Molecular Plant Breeding 2025, Vol.16, No.5, 268-277 http://genbreedpublisher.com/index.php/mpb 275 Bharamappanavara M., Chikkahosahalli M., Patil B., Vijjeswarapu A., Anantha M., Ramappa L., Diwan J., Nidagundi J., Mathada U., Talagunda S., Guddalahalli L., Byanna R., Rathod S., Sundaram R., Subbarao L., Rapolu M., and Gireesh C., 2023, Genetic analysis and identification of QTLs associated with yield-enhancing traits in Oryza sativa IR64 × Oryza glaberrima interspecific backcross populations, Crop and Pasture Science, 74: 1023-1036. https://doi.org/10.1071/CP22105 Bhat J., Yu D., Bohra A., Ganie S., and Varshney R., 2021, Features and applications of haplotypes in crop breeding, Communications Biology, 4: 1266. https://doi.org/10.1038/s42003-021-02782-y Cembrowska-Lech D., Krzemińska A., Miller T., Nowakowska A., Adamski C., Radaczyńska M., Mikiciuk G., and Mikiciuk M., 2023, An integrated multi-omics and artificial intelligence framework for advance plant phenotyping in horticulture, Biology, 12(10): 1298. https://doi.org/10.3390/biology12101298 Daryani P., Amirbakhtiar N., Soorni J., Loni F., Ramandi H., and Shobbar Z., 2024, Uncovering the genomic regions associated with yield maintenance in rice under drought stress using an integrated meta-analysis approach, Rice, 17: 7. https://doi.org/10.1186/s12284-024-00684-1 Demeke B., Dejene T., and Abebe D., 2022, Genetic variability, heritability, and genetic advance of morphological, yield related and quality traits in upland rice (Oryza sativa L.) genotypes at pawe, northwestern Ethiopia, Cogent Food and Agriculture, 9. https://doi.org/10.1080/23311932.2022.2157099 Hu H., Campbell M., Yeats T., Zheng X., Runcie D., Covarrubias-Pazaran G., Broeckling C., Yao L., Caffe-Treml M., Gutiérrez L., Smith K., Tanaka J., Hoekenga O., Sorrells M., Gore M., and Jannink J., 2021, Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations, Theoretical and Applied Genetics, 134: 4043-4054. https://doi.org/10.1007/s00122-021-03946-4 Huang J., Zhang Y., Li Y., Xing M., Lei C., Wang S., Nie Y., Wang Y., Zhao M., Han Z., Sun X., Zhou H., Wang Y., Zheng X., Xiao X., Fan W., Liu Z., Guo W., Zhang L., Cheng Y., Qian Q., He H., Yang Q., and Qiao W., 2024, Haplotype-resolved gapless genome and chromosome segment substitution lines facilitate gene identification in wild rice, Nature Communications, 15: 4573. https://doi.org/10.1038/s41467-024-48845-6 James M., Tyagi W., Magudeeswari P., Neeraja C., and Rai M., 2024, Genome-wide association-based identification of alleles, genes and haplotypes influencing yield in rice (Oryza sativa L.) under low-phosphorus acidic lowland soils, International Journal of Molecular Sciences, 25(21): 11673. https://doi.org/10.3390/ijms252111673 Khahani B., Tavakol E., Shariati V., and Fornara F., 2020, Genome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice, BMC Genomics, 21. https://doi.org/10.1186/s12864-020-6702-1 Kiranmai E., 2023, Comparative analysis association and prediction of various phenotypic traits of Oryza sativa, International Journal on Recent and Innovation Trends in Computing and Communication, 11(10): 1471-1480. https://doi.org/10.17762/ijritcc.v11i10.8697 Kulkarni S., Balachandran S., Ulaganathan K., Balakrishnan D., Praveen M., Prasad A., Fiyaz R., Senguttuvel P., Sinha P., Kale R., Rekha G., Kousik M., Harika G., Anila M., Punniakoti E., Dilip T., Hajira S., Pranathi K., Das M., Shaik M., Chaitra K., Rao P., Gangurde S., Pandey M., and Sundaram R., 2020, Molecular mapping of QTLs for yield related traits in recombinant inbred line (RIL) population derived from the popular rice hybrid KRH-2 and their validation through SNP genotyping, Scientific Reports, 10: 13695. https://doi.org/10.1038/s41598-020-70637-3 Li L., Wu X., Chen J., Wang S., Wan Y., Ji H., Wen Y., and Zhang J., 2022, Genetic dissection of epistatic interactions contributing yield-related agronomic traits in rice using the compressed mixed model, Plants, 11(19): 2504. https://doi.org/10.3390/plants11192504 Liu G., Qiu D., Lu Y., Wu Y., Han X., Jiao Y., Wang T., Yang J., You A., Chen J., and Zhang Z., 2023, Identification of superior haplotypes and haplotype combinations for grain size- and weight-related genes for breeding applications in rice (Oryza sativa L.), Genes, 14(12): 2201. https://doi.org/10.3390/genes14122201 Mahmood U., Li X., Fan Y., Chang W., Niu Y., Li J., Qu C., and Lu K., 2022, Multi-omics revolution to promote plant breeding efficiency, Frontiers in Plant Science, 13: 1062952. https://doi.org/10.3389/fpls.2022.1062952 Meena V., Thribhuvan R., Dinkar V., Bhatt A., Pandey S., Abhinav, Ahmad D., Kumar A., and Singh A., 2025, Haplotype breeding: fast-track the crop improvements, Planta, 261: 51. https://doi.org/10.1007/s00425-025-04622-3 Naqvi R., Mahmood M., Mansoor S., Amin I., and Asif M., 2024, Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops, Frontiers in Plant Science, 14: 1273859. https://doi.org/10.3389/fpls.2023.1273859 Otte K., and Schlötterer C., 2020, Detecting selected haplotype blocks in evolve and resequence experiments, Molecular Ecology Resources, 21: 93-109. https://doi.org/10.1111/1755-0998.13244 Padmashree R., Barbadikar K., Honnappa, Magar N., Balakrishnan D., Lokesha R., Gireesh C., Siddaiah A., Madhav M., Ramesha Y., Bharamappanavara M., Phule A., Senguttuvel P., Diwan J., Subrahmanyam D., and Sundaram R., 2023, Genome-wide association studies in rice germplasm reveal significant genomic regions for root and yield-related traits under aerobic and irrigated conditions, Frontiers in Plant Science, 14: 1143853. https://doi.org/10.3389/fpls.2023.1143853
RkJQdWJsaXNoZXIy MjQ4ODYzNA==