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Introduction

Genomic selection (GS) has revolutionized plant breeding by harnessing the power of genome-wide markers to predict the breeding value of plants, significantly accelerating the development of new crop varieties. Traditional plant breeding methods, which have relied heavily on phenotypic selection and the painstaking process of crossing and selecting plants over multiple generations, often take years to produce new varieties [1]. These methods are not only time-consuming but also limited in their ability to select for complex traits governed by multiple genes. GS offers a transformative approach by leveraging the comprehensive genetic information available from high-throughput genotyping technologies.

The foundational concept of GS involves using genome-wide marker data to build predictive models that estimate the genetic potential of breeding candidates. By doing so, GS enables breeders to make more informed and accurate selection decisions earlier in the breeding cycle, thus reducing the time and resources needed to develop improved crop varieties [2-3]. The advent of next-generation sequencing (NGS) technologies has played a critical role in this process, significantly reducing the cost and increasing the accessibility of genotyping. This technological advancement has allowed for the application of GS across a wide range of crops, from staple cereals like wheat and maize to vegetables and fruits, thereby broadening the impact of this innovative approach. In wheat, for example, GS has been used to develop high-yielding, drought-tolerant varieties that are crucial for ensuring food security in the face of climate change. Maize breeding programs have similarly benefited, with GS facilitating the creation of varieties that are more resistant to diseases and environmental stresses, leading to increased productivity and sustainability. Rice, another global staple, has seen improvements in nutritional quality and environmental resilience through the application of GS, addressing critical needs in regions prone to adverse climatic conditions.

The application of GS extends beyond these well-known crops. It has been instrumental in improving the breeding of crops like barley, where GS has enhanced traits critical for malting quality and disease resistance, essential for the brewing industry. In soybean, GS has accelerated the development of varieties with improved oil content and disease resistance, benefiting both farmers and consumers by enhancing crop quality and yield. Despite these successes, the implementation of GS is not without its challenges. One of the primary obstacles is the requirement for extensive and high-quality phenotypic and genotypic data to construct reliable predictive models. This necessitates significant investment in data collection and management infrastructure [4]. The complexity of plant genomes, coupled with the interactions between genotype and environment, adds further layers of difficulty, requiring continuous refinement of predictive models to maintain their accuracy and utility.

The initial costs associated with implementing GS can be a barrier, particularly for small-scale breeding programs that may lack the necessary resources. Overcoming these challenges will require collaborative efforts among researchers, breeders, and policymakers to democratize access to GS technologies and ensure their benefits are widely shared. The future of GS in plant breeding looks promising, with ongoing advancements in genomic technologies and computational tools poised to further enhance its capabilities. The integration of GS with emerging technologies such as genome editing and phenomics holds significant potential. Genome editing tools like CRISPR/Cas9 can introduce precise genetic modifications, complementing the predictive power of GS and enabling the rapid development of crops with desired traits. Phenomics, which involves high-throughput phenotyping, provides more accurate and detailed trait measurements, thereby improving the robustness of GS models. GS represents a paradigm shift in plant breeding, offering a more efficient and precise approach to crop improvement. By bridging the gap between genomics and traditional breeding practices, GS has the potential to address global food security challenges and promote sustainable agricultural development. Continued innovation and collaboration will be key to fully realizing the benefits of GS, paving the way for a new era of agricultural advancemen [5]t. This review aims to provide a comprehensive overview of the advancements in GS, its current applications, challenges, and future prospects, highlighting its pivotal role in the future of plant breeding.

  Historical Context and Evolution

The concept of genomic selection (GS) was first proposed in the early 2000s, representing a significant advancement in the field of plant breeding. Building on the foundation of marker-assisted selection (MAS), GS introduced a more comprehensive approach to genetic improvement. MAS, which emerged in the late 20th century, utilizes specific genetic markers associated with traits of interest to aid in the selection process [6]. While MAS has been instrumental in improving certain traits, its scope is limited to the markers available and the traits they are linked to. This often results in an incomplete picture of the genetic architecture governing complex traits, such as yield, drought tolerance, and disease resistance, which are controlled by multiple genes and influenced by environmental factors. GS overcomes these limitations by using high-density markers spread across the entire genome. This genome-wide approach allows for the capture of more genetic variation, providing a holistic view of an organism’s genetic potential. The implementation of GS involves the collection of extensive phenotypic and genotypic data from a training population. Statistical models, such as genomic best linear unbiased prediction (GBLUP) and various Bayesian methods, are then used to establish relationships between the markers and the traits of interest. These models enable the prediction of breeding values for individuals in a breeding population, facilitating more accurate and efficient selection decisions.

The reduction in genotyping costs due to the advent of next-generation sequencing (NGS) technologies has been a game-changer for GS. In the early days of genetic research, genotyping was an expensive and time-consuming process, limiting the number of markers that could be analyzed and the number of individuals that could be genotyped. The high costs restricted the application of MAS and other genetic improvement methods to a few economically important crops and traits [7]. However, NGS technologies have drastically reduced the cost and time required for sequencing, making high-throughput genotyping more accessible and feasible for a wide range of crops and breeding programs. The integration of NGS with GS has marked a significant evolution from traditional breeding methods that relied heavily on phenotypic selection. Phenotypic selection, which involves selecting individuals based on observable traits, can be slow and inefficient, particularly for traits that are difficult to measure or have low heritability. It also requires extensive field trials and multiple generations of selection to achieve significant genetic gains. GS, by contrast, enables the selection of superior individuals based on their genetic potential as predicted by the genome-wide markers, significantly accelerating the breeding cycle.

The historical development of GS can be traced through several key milestones. The initial theoretical framework for GS was laid out by Bernardo and Yu in 2007, who demonstrated the potential of using whole-genome markers to predict breeding values. Early applications of GS in livestock breeding provided proof of concept, showing that GS could lead to significant genetic gains. These successes spurred interest and investment in applying GS to plant breeding. The first practical implementations of GS in crop breeding programs were reported in the early 2010s, with notable examples in maize and wheat [8]. These early successes validated the theoretical models and demonstrated the practical utility of GS in improving complex traits in plants. Since then, GS has been increasingly adopted in plant breeding programs worldwide. Advances in computational tools and statistical methods have enhanced the accuracy and efficiency of GS models. The integration of high-throughput phenotyping (phenomics) and environmental data has further refined the predictive power of GS, allowing for more precise selection decisions. Collaborative efforts among research institutions, breeding companies, and funding agencies have facilitated the development and dissemination of GS technologies, making them accessible to a broader range of crops and regions, the evolution of GS from its conceptual origins to its current applications in plant breeding represents a significant leap forward in the field of genetic improvement [9]. By leveraging genome-wide markers and advanced statistical models, GS provides a more comprehensive and efficient approach to crop improvement, offering the potential to accelerate genetic gains and enhance the resilience and productivity of crops. The reduction in genotyping costs due to NGS technologies has been instrumental in making GS a practical and widely adopted tool, marking a transformative shift from traditional phenotypic selection to a more precise and data-driven approach to plant breeding.

  Mechanisms and Methodologies

Genomic selection (GS) relies on the integration of extensive phenotypic and genotypic data to construct predictive models that estimate the genetic merit of plants. This process begins with the collection of phenotypic data from a training population, which includes measurements of various traits of interest, such as yield, disease resistance, and stress tolerance. Simultaneously, genotypic data is obtained through high-throughput genotyping platforms, which generate vast amounts of genome-wide marker information [10]. The advent of next-generation sequencing (NGS) technologies has significantly increased the efficiency and reduced the cost of genotyping, enabling the generation of dense marker datasets for large populations.

Once the phenotypic and genotypic data are collected, advanced statistical methods are employed to develop predictive models. One of the most commonly used approaches is genomic best linear unbiased prediction (GBLUP), which utilizes a linear mixed model to predict the genetic value of individuals based on their marker data. GBLUP assumes that the effects of markers are normally distributed and incorporates all available marker information to provide an unbiased estimate of genetic merit. This method is particularly effective for complex traits controlled by multiple genes with small effects. In addition to GBLUP, various Bayesian approaches have been developed to enhance the predictive accuracy of GS models. Bayesian methods, such as BayesA, BayesB, and BayesCπ, incorporate prior distributions for marker effects and can accommodate different genetic architectures by allowing for marker-specific variances [11]. These models are especially useful when dealing with traits that exhibit large-effect quantitative trait loci (QTL) alongside numerous small-effect loci. By incorporating prior knowledge and accounting for marker heterogeneity, Bayesian approaches can improve the precision of genetic predictions. Machine learning techniques, including random forests, support vector machines, and deep learning, have also been applied to GS. These methods can capture complex non-linear relationships between markers and traits, potentially enhancing the predictive power of GS models. The integration of machine learning with traditional statistical approaches offers a promising avenue for further improving the accuracy and robustness of GS predictions.

The predictive models developed through these methods are used to estimate the breeding values of individuals in a breeding population. By selecting individuals with the highest predicted performance, breeders can accelerate the development of improved crop varieties. This selection process is significantly faster and more efficient than traditional phenotypic selection, as it allows for the early identification of superior individuals based on their genetic potential rather than waiting for the expression of phenotypic traits [12].

 Applications in Crop Improvement

The application of GS in crop improvement has demonstrated substantial gains in various crops, particularly in cereals like wheat and maize, where genetic advancements are crucial for global food security. The International Maize and Wheat Improvement Center (CIMMYT) has been at the forefront of applying GS to develop high-yielding, drought-tolerant wheat varieties. By leveraging GS, CIMMYT has been able to enhance the efficiency of its breeding programs, resulting in the rapid development of wheat varieties that are better suited to withstand the challenges posed by climate change. In wheat, GS has led to significant improvements in yield, stress tolerance, and disease resistance. For example, CIMMYT has utilized GS to develop wheat varieties with enhanced resistance to rust diseases, which are major threats to global wheat production. These varieties not only exhibit higher yields under disease pressure but also maintain their performance under varying environmental conditions. The ability to predict and select for multiple traits simultaneously has been a key advantage of GS, enabling breeders to optimize genetic gain across a range of important agronomic traits. GS has been successfully implemented in maize breeding programs [13]. The application of GS in maize has resulted in the development of varieties with improved drought tolerance, an essential trait for maintaining productivity in water-limited environments. Maize breeding programs have also benefited from the ability of GS to enhance resistance to major pests and diseases, such as the maize lethal necrosis disease, which poses a significant threat to maize production in Sub-Saharan Africa. By incorporating GS into their breeding strategies, maize breeders have been able to accelerate the development of resilient and high-yielding varieties, contributing to food security and agricultural sustainability. GS has facilitated the development of varieties with enhanced nutritional profiles and adaptability to adverse conditions. For instance, the application of GS has enabled the breeding of rice varieties with higher levels of micronutrients, such as iron and zinc, addressing the issue of micronutrient deficiencies in populations that rely heavily on rice as a staple food [14]. Additionally, GS has been used to develop rice varieties that are more tolerant to submergence and salinity, traits that are crucial for rice cultivation in regions prone to flooding and soil salinization. These improvements are vital for ensuring food security in areas vulnerable to climate variability and environmental stresses.

GS has been applied to a wide range of other crops, including barley, soybean, and sugarcane. In barley, GS has been used to enhance malting quality and disease resistance, traits that are critical for the brewing industry. In soybean, GS has accelerated the development of varieties with improved oil content and resistance to pests and diseases, benefiting both farmers and consumers by enhancing crop quality and yield. In sugarcane, GS has facilitated the breeding of varieties with higher sugar content and better resistance to diseases, contributing to the sustainability and profitability of sugarcane production [15]. The successful application of GS in these crops underscores its potential to revolutionize plant breeding and address global food security challenges. By providing a more efficient and precise approach to crop improvement, GS has the potential to accelerate genetic gains and enhance the resilience and productivity of agricultural systems worldwide.

 Benefits and Advancements

Genomic selection (GS) offers several substantial advantages over traditional breeding methods, making it a powerful tool for modern crop improvement. One of the most significant benefits of GS is the reduction in breeding cycle time. Traditional breeding methods often require multiple generations of crossing and selection, which can take many years to develop new varieties. GS, by contrast, allows breeders to predict the breeding value of plants early in their lifecycle using genome-wide markers [16]. This early prediction accelerates the selection process, enabling the faster development of new crop varieties that are better adapted to current and future challenges.

Another major advantage of GS is its ability to simultaneously select for multiple traits. Traditional phenotypic selection typically focuses on a few key traits at a time, which can limit the overall genetic gain. In contrast, GS uses high-density markers spread across the entire genome, capturing a more comprehensive picture of the genetic variation underlying complex traits. This enables breeders to optimize genetic gain across a suite of important agronomic traits, such as yield, stress tolerance, disease resistance, and nutritional quality [17]. The ability to select for multiple traits simultaneously is particularly valuable for improving complex traits controlled by numerous genes with small effects, which are often difficult to improve through traditional breeding methods.

The use of high-density markers in GS also increases the precision of selection. By incorporating a large number of markers, GS models can more accurately estimate the genetic merit of breeding candidates. This precision enhances the efficiency of breeding programs, reducing the reliance on extensive and costly field trials. While field trials remain important for validating the performance of new varieties under real-world conditions, the ability to make informed selection decisions based on genetic data allows breeders to focus their resources on the most promising candidates, streamlining the overall breeding process. GS has also facilitated the incorporation of diverse germplasm into breeding programs. Traditional breeding methods often rely on a limited pool of elite lines, which can restrict the genetic diversity available for improvement. GS enables the efficient evaluation and utilization of a broader range of genetic resources, including landraces, wild relatives, and exotic germplasm. By tapping into this genetic diversity, breeders can introduce new alleles that confer beneficial traits, enhancing the adaptability and resilience of crop varieties [19]. The advancements in computational tools and statistical methods have further bolstered the utility of GS. The development of more sophisticated predictive models, including machine learning techniques, has improved the accuracy and robustness of GS. These models can capture complex non-linear relationships between markers and traits, providing a more nuanced understanding of the genetic architecture underlying important agronomic traits [20]. The integration of high-throughput phenotyping (phenomics) and environmental data into GS models has also enhanced their predictive power, allowing for more precise selection decisions that account for genotype-environment interactions.

 Challenges and Limitations

Despite its transformative potential, the adoption of GS faces several challenges and limitations. One of the primary obstacles is the need for large datasets of phenotypic and genotypic information. The collection of high-quality phenotypic data requires extensive field trials and precise measurements of various traits, which can be labor-intensive and costly. Similarly, generating comprehensive genotypic data through high-throughput sequencing involves significant investment in genotyping technologies and data management infrastructure [20]. These requirements can pose a barrier, particularly for small-scale breeding programs and resource-limited regions.

The complexity of plant genomes and the interactions between genotype and environment add another layer of challenge to the implementation of GS. Plants exhibit a wide range of genetic architectures, with traits often controlled by multiple genes with varying effects. Additionally, environmental factors can significantly influence the expression of these traits, complicating the predictive modeling process. Continuous refinement of GS models is necessary to maintain their accuracy and utility, requiring ongoing research and development efforts.

The initial cost of implementing GS can also be a barrier to its widespread adoption. While the cost of genotyping has decreased significantly with the advent of NGS technologies, the upfront investment in genotyping platforms, data analysis tools, and training for breeders remains substantial. For small-scale breeding programs and developing countries, these costs can be prohibitive, limiting their ability to adopt GS. This highlights the importance of collaborative efforts and funding initiatives to democratize access to GS technologies. Public-private partnerships, international collaborations, and capacity-building programs can play a crucial role in making GS accessible to a broader range of crops and regions. Another challenge is the need for skilled personnel to manage and interpret the large datasets generated by GS [21]. The integration of genomic data into breeding programs requires expertise in bioinformatics, statistical genetics, and computational biology. Training and capacity-building initiatives are essential to equip breeders with the necessary skills to effectively utilize GS. Additionally, the development of user-friendly software and decision-support tools can facilitate the adoption of GS by making it more accessible to breeders without extensive computational backgrounds, the ongoing advancements in genomic technologies and computational methods hold promise for overcoming these obstacles. The continued reduction in genotyping costs, coupled with the development of more efficient data analysis tools, will make GS increasingly feasible for a wider range of crops and breeding programs. Collaborative efforts among researchers, breeders, policymakers, and funding agencies will be essential to address the barriers to GS adoption and fully realize its potential for sustainable agricultural development, GS offers numerous benefits and advancements for crop improvement, its successful implementation requires addressing several challenges and limitations. By investing in data collection and management infrastructure, refining predictive models, and fostering collaborative efforts, the agricultural community can harness the full potential of GS to enhance the efficiency and precision of plant breeding, ultimately contributing to global food security and sustainability. Several case studies highlight the remarkable success of genomic selection (GS) in enhancing crop improvement, demonstrating its practical impact across various agricultural sectors.

Barley: In barley breeding, GS has been instrumental in improving malting quality and disease resistance, two traits critical for the brewing industry. Traditional breeding methods for enhancing malting quality, which includes factors such as kernel size, enzyme activity, and protein content, were time-consuming and less precise. The implementation of GS has allowed breeders to predict these complex traits with higher accuracy, leading to the development of barley varieties that meet the stringent requirements of maltsters and brewers. Additionally, GS has facilitated the selection for disease resistance, particularly against barley rusts and powdery mildew, thus ensuring crop health and yield stability. This dual focus on quality and resistance through GS has significantly boosted the efficiency of barley breeding programs, directly benefiting the brewing industry by providing superior raw materials.

Soybean: In soybean, GS has accelerated the development of varieties with improved oil content and disease resistance. Soybean oil is a major product, and enhancing its content has significant economic implications. Traditional breeding approaches for oil content improvement were slow and limited in scope. GS, by leveraging genome-wide markers, has enabled the rapid selection of high-oil-content varieties. Additionally, disease resistance is a critical trait for soybean, which is susceptible to various pathogens like soybean cyst nematode and Phytophthora root rot. GS has enhanced the ability to select for multiple resistance genes simultaneously, leading to the development of robust varieties that can withstand these diseases. This not only increases yield and stability but also reduces the need for chemical inputs, benefiting both farmers and the environment.

Wheat: The International Maize and Wheat Improvement Center (CIMMYT) has successfully implemented GS to develop high-yielding, drought-tolerant wheat varieties. Given the increasing variability in weather patterns due to climate change, drought tolerance is a crucial trait. GS has allowed CIMMYT to predict and select for drought tolerance more effectively, leading to the rapid development of varieties that can maintain high yields under water-limited conditions. These wheat varieties have been deployed in regions prone to drought, significantly enhancing food security and farmers’ resilience to climate variability.

Maize: In maize, GS has been pivotal in developing varieties with improved yield and stress tolerance. For instance, the application of GS in tropical maize breeding programs has resulted in the creation of varieties that perform well under heat and drought stress [22]. The ability to predict genetic merit for these complex traits has enabled breeders to combine high yield potential with resilience, addressing the dual challenge of productivity and climate adaptability. Moreover, GS has been used to enhance resistance to major maize diseases, such as maize lethal necrosis and gray leaf spot, further securing maize production in susceptible regions.

Rice: Rice breeding programs have utilized GS to enhance nutritional profiles and adaptability to adverse conditions. For example, biofortified rice varieties with higher levels of essential micronutrients like iron and zinc have been developed using GS, addressing malnutrition issues in rice-dependent populations. Additionally, GS has facilitated the breeding of rice varieties that are tolerant to submergence and salinity, crucial traits for regions prone to flooding and soil salinization [23]. These advancements in rice breeding contribute significantly to food security and nutritional health in vulnerable areas.

 Future Prospects and Innovations

The future of GS in crop improvement looks exceedingly promising, driven by ongoing advancements in genomic technologies and computational tools. Several key innovations and trends are likely to shape the next phase of GS application in agriculture. Integration with Genome Editing: One of the most exciting prospects is the integration of GS with genome editing technologies, such as CRISPR/Cas9. While GS can predict the genetic potential of plants, genome editing can introduce precise modifications to specific genes, enhancing desirable traits or eliminating undesirable ones. The synergy between GS and genome editing can accelerate the development of crop varieties with optimal performance. For instance, GS can identify target genes associated with yield or stress tolerance, and genome editing can be used to make precise changes to these genes, resulting in enhanced crop varieties. This integrated approach holds immense potential for addressing complex agricultural challenges more efficiently.

Advancements in Phenomics: Phenomics, which involves high-throughput phenotyping, is another emerging field that complements GS Accurate and detailed trait measurements are critical for building robust GS models. Advances in phenotyping technologies, such as automated imaging systems, drones, and sensors, allow for the rapid and precise measurement of various plant traits in diverse environments. By integrating phenomics data with genomic data, breeders can improve the accuracy of GS models, leading to more reliable predictions of genetic merit. This integration is particularly valuable for capturing genotype-environment interactions, which are crucial for breeding crops that perform well under varying environmental conditions.

 Conclusion

Genomic selection (GS) represents a paradigm shift in plant breeding, fundamentally transforming the approach to crop improvement by leveraging genome-wide markers to predict the breeding value of plants. This innovative methodology significantly accelerates the development of new crop varieties, enhancing both efficiency and precision. By bridging the gap between genomics and traditional breeding practices, GS addresses the limitations of phenotypic selection and marker-assisted selection (MAS), providing a comprehensive tool for optimizing genetic gain across multiple traits simultaneously. The integration of high-throughput genotyping and advanced statistical models, such as genomic best linear unbiased prediction (GBLUP) and Bayesian approaches, has revolutionized the breeding process. GS allows for the early identification of superior breeding candidates, reducing the reliance on extensive and time-consuming field trials. This acceleration is critical in responding to the urgent challenges posed by climate change, increasing global population, and the need for sustainable agricultural practices. Several successful case studies illustrate the practical impact of GS in crop improvement. In barley, GS has enhanced malting quality and disease resistance, directly benefiting the brewing industry. In soybean, GS has led to varieties with improved oil content and disease resistance, providing economic and nutritional benefits. Wheat and maize breeding programs have seen significant advancements in yield, stress tolerance, and disease resistance through GS, contributing to food security in regions vulnerable to climate variability. These examples underscore the tangible benefits of GS in developing crops that meet specific industry and consumer needs, thereby supporting both economic and social goals. Despite its transformative potential, the adoption of GS faces several challenges. The need for large datasets of phenotypic and genotypic information necessitates substantial investment in data collection and management infrastructure. The complexity of plant genomes and genotype-environment interactions requires continuous refinement of predictive models to maintain accuracy. Additionally, the initial cost of implementing GS can be a barrier for small-scale breeding programs and resource-limited regions. Addressing these challenges requires collaborative efforts, funding initiatives, and the development of accessible, user-friendly tools and training programs to democratize the benefits of GS.

  References

• Meuwissen, T. H., Hayes, B. J., & Goddard, M. E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157(4), 1819-1829.

• VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science, 91(11), 4414-4423.

• Heffner, E. L., Sorrells, M. E., & Jannink, J. L. (2009). Genomic selection for crop improvement. Crop Science, 49(1), 1-12.

• Hickey, J. M., Crossa, J., & Perez-Rodriguez, P. (2017). Crop breeding methods and the challenge of genotype-by-environment interactions. Theoretical and Applied Genetics, 130(11), 2173-2193.

• Goddard, M. E., & Hayes, B. J. (2009). Genomic selection. Journal of Animal Breeding and Genetics, 126(4), 327-339.

• Friedman, J., & Meuleman, J. (2012). The role of genomic selection in plant breeding. Advances in Agronomy, 115, 29-52.

• Crossa, J., de los Campos, G., & Pérez, P. (2013). Prediction of genetic values of quantitative traits in plant breeding using genome-wide markers. Genetics, 194(2), 579-599.

• Jannink, J. L., & Pletcher, S. D. (2010). Genomic selection in crops: challenges and opportunities. Crop Science, 50(5), 1655-1670.
• Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., De Los Campos, G., … & Varshney, R. K. (2017). Genomic selection in plant breeding: methods, models, and perspectives. Trends in plant science, 22(11), 961-975.

• Resende, M. F. R., & Resende, R. S. (2012). Genomic selection in tree breeding. Tree Genetics & Genomes, 8(2), 167-176.

• Kroonenberg, P. M. (2015). Genomic prediction of complex traits in plant breeding. Euphytica, 206(1), 1-11.

• Jung, C., & Müller, T. (2014). Genomic selection for crop improvement. Molecular Breeding, 34(1), 1-7.

• Baker, R. J. (2011). Selection methods for genomic breeding in plants. Plant Breeding Reviews, 33, 1-24.

• Wang, J., & Zhang, Z. (2017). Advances in genomic selection for plant breeding. Frontiers in Plant Science, 8, 426.
• Varshney, R. K., & Dubey, A. (2009). Novel genomic tools and modern genetic and breeding approaches for crop improvement. Journal of Plant Biochemistry and Biotechnology, 18, 127-138.

• Heslot, N., Yang, H. P., & Beavis, W. D. (2012). Genomic selection in plant breeding: a comparison of models. Crop Science, 52(1), 146-160.

• Ribaut, J. M., & Hoisington, D. (2016). Marker-assisted selection for drought tolerance in maize. Crop Science, 56(4), 1670-1681.

• Bostwick, D., & Iwata, H. (2015). Genomic selection in barley: a review. Journal of Cereal Science, 65, 163-173.
• Bhat, J. A., Ali, S., Salgotra, R. K., Mir, Z. A., Dutta, S., Jadon, V.,. & Prabhu, K. (2016). Genomic selection in the era of next generation sequencing for complex traits in plant breeding. Frontiers in genetics, 7, 221.

• Varshney, R. K., & Thudi, M. (2016). Genomic selection in chickpea: current status and future prospects. Theoretical and Applied Genetics, 129(12), 2113-2132.

• Cobb, J. N., & Jenness, A. (2017). Genomic selection in wheat: a review. Euphytica, 213(6), 239-254.

• Sorrells, M. E., & Johnson, E. B. (2013). Advances in genomic selection and prediction for crop improvement. Journal of Experimental Botany, 64(8), 2143-2151.

• Yu, J., & Zhang, Q. (2012). Genomic prediction of genetic value in maize. The Plant Genome, 5(3), 208-221.