Czech J. Genet. Plant Breed., 2020, 56(2):62-70 | DOI: 10.17221/58/2019-CJGPB

Genetic diversity of released Malaysian rice varieties based on single nucleotide polymorphism markersOriginal Paper

Shahril Ab Razak*,1, Nor Helwa Ezzah Nor Azman1, Rahiniza Kamaruzaman2, Shamsul Amri Saidon2, Muhammad Fairuz Mohd Yusof1, Siti Norhayati Ismail1, Mohd Azwan Jaafar1, Norzihan Abdullah1
1 Agri-Omic & Bioinformatic Program, Biotechnology and Nanotechnology Research Centre, MARDI Headquarter, Serdang, Selangor, Malaysia
2 Rice Breeding Program, Rice Research Centre, MARDI Seberang Perai, Kepala Batas, Pulau Pinang, Malaysia

Understanding genetic diversity is a main key for crop improvement and genetic resource management. In this study, we aim to evaluate the genetic diversity of the released Malaysian rice varieties using single nucleotide polymorphism (SNP) markers. A total of 46 released Malaysian rice varieties were genotyped using 1536 SNP markers to evaluate their diversity. Out of 1536 SNPs, only 932 SNPs (60.7%) represented high quality alleles, whereas the remainder either failed to amplify or had low call rates across the samples. Analysis of the 932 SNPs revealed that a total of 16 SNPs were monomorphic. The analysis of the SNPs per chromosome revealed that the average of the polymorphic information content (PIC) value ranged from 0.173 for chromosome 12 to 0.259 for chromosome 11, with an average of 0.213 per locus. The genetic analysis of the 46 released Malaysian rice varieties using an unweighted pair group method with arithmetic mean (UPGMA) dendrogram revealed the presence of two major groups. The analysis was supported by the findings from the STRUCTURE analysis which indicated the ∆K value to be at the highest peak at K = 2, followed by K = 4. The pairwise genetic distance of the shared alleles showed that the value ranged from 0.000 (MR159-MR167) to 0.723 (MRIA-Setanjung), which suggested that MR159 and MR167 were identical, and that the highest dissimilarity was detected between MRIA 1 and Setanjung. The results of the study will be very useful for the variety identification, the proper management and conservation of the genetic resources, and the exploitation and utilisation in future breeding programmes.

Keywords: DNA marker; genetic relationship; Oryza sativa

Published: June 30, 2020  Show citation

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Ab Razak S, Nor Azman NHE, Kamaruzaman R, Saidon SA, Mohd Yusof MF, Ismail SN, et al.. Genetic diversity of released Malaysian rice varieties based on single nucleotide polymorphism markers. Czech J. Genet. Plant Breed. 2020;56(2):62-70. doi: 10.17221/58/2019-CJGPB.
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    References

    1. Ahuja M.R., Jain S.M. (2015): Genetic Diversity and Erosion in Plants: Indicators and Prevention. Vol. 7. Cham, Springer International Publishing Switzerland. Go to original source...
    2. Becerra V., Paredes M., Gutiérrez E., Rojo C. (2015): Genetic diversity, identification, and certification of Chilean rice varieties using molecular markers. Chilean Journal of Agricultural Research, 75: 267-274. Go to original source...
    3. Bohn M., Utz H.F., Melchinger A.E. (1999): Genetic similarities among winter wheat cultivars determined on the basis of RFLPs, AFLPs, and SSRs and their use for predicting progeny variance. Crop Science, 39: 228-237. Go to original source...
    4. Chen H., He H., Zou Y., Chen W., Yu R., Liu X., Yang Y., Gao Y.M., Xu J.L., Fan L.M., 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: 869-879. Go to original source... Go to PubMed...
    5. Dao A., Sanou J., Mitchell S.E., Gracen V., Danquah E.Y. (2014): Genetic diversity among INERA maize inbred lines with single nucleotide polymorphism (SNP) markers and their relationship with CIMMYT, IITA, and temperate lines. BMC Genetics, 15: 1-14. Go to original source... Go to PubMed...
    6. Earl D.A. (2012): STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conservation Genetics Resources, 4: 359-361. Go to original source...
    7. Evanno G., Regnaut S., Goudet J. (2005): Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Molecular Ecology, 14: 2611-2620. Go to original source... Go to PubMed...
    8. Freamo H., O'Reilly P., Berg P.R., Lien S., Boulding E.G. (2011): Outlier SNPs show more genetic structure between two Bay of Fundy metapopulations of Atlantic salmon than do neutral SNPs. Molecular Ecology Resources, 11: 254-267. Go to original source... Go to PubMed...
    9. Fufa H., Baenziger P.S., Beecher B.S., Dweikat I., Graybosch R.A., Eskridge K.M. (2005): Comparison of phenotypic and molecular marker-based classifications of hard red winter wheat cultivars. Euphytica, 145: 133-146. Go to original source...
    10. Habibuddin H., Shen Y., Rabiatul Adawiah A.Z., Khairun Hisam N., Hayati A., Shahril Firdaus A.R., Siti Norhayati I., Azwan J. (2013): Potensi penggunaan 1536 SNP dalam kajian-kajian genetik padi. In: Proc. Persidangan Padi Kebangsaan 2013, Pulai Pinang, Dec 10-12, 2013: 412-417. (in Malay)
    11. Helyar S.J., Hemmer-Hansen J., Bekkevold D., Taylor M.I., Ogden R., Limborg M.T., Cariani A., Maes G.E., Diopere E., Carvalho G.R., Nielsen E.E. (2011): Application of SNPs for population genetics of nonmodel organisms: new opportunities and challenges. Molecular Ecology Resources, 11: 123-136. Go to original source... Go to PubMed...
    12. Kumar S., Stecher G., Tamura K. (2016): MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Molecular Biology and Evolution, 33: 1870-1874. Go to original source... Go to PubMed...
    13. Liu N., Chen L., Wang S., Oh C., Zhao H. (2005): Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure. BMC Genetics, 6: S26. Go to original source... Go to PubMed...
    14. Liu K., Muse S.V. (2005): PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics, 21: 2128-2129. Go to original source... Go to PubMed...
    15. Mace E.S., Buhariwalla K.K., Buhariwalla H.K., Crouch J.H. (2003): A high-throughput DNA extraction protocol for tropical molecular breeding programs. Plant Molecular Biology, 21: 459-460. Go to original source...
    16. Mariĉ S., Bolariĉ S., Martinèiĉ J., Pejiĉ I., Kozumplik V. (2004): Genetic diversity of hexaploid wheat cultivars estimated by RAPD markers, morphological traits and coefficients of parentage. Plant Breeding, 123: 366-369. Go to original source...
    17. Morin P.A., McCarthy M. (2007): Highly accurate SNP genotyping from historical and low-quality samples. Molecular Ecology Notes, 7: 937-946. Go to original source...
    18. Pejic I., Ajmone-Marsan P., Morgante M., Kozumplick V., Castiglioni P., Taramino G., Motto M. (1998): Comparative analysis of genetic similarity among maize inbred lines detected by RFLPs, RAPDs, SSRs, and AFLPs. Theoretical and Applied Genetics, 97: 1248-1255. Go to original source...
    19. Pritchard J.K., Stephens M., Donnelly P. (2000): Inference of population structure using multilocus genotype data. Genetics, 155: 945-959. Go to original source... Go to PubMed...
    20. Reig-Valiente J.L., Viruel J., Sales E., Marqués L., Terol J., Gut M., Derdak S., Talón M., Domingo C. (2016): Genetic diversity and population structure of rice varieties cultivated in temperate regions. Rice, 9: 1-12. Go to original source... Go to PubMed...
    21. Ren J., Chen L., Sun D., You F.M., Wang J., Peng Y., Nevo E., Beiles A., Sun D., Luo M.C., Peng J. (2013): SNP-revealed genetic diversity in wild emmer wheat correlates with ecological factors. BMC Evolutionary Biology, 13: 1-15. Go to original source... Go to PubMed...
    22. Rosenberg N.A., Li L.M., Ward R., Pritchard J.K. (2003): Informativeness of genetic markers for inference of ancestry. American Journal of Human Genetics, 73: 1402-1422. Go to original source... Go to PubMed...
    23. Roy S., Banerjee A., Mawkhlieng B., Misra A.K., Pattanayak A., Harish G.D., Singh S.K., Ngachan S.V. Bansal K.C. (2015): Genetic diversity and population structure in aromatic and quality rice (Oryza sativa L.) landraces from North-Eastern India. PLOS ONE, 10: 1-13. Go to original source... Go to PubMed...
    24. Singh Y.T. (2019): Genetic Diversity and Population Structure in Upland Rice (Oryza sativa L.) of Mizoram, North East India as revealed by morphological, biochemical and molecular markers. Biochemical Genetics, 57: 421-442. Go to original source... Go to PubMed...
    25. Soleimani V.D., Baum B.R., Johnson D.A. (2003): Efficient validation of single nucleotide polymorphisms in plants by allele-specific PCR, with an example from barley. Plant Molecular Biology Reporter, 21: 281-288. Go to original source...
    26. Thomson M.J., Septiningsih E.M., Suwardjo F., Santoso T.J., Silitonga T.S., McCouch S.R. (2007): Genetic diversity analysis of traditional and improved Indonesian rice (Oryza sativa L.) germplasm using microsatellite markers. Theoretical and Applied Genetics, 114: 559-568. Go to original source... Go to PubMed...
    27. Xu Q., Yuan X., Wang S., Feng Y., Yu H., Wang Y., Yang Y., Li X. (2016): The genetic diversity and structure of indica rice in China as detected by single nucleotide polymorphism analysis. BMC Genetics, 17: 1-8. Go to original source... Go to PubMed...
    28. Zhu Y., Chen H., Fan J., Wang Y., Li Y., Chen J., Fan J., Yang S., Hu L., Leung H., Mew T.W. (2000): Genetic diversity and disease control in rice. Nature, 406: 718-722. Go to original source... Go to PubMed...

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