Genetic variation and association analysis of some important traits related to grain in rice (Oryza sativa L.) germplasm

Document Type : Research Paper

Authors

1 Department of Agronomy and Plant Breeding, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

2 Department of Biotechnology, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

3 Rice Research Institute of Iran, Rasht, Iran

Abstract

The identification of genomic loci involved in control of quantitative traits receives growing attention in plant molecular breeding. The present study was carried out to evaluate the genetic variability among 48 rice genotypes and determine the genomic regions associated with ten grain related important traits. A total number of 63 alleles were detected by 18 selected SSR markers from different chromosomes with an average of 3.5 alleles per marker. A model-based Bayesian approach subdivided 48 evaluated rice genotypes into three major subgroups with the consideration of the highest value of ΔK. The mean r2 value for all loci pairs on the same chromosome was 0.053. A total of 38 significant marker-trait associations were identified (P< 0.05) that explaining more than 32% of the total variation. RM315, RM3428, RM289, RM16, RM574 and RM156 markers had highest R2 and most association with assayed traits, respectively. The findings of this study revealed association of grain properties in rice with some SSR markers that could serve as target genomic regions for further research such as MAS, fine mapping and candidate gene discovery in rice breeding programs.

Keywords

Main Subjects


[1]      Basirnia, A., Hatami Maleki, H., Darvishzadeh, R. and Ghavami, F. 2014. Mixed linear model association mapping for low chloride accumulation rate in oriental-type tobacco (Nicotianatabaccum L.) germplasm. J Plant Interact, 9 (1): 666-672.
 [2]     Botstein, D., White, R. L., Skolnick, M. and Davis, R. W. 1980. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am J Hum Genet, 32: 314-331.
 [3]     Evanno, G., Regnaut, S. and Goudet, J. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol, 14: 2611-2620.
 [4]     Falconer, D. S. and Mackay, T. F. C. 1996. Introduction to quantitative genetics, 4th edt. Longman Group Ltd, London, UK.
 [5]     Flint‐Garcia, S. A, Thuillet, A. C., Yu, J., Pressoir, G., Romero, S. M, Mitchell, S. E., Doebley, J., Kresovich, S., Goodman, M. M. and Buckler, E. S. 2005. Maize association population: a high‐resolution platform for quantitative trait locus dissection. Plant J, 44: 1054-1064.
 [6]     Han, Y., Teng, C., Hu, Z. and Song, Y. 2008. An optimal method of DNA silver staining in polyacrylamide gels. Electrophoresis, 29: 1355-1358.
 [7]     Kalia, R. K., Rai, M. K., Kalia, S., Singh, R. and Dhawan, A. K. 2011. Microsatellite markers: an overview of the recent progress in plants. Euphytica, 177: 309-334.
 [8]     Kumar, V., Singh, A., Amitha Mithra, S. V., Krishnamurthy, S. L., Parida, S. K., Jain, S., Tiwari, K. K., Singh, N. K. and Mohapatra, T. 2015. Genome-wide association mapping of salinity tolerance in rice (Oryzasativa). DNA Res, 1-13.
 [9]     Liu, K. and Muse, S. V. 2005. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics, 21: 2128-2129.
 [10]   Liu, L., Wang, L., Yao, J., Zheng, Y. and Zhao, C. 2010. Association mapping of six agronomic traits on chromosome 4A of wheat (Triticumaestivum L.). Mol Plant Breed, 1(5): 1-10.
 [11]   Morgante, M. and Salamini, F. 2003. From plant genomics to breeding practice. Curr Opin Biotechnol, 14: 214-219.
 [12]   Pritchard, J. K., Stephens, M. and Donnelly, P. 2000. Inference of population structure using multilocus genotype data. Genetics, 155: 945–959.
 [13]   Shehzad, T., Iwata, H. and Okuno, K. 2009. Genome-wide association mapping of quantitative traits in sorghum [Sorghum bicolor (L.) Moench] by using multiple models. Breed Sci, 59: 217-227.
 [14]   Shulman, A. H. 2007. Molecular markers to assess genetic diversity. Euphytica, 158: 313- 321.
 [15]   Simko, I., Pechenick, D. A., McHale, L. K., Truco, M. J., Ochoa, O. E., Michelmore, R. W. and Scheffler, B. E. 2009. Association mapping and marker-assisted selection of the lettuce dieback resistance gene Tvr1. BMCPlantBiol, 23: 9-135.
 [16]   Varshney, R. K. and Tuberosa, R. 2007. Genomics-assisted crop improvement: an overview. In: Genomics-assisted crop improvement. Springer. p. 1-12.
 [17]   Wang, M., Jiang, N., Jia, T., Leach, L., Cockram, J., Comadran, J., Shaw, P., Waugh, R. and Luo Z. 2012. Genome-wide association mapping of agronomic and morphologic traits in highly structured populations of barley cultivars. Theor Appl Genet, 124: 233-246.
 [18]   Warburton, M., Yan, J. and Crouch, J. 2011. Association mapping for enhancing maize (L.) genetic improvement. Crop Sci, 51: 433-449.
 [19]   Wen, W., Mei, H., Feng, F., Yu, S., Huang, Z., Wu, J., Chen, L., Xu, X. and Luo, L. 2009. Population structure and association mapping on chromosome 7 using a diverse panel of Chinese germplasm of rice (Oryzasativa L.). Theor Appl Genet, 119: 459-470.
 [20]   Yao, J., Wang, L., Liu, L., Zhao, C. and Zheng, Y. 2009. Association mapping of agronomic traits on chromosome 2A of wheat. Genetica, 137: 67-75.
 [21]   Yu, J., Pressoir, G., Briggs, W. H., Bi, I. V., Yamasaki, M. and Doebley, J. F. 2006. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. NatGenet, 38: 203- 208.
 [22]   YunFei, Z., GuoPing, S. and XuGao, C. 2007. Characteristics of the short rachillae of rice from archaeological sites dating to 7000 years ago. Chin Sci Bull, 52: 1654-1660.
 [23]   Zhang, P., Liu, X., Tong, H., Lu, Y. and Li, J. 2014. Association mapping for important agronomic traits in core collection of rice (Oryza sativa L.) with SSR markers. PLoS ONE, 9 (10): 508-519.
 [24]   Zhao, W., Park, E. J., Chung, J. W., Park, Y. J., Chung, I. M., Ahn, J. K. and Kim, G. H. 2009. Association analysis of the amino acid contents in rice. J Integr Plant Biol, 51 (12): 1126- 1137.
 [25]   Zheng, X., Wu, J. G., Lou, X. H., Xu, H. M. and Shi, C. H. 2008. QTL analysis of maternal and endosperm genomes for histidine and arginine in rice (Oryzasativa L.) across environments. Acta Agron Sin, 34: 369- 375.
 [26]   Zong, Y., Chen, Z., Innes, J., Chen, C., Wang, Z. and Wang, H. 2007. Fire and flood management of coastal swamp enabled first rice paddy cultivation in east china. Nature, 449: 459-462.