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Delineation of Bangladeshi coastal rice germplasm based on qualitative phenotypic traits

Published online by Cambridge University Press:  12 May 2022

Susmita Banik ,
Md. Golam Rasul ,
Nasrin Akter Ivy ,
M. Moynul Haque  and
Mehfuz Hasan Open the ORCID record for Mehfuz Hasan [Opens in a new window]
Susmita Banik
Affiliation:
Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Md. Golam Rasul
Affiliation:
Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Nasrin Akter Ivy
Affiliation:
Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
M. Moynul Haque
Affiliation:
Department of Agronomy, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Mehfuz Hasan*
Affiliation:
Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
*
Author for correspondence: Mehfuz Hasan, E-mail: [email protected]
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Abstract

A detailed study of rice genetic resources in Bangladesh's coastal areas is necessary. This understanding is a necessary requirement for its utilization in selective breeding. The study reports on the qualitative morphological trait-based assessment of 150 local rice samples collected from Bangladesh's coastal zone, including 50 advanced lines developed from coastal germplasm. Six of the thirteen analysed characters had a substantial gene contribution, whereas the average was 0.694. The most impressive diversity was in leaf blade intensity of green colour (LBIGC: 0.705). The total morpho-qualitative diversity was calculated to be 0.412. The character efficiency content ranged from 0.655 (LBIGC) to 0.136 (Leaf Sheath: Anthocyanin colouration, Leaf Blade: Presence/Absence, and Leaf Blade: Anthocyanin. Colouration). As per the morphological variance study, 93% of morphological changes were detected within individuals, whereas 7% were found in populations. The 150 germplasm samples were divided into four subpopulations using STRUCTURE-based population analysis. A moderate genotypic difference was detected amongst all groups, with an Fst value of 0.111. The G statistic backed up the record as well. The Shannon mutual information index reached a value of 1.252 between populations 2 and 3. In terms of gene exchange, the highest value was found between populations 3 and 4. Our data indicate a high degree of diversity in Bangladesh's coastline rice germplasm. The findings will aid in conferring the farmers' Intellectual Property Rights on the investigated rice germplasm.

Keywords

Characterizationdiversitymorphological variancepopulation structureShannon index
Type
Research Article
Information
Plant Genetic Resources , Volume 20 , Issue 1 , February 2022 , pp. 29 - 35
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of NIAB

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References

Agrama, HA, Eizenga, GC and Yan, W (2007) Association mapping of yield and its components in rice cultivars. Molecular Breeding 19, 341–356.CrossRefGoogle Scholar
Ali, LM, McClung, AM, Jia, MH, Kimball, JA, McCouch, SR and Georgia, CE (2011) A rice diversity panel evaluated for genetic and agro-morphological diversity between subpopulations and its geographic distribution. Crop Science 51, 2021–2035.Google Scholar
Ali, MA, Rasul, MG, Islam, AKMA, Ahmed, JU, Abdullah, HM, Jahan, T and Hasan, M (2020) Population differentiation of coastal rice germplasm of Bangladesh based on morpho-qualitative traits. International Journal of Business, Social and Scientific Research 8, 55–61.Google Scholar
An, JP, Zhang, XW, Bi, SQ, You, CX, Wang, XF and Hao, YJ (2020) The ERF transcription factor MdERF38 promotes drought stress-induced anthocyanin biosynthesis in apple. The Plant Journal 101, 573–589.CrossRefGoogle ScholarPubMed
Aravind, K, Banumathy, S, Vanniarajan, C, Arunachalam, P, Ilamaran, M and Kalpana, K (2019) DUS characterization and genetic variability studies of rice mutants. Electronic Journal of Plant Breeding 10, 451–461.CrossRefGoogle Scholar
Bai, S, Saito, T, Honda, C, Hatsuyama, Y, Ito, A and Moriguchi, T (2014) An apple B-box protein, MdCOL11, is involved in UV-B-and temperature-induced anthocyanin biosynthesis. Planta 240, 1051–1062.CrossRefGoogle ScholarPubMed
Bandumula, N (2018) Rice production in Asia: key to global food security. Proceedings of the National Academy of Sciences, India Section B 88, 1323–1328.CrossRefGoogle Scholar
Baten, MA, Seal, L and Lisa, KS (2015) Salinity intrusion in interior coast of Bangladesh: challenges to agriculture in south-central coastal zone. American Journal of Climate Change 4, 248.CrossRefGoogle Scholar
Bazrkar-Khatibani, L, Fakheri, BA, Hosseini-Chaleshtori, M, Mahender, A, Mahdinejad, N and Ali, J (2019) Genetic mapping and validation of Quantitative Trait Loci (QTL) for the grain appearance and quality traits in rice (Oryza sativa L.) by using Recombinant Inbred Line (RIL) population. International Journal of Genomics 2019, 1–13.CrossRefGoogle ScholarPubMed
Chen, H, He, H, Zou, Y, Chen, W, Yu, R, Liu, X, Yang, Y, Gao, YM, Xu, JL, Fan, LM and Li, Y (2011) Development and application of a set of breeder-friendly SNP markers for genetic analyses and molecular breeding of rice (Oryza sativa L.). Theoretical and Applied Genetics 123, 869.CrossRefGoogle Scholar
Das, B, Sengupta, S, Parida, SK, Roy, B, Ghosh, M, Prasad, M and Ghose, TK (2013) Genetic diversity and population structure of rice landraces from Eastern and North Eastern States of India. BMC Genetics 14, 71.CrossRefGoogle ScholarPubMed
Eltaher, S, Sallam, A, Belamkar, V, Emara, HA, Nower, AA, Salem, KF, Poland, J and Baenziger, PS (2018) Genetic diversity and population structure of F3:6 Nebraska winter wheat genotypes using genotyping-by-sequencing. Frontiers of Genetics 9, 76.CrossRefGoogle ScholarPubMed
Fan, X, Fan, B, Wang, Y and Yang, W (2016) Anthocyanin accumulation enhanced in Lc-transgenic cotton under light and increased resistance to bollworm. Plant Biotechnology Reports 10, 1–11.CrossRefGoogle ScholarPubMed
Frankham, R, Briscoe, DA and Ballou, JD (2002) Introduction to Conservation Genetics. Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
Gao, LZ (2003) The conservation of Chinese rice biodiversity: genetic erosion, ethnobotany and prospects. Genetic Resources and Crop Evolution 50, 17–32.CrossRefGoogle Scholar
Gao, J, Chen, B, Lin, H, Liu, Y, Wei, Y, Chen, F and Li, W (2020) Identification and characterization of the glutathione S-transferase (GST) family in radish reveals a likely role in anthocyanin biosynthesis and heavy metal stress tolerance. Gene 743, 144484.CrossRefGoogle ScholarPubMed
Haque, MA (2018) Variation in salinity through the soil profile in south coastal region of Bangladesh. Journal of Bangladesh Academy of Sciences 42, 11–23.CrossRefGoogle Scholar
Hartl, DL and Clark, AG (1997) Principles of Population Genetics, Vol. 116. Sunderland, MA: Sinauer Associates.Google Scholar
Hasibuzzaman, ASM, Islam, AKM, Miah, MG and Hasan, M (2020) Phylogeographic diversity and population structure of Carica papaya L. revealed through nuclear microsatellites. Brazilian Journal of Botany 43, 1–8.CrossRefGoogle Scholar
Huda, MN, Hasan, M, Abdullah, HM and Sarker, U (2019) Spatial distribution and genetic diversity of wild date palm (Phoenix sylvestris) growing in coastal Bangladesh. Tree Genetics and Genomes 15, 3.CrossRefGoogle Scholar
Imtiaz, M and Upadhyaya, HD (2010) Key access and utilization descriptors for chickpea genetic resources. Web Article 10, 1–6.Google Scholar
Islam, MZ, Khalequzzaman, M, Prince MFRK, Siddique MA, Rashid ESMH, Ahmed MSU, Pittendrigh BR and Ali MP (2018) Diversity and population structure of red rice germplasm in Bangladesh. PLoS One 13, e0196096.CrossRefGoogle ScholarPubMed
Jahan, T, Ali, MA, Raihan, MS, Rahman, MM, Abdullah, HM, Huda, MN and Hasan, M (2020) Indigenous Mota-named coarse rice germplasm is distinct from fine-grained rice collected from south-central coastal Bangladesh as compared with the morphological descriptors and molecular markers. Brazilian Journal of Botany 413, 933–945.CrossRefGoogle Scholar
Jin, L, Lu, Y, Xiao, P, Sun, M, Corke, H and Bao, J (2010) Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theoretical and Applied Genetics 121, 475–487.CrossRefGoogle ScholarPubMed
Kinoshita, N, Kato, M, Koyasaki K, Kawashima T, Nishimura T, Hirayama Y, Takamure I, Sato T and Kato K (2017) Identification of quantitative trait loci for rice grain quality and yield-related traits in two closely related Oryza sativa L. subsp. japonica cultivars grown near the northernmost limit for rice paddy cultivation. Breeding Science, 67, 191–206.CrossRefGoogle ScholarPubMed
Kumar, S, Bisht, IS and Bhat, KV (2010) Population structure of rice (Oryza sativa) landraces under farmer management. Annals of Applied Biology 156, 137–146.CrossRefGoogle Scholar
Li, X, Yan, W, Agrama, H, Hu, B, Jia, L, Jia, M, Jackson, A, Moldenhauer, K, McClung, A and Wu, D (2010) Genotypic and phenotypic characterization of genetic differentiation and diversity in the USDA rice mini-core collection. Genetica 138, 1221–1230.CrossRefGoogle ScholarPubMed
Lisa, LA, Seraj, ZI, Elahi, F, Das, KC, Biswas, K, Islam, MR, Salam, MA and Gomosta, AR (2004) Genetic variation in microsatellite DNA, physiology and morphology of coastal saline rice (Oryza sativa L.) landraces of Bangladesh. Plant and Soil 263, 213–228.CrossRefGoogle Scholar
Liu, K and Muse, SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics (Oxford, England) 21, 2128–2129.CrossRefGoogle ScholarPubMed
Luo, Z, Brock, J, Dyer, JM, Kutchan, T, Schachtman, D, Augustin, M, Ge, Y, Fahlgren, N and Abdel-Haleem, H (2019) Genetic diversity and population structure of a Camelina sativa spring panel. Frontiers of Plant Science 10, 184.CrossRefGoogle ScholarPubMed
Mekonnen, A, Tessema, A, Ganewo, Z and Haile, A (2021) Climate change impacts on household food security and adaptation strategies in southern Ethiopia. Food and Energy Security 10, e266.CrossRefGoogle Scholar
Mishra, A, Kumar, P, Shamim, M, Tiwari, KK, Fatima, P, Srivastava, D, Singh, R and Yadav, P (2019) Genetic diversity and population structure analysis of Asian and African aromatic rice (Oryza sativa L.) genotypes. Journal of Genetics 98, 92.CrossRefGoogle ScholarPubMed
Modi, AT and Bornman, CH (2004) Short-term preservation of maize landrace seed and taro propagules using indigenous storage methods. South African Journal of Botany 70, 16–23.CrossRefGoogle Scholar
Mustafa, G, Akhtar, MS and Abdullah, R (2019) Global concern for salinity on various agro-ecosystems. In Pitman, MG and Läuchli, A (eds), Salt Stress, Microbes, and Plant Interactions: Causes and Solution. Eds. Michael G. Pitman and André Läuchli. Singapore: Springer, pp. 1–19.Google Scholar
Nachimuthu, VV, Muthurajan, R, Duraialaguraja, S, Sivakami, R, Pandian, BA, Ponniah, G, Gunasekaran, K, Swaminathan, M, Suji, KK and Sabariappa, R (2015) Analysis of population structure and genetic diversity in rice germplasm using SSR markers: an initiative towards association mapping of agronomic traits in Oryza sativa. Rice 8, 30.CrossRefGoogle ScholarPubMed
Nelson, GC, Rosegrant, MW, Koo, J, Robertson, R, Sulser, T, Zhu, T, Ringler, C, Msangi, S, Palazzo, A, Batka, M and Magalhaes, M (2009) Climate change: impact on agriculture and costs of adaptation. International Food Policy Research Institute 21, 1–30.Google Scholar
Oladosu, Y, Rafii, MY, Abdullah N, Abdul Malek M, Rahim HA, Hussin G, Abdul Latif M and Kareem I (2014) Genetic variability and selection criteria in rice mutant lines as revealed by quantitative traits. The Scientific World Journal 2014, 1–12.CrossRefGoogle ScholarPubMed
Pachauri, V, Taneja, N, Vikram, P, Singh, NK and Singh, S (2013) Molecular and morphological characterization of Indian farmers rice varieties (Oryza sativa L.). Australian Journal of Crop Science 7, 923.Google Scholar
Park, SG, Park, HS, Baek, MK, Baek, MK, Jeong, JM, Cho, YC, Lee, GM, Lee, CM, Suh, JP, Kim, CS and Kim, SM (2019) Improving the glossiness of cooked rice, an important component of visual rice grain quality. Rice 12, 87.CrossRefGoogle ScholarPubMed
Peakall, ROD and Smouse, PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Resources 6, 288–295.Google Scholar
Perdereau, AC, Kelleher, CT, Douglas, GC and Hodkinson, TR (2014) High levels of gene flow and genetic diversity in Irish populations of Salix caprea L. inferred from chloroplast and nuclear SSR markers. BMC Plant Biology 14, 1–12.CrossRefGoogle ScholarPubMed
Pritchard, JK, Stephens, M and Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155, 945–959.CrossRefGoogle ScholarPubMed
Rahman, MA, Thomson, MJ, De Ocampo, M, Egdane, JA, Salam, MA, Shah-E-Alam, M and Ismail, AM (2019) Assessing trait contribution and mapping novel QTL for salinity tolerance using the Bangladeshi rice landrace capsule. Rice 12, 63.CrossRefGoogle ScholarPubMed
Roy, S, Marndi, BC, Mawkhlieng, B, Banerjee, A, Yadav, RM, Misra, AK and Bansal, KC (2016) Genetic diversity and structure in hill rice (Oryza sativa L.) landraces from the North-Eastern Himalayas of India. BMC Genetics 17, 107.CrossRefGoogle Scholar
Seraj, ZI, Lisa, LA, Islam, MR, Begum, R and Das, DK (2006) Genetic diversity of saline coastal rice (Oryza sativa L.) landraces of Bangladesh. In Ashwani, K and Takabe, R (eds), Abiotic Stress Tolerance in Plants. Dordrecht: Springer, pp. 229–244.CrossRefGoogle Scholar
Sharifi, P and Ebadi, AA (2018) Relationships of rice yield and quality based on genotype by trait (GT) biplot. Anais da Academia Brasileira de Ciências 90, 343–356.CrossRefGoogle ScholarPubMed
Shete, S, Tiwari, H and Elston, RC (2000) On estimating the heterozygosity and polymorphism information content value. Theoretical population biology 57, 265–271.CrossRefGoogle ScholarPubMed
Sivankalyani, V, Feygenberg, O, Diskin, S, Wright, B and Alkan, N (2016) Increased anthocyanin and flavonoids in mango fruit peel are associated with cold and pathogen resistance. Postharvest Biology and Technology 111, 132–139.CrossRefGoogle Scholar
Thorslund, J, Bierkens, MF, Essink, GHO, Sutanudjaja, EH and van Vliet, MT (2021) Common irrigation drivers of freshwater salinisation in river basins worldwide. Nature Communications 12, 1–13.CrossRefGoogle ScholarPubMed
Umakanth, B, Vishalakshi, B, Sathish Kumar, P, Rama Devi, SJS, Bhadana, VP, Senguttuvel, P, Kumar, S, Sharma, SK, Sharma PK, Prasad, MS and Madhav, MS (2017) Diverse rice landraces of North-East India enables the identification of novel genetic resources for magnaporthe resistance. Frontiers of Plant Science 8, 1500.CrossRefGoogle ScholarPubMed
Van den Ende, W and El-Esawe, SK (2014) Sucrose signaling pathways leading to fructan and anthocyanin accumulation: a dual function in abiotic and biotic stress responses? Environmental and Experimental Botany 108, 4–13.CrossRefGoogle Scholar
Wallace, TC and Giusti, MM (2019) Anthocyanins – nature's bold, beautiful, and health-promoting colors. Foods (basel, Switzerland) 8, 550.Google ScholarPubMed
Wang, X, Pang, Y, Wang, C, Chen, K, Zhu, Y, Shen, C, Ali, J, Xu, J and Li, Z (2017) New candidate genes affecting rice grain appearance and milling quality detected by genome-wide and gene-based association analyses. Frontiers of Plant Science 7, 1998.CrossRefGoogle ScholarPubMed
Wright, S (1965) The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420.CrossRefGoogle Scholar
Zhao, X, Zhou, L, Ponce, K and Ye, G (2015) The usefulness of known genes/QTLs for grain quality traits in an indica population of diverse breeding lines tested using association analysis. Rice 8, 29.CrossRefGoogle Scholar
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