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Genetic diversity and population structure in a rice drought stress panel

Published online by Cambridge University Press:  12 September 2014

Dindo A. Tabanao*
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Arnel E. Pocsedio
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Jonalyn C. Yabes
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Marjohn C. Niño
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Reneth A. Millas
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Neah Rosandra L. Sevilla
Affiliation:
Plant Breeding and Biotechnology Division, Philippine Rice Research Institute, Maligaya, Science City of Muñoz 3119, Nueva Ecija, Philippines
Xiao Yulong
Affiliation:
Jiangxi Academy of Agricultural Science, No. 602 Nanlian Road, Nanchang, Jiangxi, P. R. China
Jianming Yu
Affiliation:
Department of Agronomy, Iowa State University, Ames, IA50011, USA
*
*Corresponding author. E-mail: [email protected]

Abstract

A drought stress panel composed of diverse accessions selected from upland, aerobic, rainfed lowland and irrigated lowland environments, was assembled to serve as germplasm for aerobic adaptation breeding. Aerobic rice requires significant levels of tolerance to drought stress due to intermittent water deficit and high soil impedance caused by aerobic conditions. Genomic information may be utilized to investigate the nature of the panel to guide varietal improvement. Using 153 simple sequence repeat and 384 single nucleotide polymorphism markers, the aim of the study was to compare the allelic properties of the two marker types, infer population structure of the panel, and estimate kinship among the accessions. There was a general agreement between the results derived from the two marker types. Marker alleles were found to occur at low frequencies, as the panel was composed mostly of improved accessions with some landraces. The panel clustered into japonica (JA), aus (AU), upland-adapted indica (UL) and lowland-adapted indica (LL) subpopulations. The AU and JA subpopulations were more divergent from the rest of the subpopulations than were the LL and UL subpopulations. Average marker-based kinship for related accessions was less than 0.20, indicating a low degree of genetic relatedness in the panel. Within the LL and UL subpopulations, the low levels of kinship imply that there is still much genetic gain to be expected from utilizing the accessions in breeding. Thus, an understanding of the genetic variation in the panel suggests focusing on improving the mean in the short term, and tapping into the exotic alleles from the AU and JA subpopulations when genetic gain declines.

Type
Research Article
Copyright
Copyright © NIAB 2014 

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References

Abdurakhmonov, IY and Abdukarimov, A (2008) Application of association mapping to understand the genetic diversity of plant germplasm resources. International Journal of Plant Genomics 2008: 118.CrossRefGoogle ScholarPubMed
Agrama, HA, Eizenga, GC and Yan, W (2007) Association mapping of yield and its components in rice cultivars. Molecular Breeding 19: 341356.Google Scholar
Anderson, JA, Churchill, GA, Autrique, JE, Tanksley, SD and Sorrells, ME (1993) Optimizing parental selection for genetic linkage maps. Genome 36: 181186.Google Scholar
Atlin, GN, Lafitte, HR, Tao, D, Laza, M, Amante, M and Courtois, B (2006) Developing rice cultivars for high-fertility upland systems in the Asian tropics. Field Crops Research 97: 4352.Google Scholar
Bernier, J, Kumar, A, Venuprasad, R, Spaner, D and Atlin, G (2007) A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Science 47: 507518.Google Scholar
Bernier, J, Atlin, G, Serraj, R, Kumar, A and Spaner, D (2008) Breeding upland rice for drought resistance. Journal of the Science of Food and Agriculture 88: 927939.CrossRefGoogle Scholar
Collard, BCY, Jahufer, MZZ, Brouwer, JB and Pang, ECK (2005) An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: the basic concepts. Euphytica 142: 169196.Google Scholar
Courtois, B, Frouin, J, Greco, R, Bruschi, G, Droc, G, Hamelin, C, Ruiz, M, Clément, G, Evrard, JC, Coppenole, S, Katsantonis, D, Oliveira, M, Negrão, S, Matos, C, Cavigiolo, S, Lupotto, E, Piffanelli, P and Ahmadi, N (2012) Genetic diversity and population structure in a European Collection of Rice. Crop Science 52: 16631675.Google Scholar
Dixit, S, Swamy, BPM, Vikram, P, Ahmed, HU, Sta Cruz, MT, Amante, M, Atri, D, Leung, H and Kumar, A (2012) Fine mapping of QTLs for rice grain yield under drought reveals sub-QTLs conferring a response to variable drought severities. Theoretical and Applied Genetics 125: 155169.CrossRefGoogle ScholarPubMed
Earl, DA and vonHoldt, BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources 4: 359361.Google Scholar
Evanno, G, Regnaut, S and Goudet, J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology 14: 26112620.CrossRefGoogle ScholarPubMed
Excoffier, L, Laval, G and Schneider, S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online 1: 4750.Google Scholar
Falush, D, Stephens, M and Pritchard, JK (2003) Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164: 15671587.CrossRefGoogle ScholarPubMed
Frascaroli, E, Schrag, TA and Melchinger, AE (2013) Genetic diversity analysis of elite Europen maize (Zea mays L.) inbred lines using AFLP, SSR and SNP markers reveals ascertainment bias for a subset of SNPs. Theoretical and Applied Genetics 126: 133141.Google Scholar
Garris, AJ, Tai, TH, Coburn, J, Kresovich, S and McCouch, S (2005) Genetic structure and diversity in Oryza sativa L. Genetics 169: 16311638.CrossRefGoogle ScholarPubMed
Giarocco, LE, Marassi, MA and Salerno, GL (2007) Assessment of the genetic diversity in Argentine rice cultivars with SSR markers. Crop Science 47: 853860.Google Scholar
Hamblin, MT, Warburton, ML and Buckler, ED (2007) Empirical comparison of simple sequence repeats and single nucleotide polymorphisms in assessment of maize diversity and relatedness. PLoS ONE 2(12): e1367.CrossRefGoogle ScholarPubMed
Hardy, OJ and Vekemans, X (2002) SPAGeDi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes 2: 618620.Google Scholar
Huang, X, Wei, X, Sang, T, Zhao, Q, Feng, Q, Zhao, Y, Li, C, Zhu, C, Lu, T, Zhang, Z, Li, M, Fan, D, Guo, Y, Wang, A, Wang, L, Deng, L, Li, W, Lu, Y, Weng, Q, Liu, K, Huang, T, Zhou, T, Jing, Y, Li, W, Lin, Z, Buckler, ES, Qian, Q, Zhang, QF, Li, Jiayang and Han, B (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nature Genetics 42: 961969.Google Scholar
Lafitte, HR and Bennett, J (2002) Requirements for aerobic rice: physiological and molecular consideration. In: Bouman, BAM, Hengsdijk, H, Hardy, B, Bindraban, PS, Tuong, TP and Ladha, JK (eds) Proceedings of the International Workshop on Water-Wise Rice Production, 8–11 April 2002 . Los Baños, Philippines: International Rice Research Institute, pp. 259274.Google Scholar
Liu, K and Muse, SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21: 21282129.CrossRefGoogle ScholarPubMed
Loiselle, BA, Sork, VL, Nason, J and Graham, C (1995) Spatial genetic structure of a tropical understory shrub, Psychotria officinalis (Rubiaceae). American Journal of Botany 82: 14201425.Google Scholar
Lu, H, Redus, MA, Coburn, JR, Rutger, JN, McCouch, SR and Tai, TH (2005) Population structure and breeding patterns of 145 U.S. rice cultivars based on SSR marker analysis. Crop Science 45: 6676.Google Scholar
Murray, MG and Thompson, WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research 8: 43214325.Google Scholar
Nei, M (1973) The theory and estimation of genetic distance. In: Morton, ME (ed.) Genetic Structure of Populations. Honolulu: University Press of Hawaii, pp. 4554.Google Scholar
Pervaiz, ZH, Rabbani, MA, Pearce, SR and Malik, SA (2009) Determination of genetic variability of Asian rice (Oryza sativa L.) varieties using microsatellite markers. African Journal of Biotechnology 8: 56415651.Google Scholar
Ram, SG, Thiruvengadam, V and Vinod, KK (2007) Genetic diversity among cultivars, landraces and wild relatives of rice as revealed by microsatellite markers. Journal of Applied Genetics 48: 337345.Google Scholar
Saitou, N and Nei, M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4: 406425.Google Scholar
Schneider, S and Excoffier, L (1999) Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA. Genetics 152: 10791089.Google Scholar
Shinada, H, Yamamoto, T, Yamamoto, E, Hori, K, Yonemaru, J, Matsuba, S and Fujino, K (2014) Historical changes in population structure during rice breeding programs in the northern limits of rice cultivation. Theoretical and Applied Genetics 127: 9951004.Google Scholar
Tamura, K, Peterson, D, Peterson, N, Stecher, G, Nei, M and Kumar, S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Molecular Biology and Evolution 28: 27312739.Google Scholar
Thomson, MJ, Zhao, K, Wright, M, McNally, KL, Rey, J, Tung, CW, Reynolds, A, Scheffler, B, Eizenga, G, McCLung, A, Kim, H, Ismail, AM, de Ocampo, M, Mojica, C, Reveche, MY, Dilla-Ermita, CJ, Mauleon, R, Leung, H, Bustamante, C and McCouch, SR (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Molecular Breeding 29: 875886.CrossRefGoogle Scholar
Thornton, T, Tang, H, Hoffman, TJ, Ochs-Balcom, HM, Caan, BJ and Risch, N (2012) Estimating kinship in admixed populations. American Journal of Human Genetics 91: 122138.Google Scholar
Van Inghelandt, D, Melchinger, AE, Lebreton, C and Stich, B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theoretical and Applied Genetics 120: 12891299.Google Scholar
Venuprasad, R, Bool, ME, Quiatchon, L and Atlin, GN (2012) A QTL for rice grain yield in aerobic environments with large effects in three genetic backgrounds. Theoretical and Applied Genetics 124: 323332.Google Scholar
Würschum, T, Langer, SM, Longin, CFH, Korzun, V, Akhunov, E, Ebmeyer, E, Schachschneider, R, Schacht, J, Kazman, E and Reif, JC (2013) Population structure, genetic diversity and linkage disequilibrium in elite winter wheat assessed with SNP and SSR markers. Theoretical and Applied Genetics 126: 14771486.Google Scholar
Yang, X, Xu, Y, Shah, T, Li, H, Han, Z, Li, J and Yan, J (2011) Comparison of SSRs and SNPs in assessment of genetic relatedness in maize. Genetics 139: 10451054.Google Scholar
Yu, J, Pressoir, G, Briggs, WH, Bi, Y IV, amasaki, M, Doebley, JF, McMullen, MD, Gaut, BS, Nielsen, DM, Holland, JB, Kresovich, S and Buckler, ES (2005) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nature Genetics 38: 203208.Google Scholar
Yu, J, Zhang, Z, Zhu, C, Tabanao, DA, Pressoir, G, Tuinstra, MR, Kresovich, S, Todhunter, RJ and Buckler, ES (2009) Simulation appraisal of the adequacy of number of background markers for relationship estimation in association mapping. Plant Genome 2: 6377.Google Scholar
Zhao, DL, Atlin, GN, Amante, M, Sta Cruz, MT and Kumar, A (2010) Developing aerobic rice cultivars for water-short irrigated and drought-prone rainfed areas in the tropics. Crop Science 50: 22682276.Google Scholar
Zhao, K, Tung, C-W, 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 structure of complex traits in Oryza sativa . Nature Communication 2: 467.Google Scholar
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