MPB_2024v15n5

Molecular Plant Breeding 2024, Vol.15, No.5, 295-307 http://genbreedpublisher.com/index.php/mpb 306 Maestri S., Maturo M., Cosentino E., Marcolungo L., Iadarola B., Fortunati E., Rossato M., and Delledonne M., 2020, A long-read sequencing approach for direct haplotype phasing in clinical settings, International Journal of Molecular Sciences, 21(23): 9177. https://doi.org/10.3390/ijms21239177 PMid:33271988 PMCid:PMC7731377 Meier J., Salazar P., Kučka M., Davies R., Dréau A., Aldás I., Power O., Nadeau N., Bridle J., Rolian C., Barton N., McMillan W., Jiggins C., and Chan Y., 2020, Haplotype tagging reveals parallel formation of hybrid races in two butterfly species, Proceedings of the National Academy of Sciences of the United States of America, 118(25): e2015005118. https://doi.org/10.1073/pnas.2015005118 PMid:34155138 PMCid:PMC8237668 Moeinzadeh M., Yang J., Muzychenko E., Gallone G., Heller D., Reinert K., Haas S., and Vingron M., 2020, Ranbow: a fast and accurate method for polyploid haplotype reconstruction, PLoS Computational Biology, 16(5): e1007843. https://doi.org/10.1371/journal.pcbi.1007843 PMid:32469863 PMCid:PMC7310859 Mussurova S., Al-Bader N., Zuccolo A., and Wing R., 2020, Potential of platinum standard reference genomes to exploit natural variation in the wild relatives of rice, Frontiers in Plant Science, 11: 579980. https://doi.org/10.3389/fpls.2020.579980 PMid:33072154 PMCid:PMC7539145 Nath S., and Kole P., 2021, Genetic variability and yield analysis in rice, Electronic Journal of Plant Breeding, 12(1): 253-258. https://doi.org/10.37992/2021.1201.039 Purugganan M., and Jackson S., 2021, Advancing crop genomics from lab to field, Nature Genetics, 53: 595-601. https://doi.org/10.1038/s41588-021-00866-3 PMid:33958781 Selvaraj R., Singh A., Singh V., Abbai R., Habde S., Singh U., and Kumar A., 2021, Superior haplotypes towards development of low glycemic index rice with preferred grain and cooking quality, Scientific Reports, 11: 10082. https://doi.org/10.1038/s41598-021-87964-8 PMid:33980871 PMCid:PMC8115083 Senguttuvel P., Sravanraju N., Jaldhani V., Divya B., Beulah P., Nagaraju P., Manasa Y., Prasad A., Brajendra P., Gireesh C., Anantha M., Suneetha K., Sundaram R., Madhav M., Tuti M., Subbarao L., Neeraja C., Bhadana V., Rao P., Voleti S., and Subrahmanyam D., 2021, Evaluation of genotype by environment interaction and adaptability in lowland irrigated rice hybrids for grain yield under high temperature, Scientific Reports, 11: 15825. https://doi.org/10.1038/s41598-021-95264-4 PMid:34349182 PMCid:PMC8338964 Shen L., Wang C., Fu Y., Wang J., Liu Q., Zhang X., Yan C., Qian Q., and Wang K., 2018, QTL editing confers opposing yield performance in different rice varieties, Journal of Integrative Plant Biology, 60: 89-93. https://doi.org/10.1111/jipb.12501 PMid:27628577 Singh A., Verma O., Singh A., and Choudhary A., 2022a, Association analysis for yield and its attributing components in rice (Oryza sativa L) under two environments, Journal of Agriculture Research and Technology, 1: 3-10. Singh G., Kaur N., Khanna R., Kaur R., Gudi S., Kaur R., Sidhu N., Vikal Y., and Mangat G., 2022b, 2Gs and plant architecture: breaking grain yield ceiling through breeding approaches for next wave of revolution in rice (Oryza sativa L.), Critical Reviews in Biotechnology, 44(1): 139-162. https://doi.org/10.1080/07388551.2022.2112648 PMid:36176065 Sinha P., Singh V., Saxena R., Khan A., Abbai R., Chitikineni A., Desai A., Molla J., Upadhyaya H., Kumar A., and Varshney R., 2020, Superior haplotypes for haplotype-based breeding for drought tolerance in pigeonpea (Cajanus cajan L.), Plant Biotechnology Journal, 18: 2482-2490. Sivabharathi R., Rajagopalan V., Suresh R., Sudha M., Karthikeyan G., Jayakanthan M., and Raveendran M., 2024, Haplotype-based breeding: a new insight in crop improvement, Plant Science, 346: 112129. https://doi.org/10.1016/j.plantsci.2024.112129 PMid:38763472 Sousa K., Etten J., Poland J., Fadda C., Jannink J., Kidane Y., Lakew B., Mengistu D., Pè M., Solberg S., and Dell’Acqua M., 2021, Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment, Communications Biology, 4: 944. Su J., Xu K., Li Z., Hu Y., Hu Z., Zheng X., Song S., Tang Z., and Li L., 2021, Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential, Scientific Reports, 11: 6894. https://doi.org/10.1038/s41598-021-86389-7 PMid:33767346 PMCid:PMC7994632 Thudi M., Palakurthi R., Schnable J., Chitikineni A., Dreisigacker S., Mace E., Srivastava R., Satyavathi C., Odeny D., Tiwari V., Lam H., Hong Y., Singh V., Li G., Xu Y., Chen X., Kaila S., Nguyen H., Sivasankar S., Jackson S., Close T., Shubo W., and Varshney R., 2020, Genomic resources in plant breeding for sustainable agriculture, Journal of Plant Physiology, 257: 153351. https://doi.org/10.1016/j.jplph.2020.153351 PMid:33412425 PMCid:PMC7903322

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