Molecular Plant Breeding 2025, Vol.16, No.5, 268-277 http://genbreedpublisher.com/index.php/mpb 274 of superior haplotypes will be further accelerated, which is helpful for achieving molecular design breeding of complex traits (Yang et al., 2021; Wang et al., 2024). 9.2 AI-powered haplotype prediction and breeding decision support systems Artificial intelligence (AI) and machine learning (ML) technologies provide powerful tools for the integration of multi-omics big data and the prediction of complex traits. AI can efficiently process large-scale genomic, phenotypic and environmental data, enhance the recognition ability of haplotypes and trait associations, and help optimize breeding decisions (Yan and Wang, 2022; Wu et al., 2024). AI-driven multi-omics integrated models can achieve multi-level prediction of genotype - environment - phenotype, helping breeders make more accurate decisions in the early screening stage (Wu and Xie, 2024). In the future, with the continuous advancement of high-throughput phenoomics and AI algorithms, AI-based haplotype prediction and decision support systems will play an important role in molecular design breeding of crops such as rice (Cembrowska-Lech et al., 2023; Wu et al., 2024). 9.3 Toward climate-resilient, high-yield rice through haplotype-based breeding Haplotype breeding provides a new idea for cultivating climate-resilient and high-yield rice varieties by exploring and aggregating excellent haplotypes related to adverse adaptability such as drought resistance and heat tolerance . Under drought stress conditions, Naqvi et al. (2024) and Singh et al. (2024) have identified multiple superior haplotypes associated with high yield and drought resistance, and have applied them in molecular breeding. In the future, the combination of multi-omics and AI will accelerate the discovery and aggregation of climate-adaptive haplotypes, promoting the development of rice varieties towards high yield, stable yield and climate resilience (Mahmood et al., 2022). Acknowledgments The authors appreciate the comments from two anonymous peer reviewers on the manuscript of this study. Conflict of Interest Disclosure The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Adam H., Gutiérrez A., Couderc M., Sabot F., Ntakirutimana F., Serret J., Orjuela J., Tregear J., Jouannic S., and Lorieux M., 2023, Genomic introgressions from African rice (Oryza glaberrima) in Asian rice (O. sativa) lead to the identification of key QTLs for panicle architecture, BMC Genomics, 24: 587. https://doi.org/10.1186/s12864-023-09695-6 Alam S., Sundaram K., Singh U., Prasad S., Laha G., Sinha P., and Singh V., 2024, Superior haplotypes towards the development of blast and bacterial blight-resistant rice, Frontiers in Plant Science, 15: 1272326. https://doi.org/10.3389/fpls.2024.1272326 Al-Daej M., Rezk A., El-Malky M., Shalaby T., and Ismail M., 2023, Comparative genetic diversity assessment and marker-trait association using two dna marker systems in rice (Oryza sativa L.), Agronomy, 13(2): 329. https://doi.org/10.3390/agronomy13020329 Anandan A., Nagireddy R., Sabarinathan S., Bhatta B., Mahender A., Vinothkumar M., Parameswaran C., Panneerselvam P., Subudhi H., Meher J., Bose L., and Ali J., 2022, Multi-trait association study identifies loci associated with tolerance of low phosphorus in Oryza sativa and its wild relatives, Scientific Reports, 12: 4089. https://doi.org/10.1038/s41598-022-07781-5 Ashfaq M., Rasheed A., Zhu R., Ali M., Javed M., Anwar A., Tabassum J., Shaheen S., and Wu X., 2023, Genome-wide association mapping for yield and yield-related traits in rice (Oryza Sativa L.) using SNPs markers, Genes, 14(5): 1089. https://doi.org/10.3390/genes14051089 Ata-Ul-Karim S., Begum H., Lopena V., Borromeo T., Virk P., Hernández J., Gregorio G., Collard B., and Kato Y., 2022, Genotypic variation of yield-related traits in an irrigated rice breeding program for tropical Asia, Crop and Environment, 1(3): 173-181. https://doi.org/10.1016/j.crope.2022.08.004 Aung K., Sang-Ho C., Nawade B., Lee C., Myung E., and Park Y., 2024, Analyzing the response of rice to tefuryltrione herbicide: haplotype variation and evolutionary dynamics of the HIS1gene, Environmental Research, 252: 118839. https://doi.org/10.1016/j.envres.2024.118839 Bejjam K., and Basuthkar U., 2024, GPOSYSH: genomic prediction of Oryza sativa yield and subpopulation using hybrid methods, Recent Advances in Food, Nutrition and Agriculture, 16: 57-69. https://doi.org/10.2174/012772574X281849240130120235
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