Skip to main content
SpringerLink
Log in

SSR marker-based study of the effects of genomic regions on Fe, Mn, Zn, and protein content in a rice diversity panel

  • Original Article
  • Published:
Journal of Plant Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Rice is one of the most important basic foods, especially in developing countries. However, rice production is negatively affected by drought and decreased water availability. Over the recent decades, aerobic rice has been considered as a promising approach to reduce water input. However, the rice genotypes suitable for aerobic rice should also be evaluated for other important characteristics such as grain micronutrient content. Studying the genetic variability of grain Fe, Mn, Zn, and protein content in aerobic rice genotypes compared to other rice varieties can be useful for improving nutritive value via plant breeding programs. In this study, 50 rice genotypes, including aerobic rice, Iranian-improved, and landrace varieties were studied for Fe, Mn, Zn, and protein content in brown rice and also for their association with 77 SSR markers. There was a significant diversity among the rice genotypes: 13.91–44.91 mg/kg for Fe, 5.85–35.93 mg/kg for Mn, 11.79–34.33 mg/kg for Zn, and 4.38–16.63% for protein. According to the cluster analysis, group (I) contained five Iranian landrace rice varieties (Champaboudar, Gharib, Alikazemi, Anbarbou, and Sangetarom) well-known for their eating and cooking quality and aroma; furthermore, 10 aerobic rice genotypes had the highest level of Fe and Zn content among other groups. According to the results some aerobic rice genotypes had high grain micronutrient values such as IR 82,639-B-B-103–4, which also had 16.63% protein content in the grain. Based on the results of association analysis, out of 55 random SSRs, 11 markers, and out of 22 linked SSRs, 17 markers were found to be associated with grain protein, Fe, Zn, and Mn in the present population. Out of 28 significant markers, RM488 associated with grain Fe, RM574 with Mn, and RM248 with Mn and Zn and the associated was detected in both the years. In addition, out of 11 newly detected SSR markers, five were substantially associated with rice grain quality traits. The markers, which are showing association, can be utilized to enhance the nutritional value of rice in plant breeding programs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

SSR:

Simple sequence repeats

PCR:

Polymerase chain reaction

QTL:

Quantitative trait loci

References

  • Agarwal S, Mangrauthia SK, Sarla N (2018) Genomic approaches for micronutrients biofortification of rice. In plant micronutrient use efficiency. Academic Press, Cambridge, pp 245–260

    Google Scholar 

  • Aluko G, Martinez C, Tohme J et al (2004) QTL mapping of grain quality traits from the interspecific cross Oryza sativa×O. glaberrima. Theor Appl Genet 109(3):630–639. https://doi.org/10.1007/s00122-004-1668-y

    Article  CAS  PubMed  Google Scholar 

  • Anuradha K, Agarwal S, Rao YV et al (2012) Mapping QTLs and candidate genes for iron and zinc concentrations in unpolished rice of Madhukar× Swarna RILs. Gene 508(2):233–240. https://doi.org/10.1016/j.gene.2012.07.054

    Article  CAS  PubMed  Google Scholar 

  • Asante MD (2017) Breeding rice for improved grain quality Advances in international rice research. IntechOpen, London, pp 69–89

    Google Scholar 

  • Barber S, Benedito de Barber C (1991) Rice bran: chemistry and technology. In: Luh BS (ed) Rice: production and utilization. AVI Publishing, Connecticut

    Google Scholar 

  • Bekele BD, Rakhi S, Naveen GK et al (2013) Estimation of genetic variability and correlation studies for grain zinc concentrations and yield related traits in selected rice (Oryza sativa L.) genotypes. Asian J Exp Biol Sci 4(3):345–351

    CAS  Google Scholar 

  • Bouis HE, Welch RM (2010) Biofortification-a sustainable agricultural strategy for reducing micronutrient malnutrition in the global south. Crop Sci 50:20–32. https://doi.org/10.2135/cropsci2009.09.0531

    Article  Google Scholar 

  • Bouman BAM, Peng S, Castaneda AR et al (2005) Yield and water use of irrigated tropical aerobic rice systems. Agric Water Manag 74(2):87–105. https://doi.org/10.1016/j.agwat.2004.11.007

    Article  Google Scholar 

  • Bouman BAM, Wang H, Yang X et al (2002) Aerobic rice (Han Dao): a new way of growing rice in water-short areas. In proceedings of the 12th international soil conservation organization conference (Vol. 26, p. 31). Tsinghua University Press: Beijing, China

  • Cakmak I, Kutman UB (2018) Agronomic biofortification of cereals with zinc: a review. Euro J Soil Sci 69:172–180. https://doi.org/10.1111/ejss.12437

    Article  Google Scholar 

  • Creste S, Tulmann Neto A, Figueira A (2001) Detection of single sequence repeat polymorphisms in denaturing polyacrylamide sequencing gels by silver staining. Plant Mol Biol Rep 19:299–306. https://doi.org/10.1007/BF02772828

    Article  CAS  Google Scholar 

  • Descalsota GIL, Swamy M, Zaw H et al (2018) Genome-wide association mapping in a rice MAGIC plus population detects QTLs and genes useful for biofortification. Front Plant Sci 9:1347. https://doi.org/10.3389/fpls.2018.01347

    Article  PubMed  PubMed Central  Google Scholar 

  • Dixit S, Singh UM, Abbai R et al (2019) Identification of genomic region (s) responsible for high iron and zinc content in rice. Sci Rep 9(1):8136. https://doi.org/10.1038/s41598-019-43888-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • FAO, IFAD, UNICEF, WFP and WHO (2019) The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns. Rome, FAO.Licence: CC BY-NC-SA 3.0 IGO.

  • Gande NK, Rakhi S, Kundur PJ et al (2013) Evaluation of recombinant inbred lines of rice (Oryza sativa L.) for grain zinc content, yield related traits and identification of transgressant lines grown under aerobic conditions. Asian J Exp Biol Sci 4(4):567–574

    Google Scholar 

  • Hanjra MA, Qureshi ME (2010) Global water crisis and future food security in an era of climate change. Food Policy 35(5):365–377. https://doi.org/10.1016/j.foodpol.2010.05.006

    Article  Google Scholar 

  • Hefferon K (2019) Biotechnological approaches for generating zinc-enriched crops to combat malnutrition. Nutrients 11:253. https://doi.org/10.3390/nu11020253

    Article  CAS  PubMed Central  Google Scholar 

  • Huaqi W, Bouman BAM, Zhao D et al (2002) Aerobic rice in northern China: opportunities and challenges Water-wise rice production. International Rice Research Institute, Los Baños (Philippines), pp 143–154

    Google Scholar 

  • IBM Corp (2016) IBM SPSS statistics for windows, Version 24.0. Armonk, NY IBM Corp. (Released 2016).

  • Indurkar AB, Majgahe SK, Sahu VK, Vishwakarma A, Premi V, Shrivastatva P (2015) Research article identification, characterization and mapping of QTLs related to grain Fe, Zn and protein contents in rice (Oryza sativa L.). Electr J Plant Breed 6(4):1059–1068

    Google Scholar 

  • Jamali SH, Mohammadi SA, Sadeghzadeh B (2017) Association mapping for morphological traits relevant to registration of barley varieties. Span J Agric Res 15(4):e0704. https://doi.org/10.5424/sjar/2017154-10494

    Article  Google Scholar 

  • Jana K, Karmakar R, Banerjee S et al (2018) Aerobic rice cultivation system eco-friendly and water saving technology under changed climate. J Agric Res Technol 13(2):555878. https://doi.org/10.19080/ARTOAJ.2018.13.555878002

    Article  Google Scholar 

  • Jhang T, Vikal Y, Singh K et al (2006) Molecular identification and introgression of QTLs for yield components from temperate japonica rice in Basmati 370. SABRAO J Breed Genet 38(2):83

    Google Scholar 

  • Kalra YP (1998) Handbook of reference methods for plant analysis. Taylor & Francis Group, LLC CRC Press, Boca Raton, FL, p 287

    Google Scholar 

  • Liu T, Li L, Zhang Y et al (2011) Comparison of quantitative trait loci for rice yield, panicle length and spikelet density across three connected populations. J Genet 90(2):377–382

    Article  Google Scholar 

  • Lu K, Li L, Zheng X et al (2008) Quantitative trait loci controlling Cu, Ca, Zn, Mn and Fe content in rice grains. J Genet 87(3):305–310

    Article  Google Scholar 

  • Mohammadi M, Xavier A, Beckett T et al (2020) Identification, deployment, and transferability of quantitative trait loci from genome-wide association studies in plants. Curr Plant Biol. https://doi.org/10.1016/j.cpb.2020.100145

    Article  Google Scholar 

  • Mahender A, Anandan A, Pradhan SK, Pandit E (2016) Rice grain nutritional traits and their enhancement using relevant genes and QTLs through advanced approaches. Springerplus 5(1):2086. https://doi.org/10.1186/s40064-016-3744-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Mohammadi SA, Prasanna BM, Sudan C et al (2002) A microsatellite marker based study of chromosomal regions and gene effects on yield and yield components in maize. Cell Mol Biol Lett 7:599–606

    CAS  PubMed  Google Scholar 

  • Palanog AD, Calayugan MIC, Descalsota-Empleo GIL et al (2019) Zinc and Iron nutrition status in the Philippines population and local soils: a review. Front Nutr 6:81. https://doi.org/10.3389/fnut.2019.00081

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Priyanka S, Jitesh B, Babu S (2012) Aerobic rice, a new approach of rice cultivation. Int J Res BioSci 1(1):1–6

    Google Scholar 

  • Sabouri A, Afshari R, Raiesi T et al (2018) Superior adaptation of aerobic rice under drought stress in Iran and validation test of linked SSR markers to major QTLs by MLM analysis across two years. Mol Biol Rep 45(5):1037–1105. https://doi.org/10.1007/s11033-018-4253-1

    Article  CAS  PubMed  Google Scholar 

  • Saghai Maroof MA, Biyashev RM, Yang GP et al (1994) Extraordinarily polymorphic microsatillate DNA in barley species diversity, chromosomal location, and population dynamics. Proc Nat Acad Sci, USA 91:5466–5570. https://doi.org/10.1073/pnas.91.12.5466

    Article  CAS  Google Scholar 

  • Segal R, Le Nguyet M (2019) Unfair harvest: the state of rice in Asia. Oxfam International, Oxford, UK

    Google Scholar 

  • Stangoulis J (2010) Technical aspects of zinc and iron analysis in biofortification of the staple food crops, wheat and rice. World Congress of Soil Science, Soil Solutions for a Changing World 1–6 August 2010, Brisbane: Australia

  • Swamy BPM, Kaladhar K, Anuradha K et al (2018) QTL analysis for grain iron and zinc concentrations in two O. nivara derived backcross populations. Rice Sci 25(4):197–207. https://doi.org/10.1016/j.rsci.2018.06.003

    Article  Google Scholar 

  • Swamy BPM, Kaladhar K, Anuradha K et al (2011) Enhancing iron and zinc concentration in rice grains using wild species. In ADNAT convention and international symposium on genomics and biodiversity”, CCMB, Hyderabad 23–25 Feb 2011, Abstracts p. 71

  • Tan YF, Sun M, Xing YZ et al (2001) Mapping quantitative trait loci for milling quality, protein content and color characteristics of rice using a recombinant inbred line population derived from an elite rice hybrid. Theor Appl Genet 103(6–7):1037–1045. https://doi.org/10.1007/s001220100665

    Article  CAS  Google Scholar 

  • Vijayaraghavareddy P, Xinyou YIN, Struik PC, Makarla U, Sreeman S (2020) Responses of lowland, upland and aerobic rice genotypes to water limitation during different phases. Rice Sci 27(4):345–354. https://doi.org/10.1016/j.rsci.2020.05.009

    Article  Google Scholar 

  • Welch RM, Graham RD (1999) A new paradigm for world agriculture: meeting human needs: productive, sustainable, nutritious. Field Crops Res 60(1–2):1–10. https://doi.org/10.1016/S0378-4290(98)00129-4

    Article  Google Scholar 

  • Xiaoguang Y, Huaqi W, Zhimin W, Junfang Z, Bin C, Bouman BAM (2002) Yield of aerobic rice (Han Dao) under different water regimes in North China. In: Bouman BAM, Hengsdijk H, Hardy B, Bindraban PS, Tuong TP, Ladha JK (eds) Water-wise rice production. International Rice Research Institute, Los Banos (Philippines), pp 155–164

    Google Scholar 

  • Xie GH, Jun YU, Wang HQ, Bouman BAM (2008) Progress and yield bottleneck of aerobic rice in the North China Plain: a case study of varieties Handao 297 and Handao 502. Agric Sci China 7(6):641–646. https://doi.org/10.1016/S1671-2927(08)60097-8

    Article  CAS  Google Scholar 

  • Xu F, Sun C, Huang Y, Chen Y, Tong C, Bao J (2015) QTL mapping for rice grain quality: a strategy to detect more QTLs within sub-populations. Mol Breed 35(4):105. https://doi.org/10.1007/s11032-015-0296-3

    Article  Google Scholar 

  • Yun BW, Kim MG, Handoyo T et al (2014) Analysis of rice grain quality-associated quantitative trait loci by using genetic mapping. Am J Plant Sci 5(09):1125. https://doi.org/10.4236/ajps.2014.59125

    Article  Google Scholar 

Download references

Acknowledgements

We would like to acknowledge the help of Dr. Arvind Kumar, Senior Scientist, for providing of original seeds and Dr. B.P. Mallikarjuna Swamy for providing valuable suggestions at International Rice Research Institute (IRRI). We would like to acknowledge the help Dr. A.R. Dadras, at Olive Research Station of Tarom, AREEO, Zanjan, Iran, and Miss Haniyeh Babaei-Raouf for their technical and scientific assistance.

Funding

This research supported by University of Guilan.

Author information

Authors and Affiliations

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

    Atefeh Sabouri, Elham Nasiri & Masoud Esfahani

  2. Department of Soil Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran

    Akbar Forghani

Authors
  1. Atefeh Sabouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elham Nasiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masoud Esfahani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akbar Forghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AS planned and designed the project, also analyzed data, collaborated in writing of draft manuscript and revised the final manuscript. EN conducted the lab works and collaborated in writing of draft manuscript. AF planned the work at the soil science lab. ME contributed to the design of the research.

Corresponding author

Correspondence to Atefeh Sabouri.

Ethics declarations

Conflict of interest

The authors declare that they have not conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sabouri, A., Nasiri, E., Esfahani, M. et al. SSR marker-based study of the effects of genomic regions on Fe, Mn, Zn, and protein content in a rice diversity panel. J. Plant Biochem. Biotechnol. 30, 504–514 (2021). https://doi.org/10.1007/s13562-020-00637-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13562-020-00637-x

Keywords

Search

Navigation