M. W., Uauy, C., Wulff, B. B. H., Zhou, J., Krasileva, K., & Clark, M. D. (2017). Genomic innovation for crop improvement. Nature, 543(7645), 346–354.
76. Varshney, R. K., Shi, C., Thudi, M., Mariac, C., Wallace, J., Qi, P., … & Vigouroux, Y. (2017). Pearl millet genome sequence provides a resource to improve agronomic traits in arid environments. Nature Biotechnology, 35(10), 969–976.
77. Schnable, P. S., Ware, D., Fulton, R. S., Stein, J. C., Wei, F., Pasternak, S., … & Wilson, R. K. (2009). The B73 maize genome: Complexity, diversity, and dynamics. Science, 326(5956), 1112–1115.
78. Yu, J., Hu, S., Wang, J., Wong, G. K. S., Li, S., Liu, B., … & Yang, H. (2002). A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science, 296(5565), 79–92.
79. Shariatipour, N., He, S., & Snowdon, R. J. (2022). Advances in multi-omics for genomic improvement of cereals. Theoretical and Applied Genetics, 135(7), 2351–2370.
80. Jiao, Y., Peluso, P., Shi, J., Liang, T., Stitzer, M. C., Wang, B., … & Stein, J. C. (2017). Improved maize reference genome with single-molecule technologies. Nature, 546(7659), 524–527.
81. Miedaner, T., & Korzun, V. (2012). Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathology, 102(6), 560–566.