Jul 2026· International Journal of Computational and Biological Sciences· 0 citations
TL;DR
A comprehensive analysis of the methodological, theoretical, and practical frameworks underlying molecular-marker applications in modern plant breeding is provided, offering a thorough perspective on how molecular biotechnology can be optimized to ensure future global food security.
Abstract
The integration of molecular markers into agricultural biotechnology has fundamentally transformed crop breeding paradigms, particularly concerning disease-resistance screening and the identification of complex agronomic traits. Global agricultural systems face unprecedented challenges from biotic stressors, shifting climatic patterns, and the continuous evolution of pathogenic microorganisms, thereby necessitating the rapid development of resilient crop varieties. This paper provides a comprehensive analysis of the methodological, theoretical, and practical frameworks underlying molecular-marker applications in modern plant breeding. By exploring the historical evolution from classical phenotypic selection to advanced genomics-assisted breeding, the discussion elucidates the mechanisms through which single nucleotide polymorphisms and simple sequence repeats facilitate the precise localization of quantitative trait loci. Furthermore, the paper details the analytical pipelines required for high-throughput genotyping, marker-assisted selection, and genome-wide association studies, highlighting their respective impacts on selection efficiency and cost reduction in crop improvement programs. An evaluation of contemporary empirical data reveals that integrating multi-omic approaches with established molecular markers significantly enhances the accuracy of predicting disease resistance across diverse agricultural panels. Ultimately, this research synthesizes current technological advancements and methodological constraints, offering a thorough perspective on how molecular biotechnology can be optimized to ensure future global food security.
This comprehensive review demonstrates that shifting from reactive field evaluation to marker-driven, genomics-assisted precision design provides the definitive molecular framework required to engineer high-yielding, climate-resilient, and disease-proof cacao cultivars, thereby permanently safeguarding the long-term economic sustainability of global cocoa supply chains.
Atharva Gangurde, Adesina Christiana, Franc Olivier Nzogang· International Journal of Inn...· 0 citations
By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations
Kazakhstan possesses considerable potential for the development of organic agriculture. In organic production systems, the use of chemical plant protection products is restricted or completely excluded, making the cultivation of genetically resistant wheat lines to major fungal diseases one of the most effective approaches for maintaining stable grain production. The current study aimed to evaluate disease resistance in wheat genotypes by integrating phenotypic screening and marker-assisted selection and their validation under organic farming conditions. A total of 50 facultative and introgressive wheat lines were evaluated under an artificial infection background for resistance to yellow rust, leaf rust, stem rust, and common bunt. Molecular marker analysis was performed to identify resistance-associated alleles. Integrated phenotypic and molecular analyses enabled the identification of three promising genotypes, namely 1675-52, 1723-32, and 1716-24. They combined a high level of resistance to yellow rust and common bunt with the presence of resistance-associated alleles. These selected genotypes were subsequently validated under organic field conditions. The results demonstrated that these lines maintained stable resistance to yellow rust and common bunt and produced seed yield ranging from 5.45 to 5.94 t/ha, exceeding that of the standard cv. Almaly (4.88 t/ha). The obtained results confirm the effectiveness of integrating phenotypic screening with marker-assisted selection for identifying wheat genotypes with complex disease resistance. These genotypes represent promising prebreeding resources for organic agriculture, subject to validation across a wider range of environments.
R. Yerzhebayeva, S. Bastaubayeva, T. Bazylova et al.· Agronomy· 0 citations
Crop productivity is increasingly threatened by climate change and multiple abiotic stresses that significantly reduce agricultural sustainability and food security worldwide. Molecular breeding and genomics-assisted crop improvement strategies have emerged as effective approaches for developing high-yielding and stress-resilient crop varieties. The present study evaluated quantitative agronomic traits and their significance in molecular breeding applications using a rice genotype–phenotype dataset containing quantitative trait information and SNP-based genomic data. Quantitative trait analysis, correlation analysis, Principal Component Analysis, and machine learning-based predictive modeling were performed to assess phenotypic variability and yield-associated trait relationships. The results demonstrated substantial phenotypic diversity among rice accessions, particularly for grain morphology, plant architecture, and reproductive traits. Correlation analysis revealed significant positive associations among several agronomic traits, while PCA identified plant architecture and grain morphology as major contributors to phenotypic variation. A Random Forest regression model was further developed to predict grain weight using agronomic traits, where grain width and grain length emerged as the most influential predictors of yield-associated performance. The findings highlight the importance of integrating quantitative trait analysis, predictive modeling, and molecular breeding approaches for improving crop yield and adaptive performance. The study also demonstrates the potential application of computational and genomics-assisted breeding frameworks in developing climate-resilient rice cultivars. Overall, the integration of machine learning and molecular breeding strategies may contribute substantially to sustainable crop improvement and future agricultural productivity.
Shikha, Munish Kaundal, D. K. Upadhyay et al.· Genetics and Molecular Resea...· 0 citations
Single nucleotide polymorphisms represent the most abundant form of genetic variation in plant genomes and have become fundamental markers in modern plant genetics and breeding. Increasing global demand for food, combined with the pressures of climate change, environmental stress, and declining arable land, requires accelerated crop improvement strategies that exceed the capacity of conventional breeding approaches. In this context, SNP-based genomic technologies provide high-resolution tools for analyzing genetic diversity, identifying trait-associated loci, and enhancing selection efficiency in breeding programs. This review provides a comprehensive synthesis of SNP discovery methodologies, tracing their development from early Sanger sequencing approaches to advanced next-generation sequencing technologies, including whole-genome resequencing, genotyping-by-sequencing, and high-density SNP arrays. The article further examines the diverse applications of SNP markers in plant genetics, including genetic diversity analysis, linkage mapping, genome-wide association studies, marker-assisted selection, genomic selection, and evolutionary research. Key analytical and technical challenges, particularly those related to polyploid genome complexity, large-scale genomic data processing, and accurate variant interpretation, are critically discussed. In addition, emerging developments such as graph-based pangenomes, long-read sequencing technologies, machine learning-assisted SNP prioritization, and multi-omics integration are highlighted as promising directions for future research. By integrating recent technological advances with established genomic approaches, this review emphasizes the central role of SNP-based genomics in accelerating crop improvement and enabling climate-resilient, sustainable agricultural systems that support global food security.
Yucong Geng, Muhammad Zain ul Abideen, Alishba Shaukat et al.· International Journal of Bio...· 0 citations