Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-10T10:55:06.526Z Has data issue: false hasContentIssue false

Genetic diversity and structure of improved indica rice germplasm

Published online by Cambridge University Press:  02 January 2014

Kai Wang
Affiliation:
International Rice Research Institute, Metro Manila, DAPO Box 7777, Philippines State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou310006, PR China
Fulin Qiu
Affiliation:
International Rice Research Institute, Metro Manila, DAPO Box 7777, Philippines Liaoning Rice Research Institute, Shenyang110101, PR China
Madonna Angelita Dela Paz
Affiliation:
International Rice Research Institute, Metro Manila, DAPO Box 7777, Philippines
Jieyun Zhuang
Affiliation:
State Key Laboratory of Rice Biology and Chinese National Center for Rice Improvement, China National Rice Research Institute, Hangzhou310006, PR China
Fangming Xie*
Affiliation:
International Rice Research Institute, Metro Manila, DAPO Box 7777, Philippines
*
* Corresponding author. E-mail: f.xie@irri.org

Abstract

The characterization of genetic diversity and structure for improved cultivated varieties/elite lines is tremendously important to assist breeders in parental selection for inbred and hybrid breeding and heterotic group construction. In this study, a total of 737 improved indica varieties/lines developed recently and/or widely used by present indica breeding programmes worldwide were genotyped with a 384-single-nucleotide polymorphism assay. Model-based population structure analysis revealed the presence of two major groups with six subgroups (SGs), wherein no clear correlation was found between the groups/SGs and breeding programmes or geographical origin of the accessions. Over half of the accessions (51.8%) appeared to have less than 0.6 memberships assigned to any one of the six model-based groups, highlighting the wide range of gene flow within improved indica varieties/lines and the genetic integration of valuable alleles shared by ancestries among improved high-yielding varieties/lines through germplasm exchanges. Distance-based clustering revealed that Latin-American cultivated indica lines have tended to form their own ecological cline, which could serve as a potential heterotic ecotype for hybrid rice breeding, although they are still closely related to Asian indica lines. African cultivated indica lines, on the other hand, have not yet formed their own ecological cline. It was also observed that the most well-known hybrid rice parents, Zhenshan97B and Minghui63, were unexpectedly clustered in the same SG with a relatively narrow genetic distance, which suggests that a significant genetic distance between parents is not a prerequisite for all elite hybrid rice lines with high heterosis.

Type
Research Article
Copyright
Copyright © NIAB 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agrama, HA, Yan, WG, Jia, M, Fjellstrom, R and McClung, AM (2010) Genetic structure associated with diversity and geographic distribution in the USDA riceworld collection. Natural Science 2: 247291.Google Scholar
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 23: 26332635.Google Scholar
Cavalli-Sforza, LL and Edwards, AWF (1967) Phylogenetic analysis: models and estimation procedures. American Journal of Human Genetics 19: 233257.Google Scholar
Chen, H, He, H, Zou, Y, Chen, W, Yu, R, Liu, X, Yang, Y, Gao, YM, Xu, JL and Fan, LM (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: 869879.Google Scholar
Cui, D, Xu, CY, Tang, CF, Yang, CG, Yu, TQ, A, XX, Cao, GL, Xu, FR, Zhang, JG and Han, LZ (2013) Genetic structure and association mapping of cold tolerance in improved japonica rice germplasm at the booting stage. Euphytica 193: 369382.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.Google Scholar
Excoffier, L and Lischer, HEL (2010) Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under Linux and Windows. Molecular Ecology Resources 10: 564567.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.Google Scholar
He, ZZ, Xie, FM, Chen, LY and Dela Paz, MA (2012) Genetic diversity of tropical hybrid rice germplasm measured by molecular markers. Chinese Journal of Rice Science 19: 193201.Google Scholar
Liu, K and Muse, SV (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21: 21282129.Google Scholar
McNally, KL, Childs, KL, Bohnert, R, Davidson, RM, Zhao, K, Ulat, VJ, Zeller, G, Clark, RM, Hoen, DR and Bureau, TE (2009) Genome-wide SNP variation reveals relationships among landraces and modern varieties of rice. Proceedings of the National Academy of Sciences 106: 1227312278.Google Scholar
Murray, MG and Thompson, WF (1980) Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research 8: 43214326.Google Scholar
Perrier, X and Jacquemoud-Collet, JP (2006) DARwin Software. Montpellier: Centre de Coopération Internationale en Recherche Agronomique pour le Développement.Google Scholar
Pritchard, JK, Stephens, M and Donnelly, P (2000) Inference of population structure using multilocus genotype data. Genetics 155: 945959.CrossRefGoogle ScholarPubMed
Thomson, MJ, Zhao, K, Wright, M, McNally, KL, Rey, J, Tung, CW, Reynolds, A, Scheffler, B, Eizenga, G and McClung, A (2012) High-throughput single nucleotide polymorphism genotyping for breeding applications in rice using the BeadXpress platform. Molecular Breeding 29: 875886.Google Scholar
Varshney, RK, Graner, A and Sorrells, ME (2005) Genomics-assisted breeding for crop improvement. Trends in Plant Science 10: 621630.Google Scholar
Wei, XH, Yuan, XP, Yu, HY, Wang, YP, Xu, Q and Tang, SX (2009) SSR analysis of genetic variation in Chinese major inbred rice varieties. Chinese Journal of Rice Science 23: 237244.Google Scholar
Xie, F, Guo, L, Ren, G, Hu, P, Wang, F, Xu, J, Li, X, Qiu, F and Dela Paz, MA (2012) Genetic diversity and structure of indica rice varieties from two heterotic pools of southern China and IRRI. Plant Genetic Resources: Characterization and Utilization 10: 186193.Google Scholar
Xu, X, Liu, X, Ge, S, Jensen, JD, Hu, F, Li, X, Dong, Y, Gutenkunst, RN, Fang, L and Huang, L (2011) Resequencing 50 accessions of cultivated and wild rice yields markers for identifying agronomically important genes. Nature Biotechnology 30: 105111.Google Scholar
Yan, WG, Agrama, H, Jia, M, Fjellstrom, R and McClung, AM (2010) Geographic description of genetic diversity and relationships in the USDA rice world collection. Crop Science 50: 24062417.Google Scholar
Zhang, Q, Maroof, MAS, Lu, TY and Shen, BZ (1992) Genetic diversity and differentiation of indica and japonica rice detected by RFLP analysis. Theoretical and Applied Genetics 83: 495499.Google Scholar
Zhang, P, Li, J, Li, X, Liu, X, Zhao, X and Lu, Y (2011) Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PloS One 6: e27565.Google Scholar
Zhao, K, Tung, CW, Eizenga, GC, Wright, MH, Ali, ML, Price, AH, Norton, GJ, Islam, MR, Reynolds, A and Mezey, J (2011) Genome-wide association mapping reveals a rich genetic architecture of complex traits in Oryza sativa . Nature Communications 2: 467.Google Scholar
Supplementary material: Image

Wang et al. Supplementary Material

Figure

Download Wang et al. Supplementary Material(Image)
Image 1.8 MB
Supplementary material: File

Wang et al. Supplementary Material

Tables

Download Wang et al. Supplementary Material(File)
File 348.2 KB