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Genetic structure and diversity of upland rice germplasm using diversity array technology (DArT)-based single nucleotide polymorphism (SNP) markers

Published online by Cambridge University Press:  04 November 2020

Kehinde A. Adeboye Open the ORCID record for Kehinde A. Adeboye [Opens in a new window] ,
Olayinka E. Oyedeji ,
Ahmad M. Alqudah ,
Andreas Börner ,
Olusegun Oduwaye ,
Olutumininu Adebambo  and
Isaac O. Daniel
Kehinde A. Adeboye*
Affiliation:
Center of Excellence in Agricultural Development and Sustainable Environment (CEADESE), Federal University of Agriculture, Abeokuta, Nigeria Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland/OT Gatersleben, Germany
Olayinka E. Oyedeji
Affiliation:
Center of Excellence in Agricultural Development and Sustainable Environment (CEADESE), Federal University of Agriculture, Abeokuta, Nigeria Department of Agricultural, Food and Nutritional Sciences, Agriculture/Forestry Centre, University of Alberta, Edmonton, Canada
Ahmad M. Alqudah
Affiliation:
Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland/OT Gatersleben, Germany
Andreas Börner
Affiliation:
Genebank Department, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland/OT Gatersleben, Germany
Olusegun Oduwaye
Affiliation:
Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta, Nigeria
Olutumininu Adebambo
Affiliation:
Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta, Nigeria
Isaac O. Daniel
Affiliation:
Department of Plant Breeding and Seed Technology, Federal University of Agriculture, Abeokuta, Nigeria
*
*Corresponding author. E-mail: kaadeboye@yahoo.com
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Abstract

Investigating genetic structure and diversity is crucial for rice improvement strategies, including mapping quantitative trait loci with the potential for improved productivity and adaptation to the upland ecology. The present study elucidated the population structure and genetic diversity of 176 rice germplasm adapted to the upland ecology using 7063 genome-wide single nucleotide polymorphism (SNP) markers from the Diversity Array Technology (DArT)-based sequencing platform (DArTseq). The SNPs were reasonably distributed across the rice genome, ranging from 432 SNPs on chromosome 9 to 989 SNPs on chromosome 1. The minimum minor allele frequency was 0.05, while the average polymorphism information content and heterozygosity were 0.25 and 0.03, respectively. The model-based (Bayesian) population structure analysis identified two major groups for the studied rice germplasm. Analysis of molecular variance revealed that all (100%) of the genetic variance was attributable to differences within the population, and none was attributable to the population structure. The estimates of genetic differentiation (PhiPT = 0.001; P = 0.197) further showed a negligible difference among the population structures. The results indicated a high genetic exchange or gene flow (number of migrants, Nm = 622.65) and a substantial level of diversity (number of private alleles, Pa = 1.52 number of different alleles, Na = 2.67; Shannon's information index, I = 0.084; and percentage of polymorphic loci, %PPL = 55.9%) within the population, representing a valuable resource for rice improvement. The findings in this study provide a critical analysis of the genetic diversity of upland rice germplasm that would be useful for rice yield improvement. We suggested further breeding and genetic analyses.

Keywords

genetic differentiationgene flowupland ecology
Type
Research Article
Information
Plant Genetic Resources , Volume 18 , Issue 5 , October 2020 , pp. 343 - 350
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press on behalf of NIAB

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References

Alluri, K, Mahsatra, IC and Lawson, TL (1979) Production constraints for upland rice in West Africa. Paper presented at the First Annual Research Conference in Soil and Climatic Resources and Constraints in Relation to Food Crop Production in West Africa, IITA, Ibadan, Nigeria.Google Scholar
Anderson, CA, Pettersson, FH, Clarke, GM, Cardon, LR, Morris, AP and Zondervan, KT (2010) Data quality control in genetic case-control association studies. Nature Protocols 5: 1564–1573.CrossRefGoogle ScholarPubMed
Arora, A, Kundu, S, Dilbaghi, N, Sharma, I and Tiwari, R (2014) Population structure and genetic diversity among Indian wheat varieties using microsatellite (SSR) markers. Australian Journal of Crop Science 8: 1281–1289.Google Scholar
Beissinger, TM, Hirsch, CN, Sekhon, RS, Foerster, JM, Johnson, JM, Muttoni, G, Vaillancourt, B, Buell, CR, Kaeppler, SM and de Leon, N (2013) Marker density and read depth for genotyping populations using genotyping-by-sequencing. Genetics 193: 1073–1081.CrossRefGoogle ScholarPubMed
Botstein, D, White, RL, Skolnick, M and Davis, RW (1980) Construction of a genetic linkage map in man using restriction fragment length polymorphisms. American Journal of Human Genetics 32: 314–331.Google ScholarPubMed
Bradbury, PJ, Zhang, Z, Kroon, DE, Casstevens, TM, Ramdoss, Y and Buckler, ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics (Oxford, England) 23: 2633–2635.CrossRefGoogle ScholarPubMed
Breseghello, F and Sorrells, ME (2006) Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172: 1165–1177.CrossRefGoogle ScholarPubMed
Buckler, ES and Thornsberry, JM (2002) Plant molecular diversity and applications to genomics. Current Opinion in Plant Biology 5: 107–111.CrossRefGoogle ScholarPubMed
Ceccarelli, S, Grando, S, Maatougui, M, Michael, M, Slash, M, Haghparast, R, Rahmanian, M, Taheri, A, Al-Yassin, A, Benbelkacem, A, Labdi, M, Mimoun, H and Nachit, M (2010) Plant breeding and climate changes. The Journal of Agricultural Science 148: 627–637.CrossRefGoogle Scholar
Chao, S, Zhang, W, Akhunov, E, Sherman, J, Ma, Y, Luo, MC and Dubcovsky, J (2009) Analysis of gene-derived SNP marker polymorphism in US wheat (Triticum aestivum L.) cultivars. Molecular Breeding 23: 23–33.CrossRefGoogle Scholar
Chen, J, Zavala, C, Ortega, N, Petroli, C, Franco, J, Burgueño, J, Costich, DE and Hearne, SJ (2016) The development of quality control genotyping approaches: a case study using elite maize lines. PLoS ONE 11: e0157236.CrossRefGoogle ScholarPubMed
Coates, BS, Sumerford, DV, Miller, NJ, Kim, KS, Sappington, TW, Siegfried, BD, Siegfried, BD and Lewis, LC (2009) Comparative performance of single nucleotide polymorphism and microsatellite markers for population genetic analysis. Journal of Heredity 100: 556–564.CrossRefGoogle ScholarPubMed
Cobb, JN, Declerck, G, Greenberg, A, Clark, R and McCouch, S (2013) Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics 126: 867–887.CrossRefGoogle ScholarPubMed
Cong, L, Ran, FA, Cox, D, Lin, S, Barretto, R, Habib, N, Hsu, PD, Wu, X, Jiang, W, Marraffini, LA and Zhang, F (2013) Multiplex genome engineering using CRISPR/Cas systems. Science (New York, N.Y.) 339: 819–823.CrossRefGoogle ScholarPubMed
Diagne, A, Amovin-Assagba, E, Futakuchi, K, and Wopereis, MCS (2013) Estimation of cultivated area, number of farming households and yield for major rice-growing environments in Africa. In: Wopereis MCS, Johnson DE, Ahimadi N, Tollens E, and Jalloh A (Eds.), Realizing Africa's Rice Promise. Wallingford: CAB Publishing, pp. 35–45.CrossRefGoogle Scholar
Dreccer, MF, Bonnett, D and Lafarge, T (2013) Plant breeding under a changing climate. In: Christou, P, Savin, R, Costa-Pierce, BA, Misztal, I and Whitelaw, CBA (eds) Sustainable Food Production. New York: Springer, pp. 1296–1307.CrossRefGoogle Scholar
Earl, DA and vonHoldt, BM (2012) Structure harvester: a website and program for visualizing structure output and implementing the Evanno method. Conservation Genetic Resources 4: 359–361.CrossRefGoogle Scholar
Egea, LA, Mérida-García, R, Kilian, A, Hernandez, P and Dorado, G (2017) Assessment of genetic diversity and structure of large garlic (Allium sativum) germplasm bank, by diversity arrays technology ‘genotyping-by-sequencing’ platform (DArTseq). Frontiers in Genetics 8: 98.10.3389/fgene.2017.00098CrossRefGoogle Scholar
Eltaher, S, Sallam, A, Belamkar, V, Emara, HA, Nower, AA, Salem, KFM, Poland, J and Baenziger, PS (2018) Genetic diversity and population structure of F3:6 Nebraska winter wheat genotypes using genotyping-by-sequencing. Frontiers in Genetics 9: 76.CrossRefGoogle ScholarPubMed
Fischer, MC, Rellstab, C, Leuzinger, M, Roumet, M, Gugerli, F, Shimizu, KK, Holderegger, R and Widmer, A (2017) Estimating genomic diversity and population differentiation – an empirical comparison of microsatellite and SNP variation in Arabidopsis halleri. BMC Genomics 18: 69.CrossRefGoogle ScholarPubMed
Garris, AJ, Tai, TH, Coburn, J, Kresovich, S and McCouch, S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169: 1631–1638.10.1534/genetics.104.035642CrossRefGoogle ScholarPubMed
Gassner, A, Harris, D, Mausch, K, Terheggen, A, Lopes, C, Finlayson, R and Dobie, P (2019) Poverty eradication and food security through agriculture in Africa: rethinking objectives and entry points. Outlook on Agriculture 48: 309–315.CrossRefGoogle ScholarPubMed
Guo, C, McDowell, IC, Nodzenski, M, Scholtens, DM, Allen, AS, Lowe, WL and Reddy, TE (2017) Transversions have larger regulatory effects than transitions. BMC Genomics 18: 394.CrossRefGoogle ScholarPubMed
Huang, SM, Deng, LB, Guan, M, Li, J, Lu, K, Wang, HZ, Fu, D, Mason, AS, Liu, S and Hua, W (2013) Identification of genome-wide single nucleotide polymorphisms in allopolyploid crop Brassica napus. BMC Genomics 14: 717.CrossRefGoogle ScholarPubMed
Hsu, PD, Lander, ES and Zhang, F (2014) Development and applications of CRISPR-Cas9 for genome engineering. Cell 157: 1262–1278.10.1016/j.cell.2014.05.010CrossRefGoogle ScholarPubMed
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
Lamkey, KR and Lee, M (Eds.) (2006) Plant Breeding: The Arnel. R. Hallauer International Symposium. Ames: Blackwell Publishing, pp. 293–309.CrossRefGoogle Scholar
Lenaerts, B, Collard, BCY and Demont, M (2019) Improving global food security through accelerated plant breeding. Plant Science 287: 110207.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 in Plant Science 10: 184.CrossRefGoogle ScholarPubMed
McCouch, S, Sweeney, M, Li, J, Jiang, H, Thomson, M, Septiningsih, E, Edwards, J, Moncada, P, Xiao, J and Garris, A (2007) Through the genetic bottleneck: O. rufipogon as a source of trait-enhancing alleles for O. sativa. Euphytica 154: 317–339.CrossRefGoogle Scholar
McCouch, S, Wing, RA, Semon, M, Venuprasad, R, Atlin, G, Sorrells, ME and Jannink, JL (2013) Making rice genomics work for Africa. In: Wopereis, MCS, Johnson, DE, Ahmadi, N, Tollens, E and Jalloh, A (eds.) Realizing Africa's rice promise. Wallingford: CABI Publishing, pp 108–129.CrossRefGoogle Scholar
Morton, BR, Bi, IV, McMullen, MD and Gaut, BS (2006) Variation in mutation dynamics across the maize genome as a function of regional and flanking base composition. Genetics 172: 569–577.CrossRefGoogle ScholarPubMed
Nazzicari, N, Biscarini, F, Cozzi, P, Brummer, EC and Annicchiarico, P (2016) Marker imputation efficiency for genotyping-by-sequencing data in rice (Oryza sativa) and alfalfa (Medicago sativa). Molecular Breeding 36: 69.CrossRefGoogle Scholar
Ndjiondjop, MN, Semagn, K, Gouda, AC, Kpeki, SB, Dro Tia, D, Sow, M, Goungoulou, A, Sie, M, Perrier, X, Ghesquiere, A and Warburton, ML (2017) Genetic variation and population structure of Oryza glaberrima and development of a mini-core collection using DArTseq. Frontiers in Plant Science 8: 1748.10.3389/fpls.2017.01748CrossRefGoogle ScholarPubMed
Ndjiondjop, MN, Semagn, K, Sow, M, Manneh, B, Gouda, AC, Kpeki, SB, Pegalepo, E, Wambugu, P, Sié, M and Warburton, ML (2018) Assessment of genetic variation and population structure of diverse rice genotypes adapted to lowland and upland ecologies in Africa using SNPs. Frontiers in Plant Science 9: 446.CrossRefGoogle ScholarPubMed
Nuijten, E and Richards, P (2013) Gene flow in farmers’ field. In: Wopereis, MCS, Johnson, DE, Ahmadi, N, Tollens, E and Jalloh, A (eds) Realizing Africa's Rice Promise. Wallingford: CABI Publishing, pp 95-107.10.1079/9781845938123.0095CrossRefGoogle Scholar
Oikeh, SO, Nwilene, FE, Agunbiade, FA, Oladimeji, O, Ajayi, O, Mande, S, Tsunematsu, H and Samejima, H (2008) Growing Upland Rice: A Production Handbook. Africa Rice Center, Benin, p. 5.Google Scholar
Park, S, Yu, HJ, Mun, JH and Lee, SC. (2010). Genome-wide discovery of DNA polymorphism in Brassica rapa. Molecular Genetics and Genomics 283: 135–145.CrossRefGoogle ScholarPubMed
Peakall, R and Smouse, PE (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research – an update. Bioinformatics (Oxford, England) 28: 2537–2539.CrossRefGoogle ScholarPubMed
Poland, JA and Rife, TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genetics 5: 92–102.Google Scholar
Pootakham, W, Shearman, JR, Ruang-areerate, P, Sonthirod, C, Sangsrakru, D, Jomchai, N, Yoocha, T, Triwitayakorn, K, Tragoonrung, S and Tangphatsornruang, S (2014) Large-scale SNP discovery through DNA sequencing and SNP genotyping by target enrichment sequencing in cassava (Manihot esculenta Crantz). PLoS ONE 9: e116028.CrossRefGoogle Scholar
Pritchard, JK, Stephens, M and Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945–959.Google ScholarPubMed
Ramadan, E, Elmoghazy, A and Mowafi, H (2015) Molecular markers based genetic diversity analysis for drought tolerance in rice (Oryza sativa, L) using SSR markers. International Journal of Scientific Research in Agricultural Sciences 2: 137–146.Google Scholar
Ren, R, Ray, R, Li, P, Xu, J, Zhang, M, Liu, G, Yao, X, Kilian, A and Yang, K (2015) Construction of a high-density DArTseq SNP-based genetic map and identification of genomic regions with segregation distortion in a genetic population derived from a cross between feral and cultivated-type watermelon. Molecular Genetics and Genomics 290: 1457–1470.CrossRefGoogle Scholar
Saito, K, Asai, H, Zhao, D, Laborte, AG and Grenier, C (2018) Progress in varietal improvement for increasing upland rice productivity in the tropics. Plant Production Science 21: 145–158.CrossRefGoogle Scholar
Saito, K, Sokei, Y and Wopereis, MCS (2012) Enhancing rice productivity in West Africa through genetic improvement. Crop Science 52: 484–494.CrossRefGoogle Scholar
Sansaloni, C, Petroli, C, Jaccoud, D, Carling, J, Detering, F, Grattapaglia, D and Kilian, A (2011) Diversity Arrays Technology (DArT) and next-generation sequencing combined: genome-wide, high throughput, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proceedings 5: P54.CrossRefGoogle Scholar
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
Tanksley, SD and McCouch, S (1997) Seed banks and molecular maps: unlocking genetic potentials from the wild. Science (New York, N.Y.) 277: 1063–1066.CrossRefGoogle Scholar
Tollens, E, Demont, M, Sié, M, Diagne, A, Saito, K and Wopereis, MCS (2013) From WARDA to AfricaRice: an overview of rice research for development activities conducted in partnership in Africa. In: Wopereis, MCS, Johnson, DE, Ahmadi, N, Tollens, E and Jalloh, A (eds) Realizing Africa's Rice Promise. Wallingford: CABI Publishing, pp. 188–203.Google Scholar
Venuprasad, R, Dalid, CO, Del Valle, M, Zhao, D, Espiritu, M, Sta Cruz, MT, Amante, M, Kumar, A and Atlin, GN (2009) Identification and characterization of large-effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk-segregant analysis. Theoretical and Applied Genetics 120: 177–190.CrossRefGoogle ScholarPubMed
Venuprasad, R, Impa, SM, VowdaGowda, RP, Atlin, GN and Serraj, R (2011) Rice near-isogenic-lines (NILs) contrasting for grain yield under lowland drought stress. Field Crops Research 123: 38–46.10.1016/j.fcr.2011.04.009CrossRefGoogle Scholar
Wassmann, R, Jagadish, S, Heuer, S, Ismail, A, Redona, E, Serraj, R, Singh, RK, Howell, G, Pathak, H and Sumfleth, K (2009) Climate change affecting rice production: the physiological and agronomic basis for possible adaptation strategies. Advances in Agronomy 101: 59–122.CrossRefGoogle Scholar
Wright, S (1978) Evolution and the Genetics of Populations, Volume 4: Variability Within and Among Natural Populations. Chicago: University of Chicago Press.Google Scholar
Yamasaki, M and Ideta, O (2013) Population structure in Japanese rice population. Breeding Science 63: 49–57.CrossRefGoogle ScholarPubMed
Yang, X, Ren, R, Ray, R, Xu, J, Li, P, Zhang, M, Liu, G, Yao, X and Kilian, A (2016a) Genetic diversity and population structure of core watermelon (Citrullus lanatus) genotypes using DArTseq-based SNPs. Plant Genetic Resources 14: 226–233.CrossRefGoogle Scholar
Yang, H, Wei, CL, Liu, HW, Wu, JL, Li, ZG, Zhang, L, Jian, JB, Li, YY, Tai, YL, Zhang, J, Zhang, ZZ, Jiang, CJ, Xia, T and Wan, XC (2016b). Genetic divergence between Camellia sinensis and its wild relatives revealed via genome-wide SNPs from RAD sequencing. PLoS ONE 11: e0151424.CrossRefGoogle Scholar
Yang, G, Chen, S, Chen, L, Sun, K, Huang, C, Zhou, D, Huang, Y, Wang, J, Liu, Y, Wang, H, Chen, Z and Guo, T (2019) Development of a core SNP arrays based on the KASP method for molecular breeding of rice. Rice 12: 21.CrossRefGoogle ScholarPubMed
Zhao, K, Tung, CW, Eizenga, GC, Wright, MH, Ali, ML, Price, AH, Norton, GJ, Islam, MR, Reynolds, A, Mezey, J, McClung, AM, Bustamante, CD and McCouch, SR. (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa. Nature Communications 2: 467.CrossRefGoogle ScholarPubMed
Zhu, S, Niu, E, Shi, A and Mou, B (2019) Genetic diversity analysis of olive germplasm (Olea europaea L.) with genotyping-by-sequencing technology. Frontiers in Genetics 10: 755.10.3389/fgene.2019.00755CrossRefGoogle ScholarPubMed
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