Molecular Plant Breeding 2024, Vol.15, No.5, 295-307 http://genbreedpublisher.com/index.php/mpb 303 challenging environments (Sousa et al., 2021). Additionally, the identification of superior haplotypes for key traits, such as grain size and weight in rice, provides clear targets for breeding programs and can be communicated effectively to stakeholders (Liu et al., 2023). Engaging farmers through participatory breeding programs and providing training on the use of genomic tools can further facilitate the adoption of HBB practices. 8 Future Directions in Rice Genomics 8.1 Emerging trends in genomic research for crop improvement The field of crop genomics is rapidly evolving, with significant advancements that promise to revolutionize rice breeding and production. Over the past two decades, the sequencing of numerous crop genomes, including rice, has laid a robust foundation for genomic research. The integration of genome-scale information across various biological scales is expected to enhance our understanding of crop biological processes, thereby facilitating the translation of laboratory findings to field applications (Purugganan and Jackson, 2021). Using18K-rice “hybrid” population, the method of rapid excavation of quantitative trait genes in RiceG2G rice was developed, which greatly improved the efficiency of genetic analysis of agronomic traits, opened up the key step from rapid screening of genetic loci to candidate genes, and systematically evaluated the additive and epistatic effects of rice QTL genes. This study provides comprehensive gene interaction information for quantitative traits for rice genetic research and will provide theoretical support for rice molecular design breeding (Wei et al., 2024). Genomics-assisted breeding (GAB) has emerged as a pivotal approach, leveraging modern genome resources to exploit allelic variation for germplasm enhancement and cultivar development. Future iterations of GAB (GAB 2.0) will focus on the targeted manipulation of allelic variation to create novel diversity, which is crucial for developing climate-smart and nutritionally superior rice cultivars (Varshney et al., 2021b). Additionally, the development of platinum standard reference genomes for the Oryza genus will enable the efficient identification and utilization of adaptive traits from wild rice relatives, further enhancing crop improvement efforts (Mussurova et al., 2020). 8.2 Potential for integrating AI and machine learning in haplotype analysis The integration of artificial intelligence (AI) and machine learning (ML) in haplotype analysis holds immense potential for accelerating rice genomic research. Recent advancements in deep learning have demonstrated its effectiveness in modeling the flow of information from genomic DNA sequences to molecular phenotypes, as well as in identifying functional variants in natural populations (Wang et al., 2020). Machine learning approaches are also being utilized for genomic selection (GS), significantly reducing the need for resource-intensive phenotyping by predicting agronomically relevant traits from genotypic data (Tong and Nikoloski, 2020). These technologies can enhance the precision of genomic prediction models, incorporating environmental effects and genotype-by-environment interactions to improve the accuracy of trait predictions (Toda et al., 2020). The integration of high-throughput molecular phenotypic data with biological networks through AI and ML will further streamline the selection of elite genotypes, thereby shortening the breeding cycle and improving crop yields (Tong and Nikoloski, 2020). 8.3 Prospects for global collaboration in rice research Global collaboration is essential for advancing rice genomics and addressing the challenges of food security. Collaborative efforts have already led to significant progress in rice functional genomics, with contributions from experts in major rice-producing countries (Xiong et al., 2020). The establishment of international research centers and consortia, such as the International Maize and Wheat Improvement Center (CIMMYT) and the National Key Laboratory of Crop Genetic Improvement, has facilitated the sharing of knowledge and resources (Xu et al., 2021). Future collaborations should focus on the development and dissemination of genomic resources, such as the platinum standard reference genomes, to ensure that researchers worldwide can access and utilize these tools for crop improvement (Mussurova et al., 2020). Additionally, fostering partnerships between academic institutions, government agencies, and private sector organizations will be crucial for translating genomic research into practical applications that benefit farmers and consumers globally.
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