Skip to main content
Log in

Identification of genomic locations associated with grain micronutrients (iron and zinc) in rice (Oryza sativa L.)

  • Research Article
  • Published:
Genetic Resources and Crop Evolution Aims and scope Submit manuscript

Abstract

In this investigation, recombinant inbred lines (RILs) population derived from PAU 201 (high yielding) and Palman 579 (high iron and zinc content) varieties were phenotyped in F5 and F6 generations for grain micronutrient content. The results showed high genetic variations among RILs and exceeded trait values beyond those of parents revealing transgressive segregants. Pearson’s correlation coefficient analysis showed that iron content had a significant positive correlation with zinc content in both generations. Composite interval mapping identified 16 QTLs on linkage groups 2, 6, 10, and 12. Eight QTLs were for zinc content and four for iron content, and four for grain yield per plant. The maximum number of QTLs were detected on chromosome 2, followed by chromosomes 12 and 10. The LOD score of identified QTLs varied from 3.7 (qYp10) to 18.43 (qFe2) explaining 51.8% and 52.8% phenotypic variation, respectively. Co-localization of QTLs (qZn12.2, qFe12, and qYp12) associated with zinc content, iron content, and grain yield in 12 between the marker interval RM 2734 and RM12 represents 5.1 cM distance, could be used for introgression for rice improvement through marker-assisted selection after validation.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Abbreviations

PH:

Plant height

ET:

Effective no of tillers

PL:

Panicle length

GY:

Grain yield

TGW:

Thousand-grain weight

Fe:

Iron content (μg/g)

Zn:

Zinc content (μg/g)

RIL:

Recombinant inbred lines

QTL:

Quantitative trait loci

References

  • Agarwal S, Vgn TV, Kotla A, Mangrauthia SK, Neelamraju S (2014) Expression patterns of QTL based and other candidate genes in Madhukar x Swarna RILs with contrasting levels of iron and zinc in unpolished rice grains. Gene 54:430–436

    Article  Google Scholar 

  • Ahuja S, Malhotra PK, Bhatia VK, Parsad R (2008) Statistical package for agricultural research (SPAR 2.0). J Indian Soc Agric Stat 62:65–74

    Google Scholar 

  • Aluko G, Martinez C, Tohme J, Castano C, Bergman C, Oard JH (2004) QTL mapping of grain quality traits from the interspecific cross Oryza sativa x O. glaberrima. Theor Appl Genet 109:630–639

    Article  CAS  Google Scholar 

  • Anonymous (2019) Indian agristatistics New Delhi. Ministry of Agriculture, India

    Google Scholar 

  • Anuradha K, Agarwal S, Batchu AK, Babu AP, Swamy BM, Longvah T, Sarla N (2012a) Evaluating rice germplasm for iron and zinc concentration in brown rice and seed dimensions. J Phytology 4:19–25

    CAS  Google Scholar 

  • Anuradha K, Agarwal S, Rao YV, Rao KV, Viraktamath BC, Sarla N (2012b) Mapping QTLs and candidate genes for iron and zinc concentrations in unpolished rice of Madhukar x Swarna RILs. Gene 508:233–240

    Article  CAS  Google Scholar 

  • Bai X, Luo L, Yan W, Kovi MR, Zhan W, Xing Y (2010) Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine-mapping a pleiotropic quantitative trait locus qGL7. BMC Genet 11:16

    Article  Google Scholar 

  • Bhusal N, Sarial AK, Saharan RP, Munjal R, Meena BK, Sareen S (2016) Phenotyping of RIL population derived from heat tolerant and susceptible parents for grain yield and its components in wheat under terminal heat stress. Adv Life Sci 5:5021–5028

    Google Scholar 

  • Bhusal N, Sarial AK, Sharma P, Sareen S (2017) Mapping QTLs for grain yield components in wheat under heat stress. PLoS ONE 12:e0189594

    Article  Google Scholar 

  • Brar B, Jain S, Singh R, Jain RK (2011) Genetic diversity for iron and zinc contents in a collection of 220 rice (Oryza sativa L.) genotypes. Indian J Genet Plant Breed 71:67–73

    Google Scholar 

  • Chakraborty R, Chakraborty S (2010) Genetic variability and correlation of some morphometric traits with grain yield in bold grained rice (Oryza sativa L.) gene pool of Barak valley. Am-Eurasian J Sustain Agric 4:26–29

    Google Scholar 

  • Descalsota GI, Swamy BP, Zaw H, Inabangan-Asilo MA, Amparado A, Mauleon R, Chadha-Mohanty P, Arocena EC, Raghavan C, Leung H, Hernandez JE (2018) Genome-wide association mapping in a rice MAGIC Plus population detects QTLs and genes useful for biofortification. Front Plant Sci 9:1347

    Article  Google Scholar 

  • Descalsota-Empleo GI, Noraziyah AA, Navea IP, Chung C, Dwiyanti MS, Labios RJ, Ikmal AM, Juanillas VM, Inabangan-Asilo MA, Amparado A, Reinke R (2019) Genetic dissection of grain nutritional traits and leaf blight resistance in rice. Genes 10(1):30

    Article  Google Scholar 

  • FAO (2015) The state of food insecurity in the world

  • Garcia-Oliveira AL, Tan L, Fu Y, Sun C (2009) Genetic identification of quantitative trait loci for contents of mineral nutrients in rice grain. J Integr Plant Biol 51:84–92

    Article  CAS  Google Scholar 

  • Gregorio GB (2002) Progress in breeding for trace elements in staple crops. J Nutr 132:500–502

    Article  Google Scholar 

  • Gregorio GB, Senadhira D, Htut T, Graham RD (2000) Breeding for trace mineral density in rice. Food Nutr Bull 21:282–286

    Article  Google Scholar 

  • Grusak MA, DellaPenna D (1999) Improving the nutrient composition of plants to enhance human nutrition and health. Annu Rev Plant Biol 50:133–161

    Article  CAS  Google Scholar 

  • Hagiwara WE, Onishi K, Takamure I, Sano Y (2006) Transgressive segregation due to linked QTLs for grain characteristics of rice. Euphytica 150:27–35

    Article  CAS  Google Scholar 

  • He G, Luo X, Tian F, Li K, Zhu Z, Su W, Qian X, Fu Y, Wang X, Sun C, Yang J (2006) Haplotype variation in structure and expression of a gene cluster associated with a quantitative trait locus for improved yield in rice. Genome Res 16:618–626

    Article  CAS  Google Scholar 

  • Hittalmani S, Huang N, Courtois B, Venuprasad R, Shashidhar HE, Zhuang JY, Zheng KL, Liu GF, Wang GC, Sidhu JS, Srivantaneeyakul S (2003) Identification of QTL for growth-and grain yield-related traits in rice across nine locations of Asia. Theor Appl Genet 107:679–690

    Article  Google Scholar 

  • Jain N, Jain S, Saini N, Jain RK (2006) SSR analysis of chromosome 8 regions associated with aroma and cooked kernel elongation in Basmati rice. Euphytica 152:259–273

    Article  CAS  Google Scholar 

  • James CR, Huynh BL, Welch RM, Choi EY, Graham RD (2007) Quantitative trait loci for phytate in rice grain and their relationship with grain micronutrient content. Euphytica 154:289–294

    Article  Google Scholar 

  • Jeong OY, Lee JH, Jeong EG, Chun A, Bombay M, Banzon Ancheta M, Ahn SN (2020) Analysis of QTL responsible for grain iron and zinc content in doubled haploid lines of rice (Oryza sativa) derived from an intra-japonica cross. Plant Breed 139(2):344–355

    Article  CAS  Google Scholar 

  • Kalaimaghal R (2011) Studies on genetic variability of grain iron and zinc content in F2, F3 generation of rice (Oryza sativa L.). M. Sc Agri Thesis TNAU Coimbatore India.

  • Kiranmayi SL, Roja V, Manorama K, Sarla N (2014) Identification of polymorphic markers associated with high iron and zinc concentration in brown rice. Trends Biosci 7:22–25

    Google Scholar 

  • Krishna L, Raju CD, Raju CS (2008) Genetic variability and correlation in yield and grain quality characters of rice germplasm. Andhra Agric J 55:27–279

    Google Scholar 

  • Kumar J, Jain S, Jain RK (2014) Linkage mapping for grain iron and zinc content in F2 population derived from the cross between PAU201 and Palman 579 in rice (Oryza sativa L.). Cereal Res Commun 42:389–400

    Article  CAS  Google Scholar 

  • Lindsay WL, Norwell WR (1978) Development of DTPA soil test for zinc, iron, manganese, and copper. Soil Sci Soc Am J 42:421–428

    Article  CAS  Google Scholar 

  • Nagesh V, Ravindrababu G, Usharani DR, Dayakar T (2012) Grain iron and zinc association studies in rice (Oryza sativa L.) F1 progenies. Arch Appl Sci Res 4:696–702

    CAS  Google Scholar 

  • Norton GJ, Deacon CM, Xiong L, Huang S, Meharg AA, Price AH (2010) Genetic mapping of the rice ionome in leaves and grain: identification of QTLs for 17 elements including arsenic, cadmium, iron and selenium. Plant Soil 329:139–153

    Article  CAS  Google Scholar 

  • Pfeiffer WH, McClafferty B (2007) HarvestPlus: breeding crops for better nutrition. Crop Sci 47:88

    Article  Google Scholar 

  • Pippal A, Jain RK, Jain S, Bhusal N (2018) Phenotyping for grain mineral contents (iron and zinc) in PAU201 × Palman 579 F5 and BC1F4 populations in rice (Oryza sativa L.). Int J Agric Environ Biotechnol. https://doi.org/10.5958/j.2230-732X

    Article  Google Scholar 

  • Rieseberg LH, Archer MA, Wayne RK (1999) Transgressive segregation, adaptation and speciation. Heredity 83:363–372

    Article  Google Scholar 

  • Saghai-Maroof MA, Soliman KM, Jorgensen R, Allard RW (1984) Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location and population dynamics. Proc Natl Acad Sci 81:8014–8018

    Article  CAS  Google Scholar 

  • Saini SS (1972) Palman, 579, a new wonder rice for Punjab. Financing agriculture.

  • Sasaki T (2005) The map-based sequence of the rice genome. Nature 436(7052):793

    Article  Google Scholar 

  • Stangoulis JC, Huynh BL, Welch RM, Choi EY, Graham RD (2007) Quantitative trait loci for phytate in rice grain and their relationship with grain micronutrient content. Euphytica 154:289–294

    Article  Google Scholar 

  • Swamy BM, Descalsota GI, Nha CT, Amparado A, Inabangan-Asilo MA, Manito C, Tesoro F, Reinke R (2018a) Identification of genomic regions associated with agronomic and biofortification traits in DH populations of rice. PLoS ONE 13(8):e0201756

    Article  Google Scholar 

  • Swamy BM, Rahman MA, Inabangan-Asilo MA, Amparado A, Manito C, Chadha-Mohanty P, Reinke R, Slamet-Loedin IH (2016) Advances in breeding for high grain Zinc in Rice. Rice 9:49

    Article  Google Scholar 

  • Swamy BP, Kaladhar K, Anuradha K, Batchu AK, Longvah T, Sarla N (2018b) QTL analysis for grain iron and zinc concentrations in two O. nivara derived backcross populations. Rice Sci 25(4):197–207

    Article  Google Scholar 

  • Voorrips RE (2002) MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93:77–78

    Article  CAS  Google Scholar 

  • Wang S, Basten CJ, Zeng ZB (2010) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC. http://statgen.ncsu.edu/qtlcart/WQTLCart.htm

  • Wessells KR, Brown KH (2012) Estimating the global prevalence of zinc deficiency: results based on zinc availability in national food supplies and the prevalence of stunting. PLoS ONE 7:50568

    Article  Google Scholar 

  • World Bank (2009) World Development Indicators.

  • Xu Q, Zheng TQ, Hu X, Cheng LR, Xu JL, Shi YM, Li ZK (2015) Examining two sets of introgression lines in rice (Oryza sativa L.) reveals favorable alleles that improve grain Zn and Fe concentrations. PLoS ONE 10:131846

    Google Scholar 

Download references

Acknowledgements

This study was part of Ph. D work, conducted in Chaudhary Charan Singh Haryana Agriculture University, Hisar India. The authors are thankful to the Department of Molecular Biology, Biotechnology and Bioinformatics, CCS Haryana Agricultural University, Hisar-125004, Haryana, India for providing research facilities for conducting the research.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nabin Bhusal.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The authors disclose that the work presented in this paper complies the with ethical standards section of the journal.

Additional information

Publisher's Note

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

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 249 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pippal, A., Bhusal, N., Meena, R.K. et al. Identification of genomic locations associated with grain micronutrients (iron and zinc) in rice (Oryza sativa L.). Genet Resour Crop Evol 69, 221–230 (2022). https://doi.org/10.1007/s10722-021-01222-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10722-021-01222-4

Keywords

Navigation