Maize Genomics and Genetics 2024, Vol.15, No.5, 247-256 http://cropscipublisher.com/index.php/mgg 253 with other breeding platforms has greatly facilitated the molecular breeding activities, particularly in small- and medium-sized breeding programs. The use of HTS in developing these markers has proven to be cost-effective and efficient, making it a valuable tool for enhancing insect resistance in maize (Guo et al., 2019). 6 Future Prospects of High-Throughput Sequencing in Maize Breeding 6.1 Integration and utilization of multi-omics data The integration of high-throughput sequencing (HTS) with various omics data, such as transcriptomics, epigenomics, proteomics, and metabolomics, holds significant promise for maize breeding. By combining these diverse data types, researchers can gain a comprehensive understanding of the genetic and molecular mechanisms underlying important agronomic traits. For instance, the integration of transcriptomic data with genomic information has been shown to improve the prediction of hybrid performance in maize, particularly for complex traits like dry matter yield (Westhues et al., 2017; Schrag et al., 2018). Additionally, multi-omics approaches have been successfully applied to identify novel biological markers for improving abiotic stress tolerance in maize (Yang et al., 2021; Farooqi et al., 2022). The integration of multi-omics data can enhance the precision of breeding outcomes by providing a more detailed and holistic view of the genetic architecture of traits. This approach allows for the identification of key regulatory networks and interactions that are not captured by genomic data alone. For example, combining genomic, transcriptomic, and metabolomic data has been shown to improve the prediction of hybrid performance in maize, leading to more efficient selection of superior candidates (Schrag et al., 2018). Furthermore, the use of multi-omics data can help elucidate the molecular basis of phenotypic variations, thereby improving the accuracy of breeding value estimation (Yang et al., 2017; Yang et al., 2021; Zhou and Yan, 2024). 6.2 Advances and breakthroughs in HTS technology Recent advancements in HTS technologies, such as single-cell sequencing, are poised to revolutionize maize breeding by providing unprecedented resolution and insights into cellular and molecular processes. Single-cell sequencing allows for the analysis of gene expression and genetic variation at the individual cell level, enabling the identification of rare cell types and the study of cellular heterogeneity. These advancements can lead to a better understanding of the genetic and epigenetic regulation of important traits, ultimately facilitating the development of more precise breeding strategies (Franzosa et al., 2015; Ritchie et al., 2015). The potential applications of HTS in maize breeding are vast, ranging from the identification of beneficial quantitative trait loci (QTL) and genes to the development of precision breeding techniques. However, several challenges must be addressed to fully realize the potential of HTS. These include the need for advanced data integration and analysis methods to handle the large and complex datasets generated by HTS, as well as the development of cost-effective and scalable sequencing technologies (Ritchie et al., 2015; Yang et al., 2021; Farooqi et al., 2022). Additionally, the successful application of HTS in maize breeding will require overcoming technical and logistical challenges related to data management and sharing. 6.3 Challenges and opportunities in data management and sharing The management and sharing of large-scale HTS data present significant challenges due to the sheer volume and complexity of the data. Effective data management strategies are essential to ensure the accessibility, reproducibility, and long-term storage of HTS data. This includes the development of standardized protocols for data collection, processing, and storage, as well as the implementation of robust data sharing platforms that facilitate collaboration among researchers (Franzosa et al., 2015; Ritchie et al., 2015). Addressing these challenges will be critical to maximizing the utility of HTS data in maize breeding. Promoting international data sharing is crucial for driving collaborative research and accelerating progress in global maize breeding. By sharing HTS data and associated metadata, researchers can leverage collective knowledge and resources to address common challenges and develop innovative solutions. International data sharing initiatives can also help standardize methodologies and ensure the comparability of results across different studies. Collaborative efforts in data sharing have the potential to enhance the efficiency and effectiveness of
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