Triticeae Genomics and Genetics, 2024, Vol.15, No.3, 137-151 http://cropscipublisher.com/index.php/tgg 146 protein- and starch-related quality traits was found to be significantly influenced by the environment (Guo et al., 2020). These interactions complicate the identification of stable QTLs and the development of robust markers for breeding programs. 6.3 Resource and infrastructure constraints 6.3.1 Cost and accessibility The high cost of developing and implementing high-density genetic maps is a major constraint, particularly for resource-limited breeding programs. The construction of high-density maps, such as those using the Wheat55K SNP array, involves significant financial investment (Liu et al., 2018; Ren et al., 2021). Additionally, the cost of genotyping and data analysis can be prohibitive, limiting the accessibility of these technologies to well-funded research institutions and breeding programs (Rimbert et al., 2018). 6.3.2 Capacity building in developing regions Developing regions often lack the infrastructure and expertise required for high-density genetic mapping. Capacity building in these regions is essential to ensure that the benefits of advanced genetic mapping technologies are widely accessible. For instance, the development of a consensus genetic map using a 90K SNP array provided a valuable resource for systematic mapping and gene discovery in wheat (Qu et al., 2021). However, the successful implementation of such technologies requires investment in training and infrastructure development to build local capacity and expertise (Borrill et al., 2018). 7 Future Directions 7.1 Emerging technologies 7.1.1 CRISPR and gene editing The advent of CRISPR/Cas9 technology has revolutionized genetic research, providing a precise and efficient method for gene editing. In wheat, CRISPR has been successfully applied to target and modify specific genes associated with important agronomic traits. For instance, the integration of CRISPR/Cas9 with genetic mapping has enabled the identification and functional validation of candidate genes in maize, demonstrating its potential in wheat as well (Liu et al., 2020). The annotated reference genome of wheat further facilitates the application of CRISPR by providing detailed gene content and structural organization, which is crucial for targeted gene editing (Appels et al., 2018). Future research should focus on optimizing CRISPR protocols for wheat and exploring its potential in creating new wheat varieties with improved traits. 7.1.2 Advanced sequencing techniques Advanced sequencing techniques, such as genotyping-by-sequencing (GBS) and whole-genome resequencing, have significantly enhanced the resolution of genetic maps in wheat. These techniques allow for the discovery of a large number of single nucleotide polymorphisms (SNPs) across the wheat genome, which are essential for high-density genetic mapping (Gutierrez-Gonzalez et al., 2019; Pang et al., 2020). The development of high-throughput genotyping arrays, such as the Wheat55K SNP array, has further streamlined the process of QTL mapping and marker-assisted selection (Liu et al., 2018; Ren et al., 2021). Future directions should include the integration of these advanced sequencing techniques with other omics data to provide a comprehensive understanding of the wheat genome. 7.2 Integrative approaches 7.2.1 Multi-omics The integration of multi-omics approaches, including genomics, transcriptomics, proteomics, and metabolomics, offers a holistic view of the genetic and molecular mechanisms underlying important traits in wheat. For example, the use of a transcriptome atlas in wheat has revealed tissue-specific gene expression and coexpression networks, which are crucial for understanding the genetic basis of complex traits (Appels et al., 2018). Combining multi-omics data with high-density genetic maps can enhance the identification of candidate genes and the elucidation of their functions. Future research should focus on developing integrative platforms that can seamlessly combine multi-omics data for comprehensive genetic analysis.
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