3 Biotech

, 9:299 | Cite as

WA-CMS-based iso-cytoplasmic restorers derived from commercial rice hybrids reveal distinct population structure and genetic divergence towards restorer diversification

  • Amit Kumar
  • Vikram Jeet Singh
  • S. Gopala Krishnan
  • K. K. Vinod
  • Prolay Kumar Bhowmick
  • M. Nagarajan
  • Ranjith Kumar Ellur
  • Haritha Bollinedi
  • Ashok Kumar SinghEmail author
Original Article


One hundred diverse iso-cytoplasmic restorer (ICR) lines carrying WA cytoplasm indicated significant but moderate variability for agro-morphological traits as well as for the microsatellite-based allele patterns. There were two major groups of ICRs based on agro-morphological clustering. Simple sequence repeat (SSR) markers identified allelic variants with an average of 2.48 alleles per locus and the gene diversity (GD) ranged from 0.02 to 0.62 at different loci. ICR lines showed a genetic structure involving two sub-populations, POP1 and POP2. Both the subpopulations had the presence of admixture lines. Nearest ancestry-based grouping of ICRs by neighbour-joining (NJ) method showed near similar grouping as that of sub-population division. The POP2 was the largest group but with fewer admixed lines. POP1 was more distinct than POP2. Since the hybrid parents of the ICRs had limited diversity on maternal lineage, paternal lineage was concluded as the major contributor to the observed divergence and population differentiation. ICRs developed from certain hybrids were more genetically distinct than other hybrids. Even with the moderate variability, ICRs could be considered as a potential source of fertility restoration in hybrid development because of their distinct population structure and the full complement of restorer genes they contained. ICR lines with high per se performance can be utilized in hybrid rice development by estimating their combining ability.


Iso-cytoplasmic restorers Combining ability Population structure SSR markers Hybrid rice 



The study is part of the PhD research of the first author. The first author acknowledges the Post Graduate School, ICAR–IARI, New Delhi for providing the necessary facilities for the research study. The authors gratefully acknowledge the funding assistance from the Indian Council of Agricultural Research under Consortia Research Platform on Hybrid Technology (Project Code # 12-142). Technical help rendered by Binder Singh, Devinder Singh, Mahendran and Bibekananda Ray in maintaining the crop is thankfully acknowledged.

Author contributions

Conceptualization of research (AKS, GKS); Designing of the experiments (AKS, GKS, AK); Contribution of experimental materials (AK, PKB); Execution of field/lab experiments and data collection (AK, GKS, VJS, PKB, MN); Analysis of data and interpretation (AK, GKS, KKV, PKB, AKS); Preparation of manuscript (AK, GKS, KKV, PKB, AKS, RKE, HB).

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Supplementary material

13205_2019_1824_MOESM1_ESM.pdf (524 kb)
Supplementary material 1 (PDF 523 kb)


  1. Ali ML, McClung AM, Jia MH, Kimball JA, McCouch SR, Eizenga GC (2011) A rice diversity panel evaluated for genetic and agro-morphological diversity between subpopulations and its geographic distribution. Crop Sci 51:2021–2035. CrossRefGoogle Scholar
  2. Anand D, Prabhu KV, Singh AK (2012) Analysis of molecular diversity and fingerprinting of commercially grown Indian rice hybrids. J Plant Biochem Biotechnol 21:173–179. CrossRefGoogle Scholar
  3. Baack E, Melo MC, Rieseberg LH, Ortiz-Barrientos D (2015) The origins of reproductive isolation in plants. New Phytol 207:968–984. CrossRefPubMedGoogle Scholar
  4. Bar-Hen A, Charcosset A, Bourgoin M, Cuiard J (1995) Relationships between genetic markers and morphological traits in a maize inbred lines collection. Euphytica 84:145–154. CrossRefGoogle Scholar
  5. Beukert U, Li Z, Liu G, Zhao Y, Ramachandra N, Mirdita V, Pita F, Pillen K, Reif JC (2017) Genome-based identification of heterotic patterns in rice. Rice 10:22. CrossRefPubMedPubMedCentralGoogle Scholar
  6. Breseghello F, Coelho ASG (2013) Traditional and modern plant breeding methods with examples in rice (Oryza sativa L.). J Agric Food Chem 61:8277–8286. CrossRefPubMedGoogle Scholar
  7. Courtois B, Frouin J, Greco R, Bruschi G, Droc G, Hamelin C, Ruiz M, Clément G, Evrard J-C, van Coppenole S (2012) Genetic diversity and population structure in a European collection of rice. Crop Sci 52:1663–1675. CrossRefGoogle Scholar
  8. Earl DA, VonHoldt BM (2012) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361. CrossRefGoogle Scholar
  9. Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620. CrossRefPubMedGoogle Scholar
  10. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3. 0: an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50CrossRefGoogle Scholar
  11. Franco J, Crossa J, Ribaut JM, Betran J, Warburton ML, Khairallah M (2001) A method for combining molecular markers and phenotypic attributes for classifying plant genotypes. Theor Appl Genet 103:944–952. CrossRefGoogle Scholar
  12. Gomez KA, Gomez A (1984) Statistical procedure for agricultural research. Wiley, New York, p 704Google Scholar
  13. Hashimoto Z, Mori N, Kawamura M, Ishii T, Yoshida S, Ikegami M, Takumi S, Nakamura C (2004) Genetic diversity and phylogeny of Japanese sake-brewing rice as revealed by AFLP and nuclear and chloroplast SSR markers. Theor Appl Genet 109:1586–1596. CrossRefPubMedGoogle Scholar
  14. He ZZ, Xie FM, Chen LY, Madonna ADP (2012) Genetic diversity of tropical hybrid rice germplasm measured by molecular markers. Rice Sci 19:193–201. CrossRefGoogle Scholar
  15. Ikehashi H (1982) Prospects for overcoming barrier in utilization of indicajaponica crosses in rice breeding. Oryza 19:69–77Google Scholar
  16. IRRI (2013) Standard evaluation system (SES) for rice, 5th edn. International Rice Research Institute, ManilaGoogle Scholar
  17. Jin L, Lu Y, Xiao P, Sun M, Corke H, Bao J (2010) Genetic diversity and population structure of a diverse set of rice germplasm for association mapping. Theor Appl Genet 121:475–487. CrossRefPubMedGoogle Scholar
  18. Kumar A, Bhowmick PK, Krishnan GS, Singh AK (2017a) Development and evaluation of iso-cytoplasmic rice restorer lines for different agro-morphological traits. Indian J Genet 77:493–500. CrossRefGoogle Scholar
  19. Kumar A, Bhowmick PK, Singh VJ, Malik M, Gupta AK, Krishnan GS, Singh AK (2017b) Marker-assisted identification of restorer gene(s) in iso-cytoplasmic restorer lines of WA cytoplasm in rice and assessment of their fertility restoration potential across environments. Physiol Mol Biol Plants 23:891–909. CrossRefPubMedPubMedCentralGoogle Scholar
  20. Kumar S, Stecher G, Li M, Knyaz C, Tamura K (2018) MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol 35:1547–1549. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Liu K, Muse SV (2005) PowerMarker V3.25: integrated analysis environment for genetic marker data. Bioinformatics 21:2128–2129. CrossRefPubMedGoogle Scholar
  22. Melchinger AE, Gumber RK (1998) Overview of heterosis and heterotic groups in agronomic crops. In: Larnkey KR, Staub JE (eds) Concepts and breeding of heterosis in crop plants. 1. Crop Science Society of America, Madison, pp 29–44. CrossRefGoogle Scholar
  23. Murray HG, Thompson WF (1980) Rapid isolation of high molecular weight DNA. Nucleic Acid Res 8:4321–4325. CrossRefPubMedGoogle Scholar
  24. Murren CJ, Auld JR, Callahan H, Ghalambor CK, Handelsman CA, Heskel MA, Kingsolver JG, Maclean HJ, Masel J, Maughan H, Pfennig DW, Relyea RA, Seiter S, Snell-Rood E, Steiner UK, Schlichting CD (2015) Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115:293–301. CrossRefPubMedPubMedCentralGoogle Scholar
  25. Nagarajan M, Vinod KK, Singh AK, Prabhu KV (2012) Rice breeding and genetics research centre—a story of success. Indian Agricultural Research Institute, New Delhi, p 104Google Scholar
  26. Naik AR, Chaudhury D, Reddy JN (2004) Genetic divergence studies in scented rice. Oryza 40:79–82Google Scholar
  27. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetic 155:945–959Google Scholar
  28. Rajendran NL, Mukherjee L, Reddy KK, Shashidhar HE (2012) DNA fingerprinting and estimation of genetic diversity among hybrid rice parental lines (Oryza sativa L.) using simple sequence repeats (SSR) markers. J Plant Breed Crop Sci 4:169–174. CrossRefGoogle Scholar
  29. Reig-Valiente JL, 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 9:58. CrossRefPubMedPubMedCentralGoogle Scholar
  30. Rogers JS (1972) Measures of genetic similarity and genetic distances. Stud Genet 7213:145–153 (Univ Texas Publ) Google Scholar
  31. Roy B, Basu AK, Mandal AB (2002) Genetic diversity in rice (Oryza sativa L.) genotypes under humid tropics of Andaman based on grain yield and seed characters. Indian J Agric Sci 72:84–87Google Scholar
  32. Roy S, Marndi BC, Mawkhlieng B, Banerjee A, Yadav RM, Misra AK, Bansal KC (2016) Genetic diversity and structure in hill rice (Oryza sativa L.) landraces from the North-Eastern Himalayas of India. BMC Genet 17:107. CrossRefPubMedPubMedCentralGoogle Scholar
  33. Sabouri H, Rabiei B, Fazlalipour M (2008) Use of selection indices based on multivariate analysis for improving grain yield in rice. Rice Sci 15:303–310. CrossRefGoogle Scholar
  34. Sarawgi AK, Rastogi NK (2000) Genetic diversity in traditional aromatic rice accessions from Madhya Pradesh. Indian J Plant Genet Res 13:138–146Google Scholar
  35. Shahidullah SM, Hanafi MM, Ashrafuzzaman M, Ismail MR, Salam MA, Khair A (2010) Biomass accumulation and energy conversion efficiency in aromatic rice genotypes. C R Biol 333:61–67. CrossRefPubMedGoogle Scholar
  36. Sheeba NK, Viraktamath BC, Sivaramakrishnan S, Gangashetti MG, Khera P, Sundaram RM (2009) Validation of molecular markers linked to fertility restorer gene(s) for WA-CMS lines of rice. Euphytica 167:217–227. CrossRefGoogle Scholar
  37. Singh VP (2000) The Basmati rice of India. In: Singh RK, Singh US, Khush GS (eds) Aromatic rices. Oxford & IBH Publishing Co Pvt Limited, New DelhiGoogle Scholar
  38. Singh N, Choudhury DR, Tiwari G, Singh AK, Kumar S, Srinivasan K, Tyagi RK, Sharma AD, Singh NK, Singh R (2016) Genetic diversity trend in Indian rice varieties: an analysis using SSR markers. BMC Genet 17:127. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Siwach P, Jain S, Saini N, Chowdhury VK, Jain RK (2004) Allelic diversity among basmati and non-basmati long grain indica rice varieties using microsatellite markers. J Plant Biochem Biotechnol 13:25–32. CrossRefGoogle Scholar
  40. Subbaiyan GK, Waters DL, Katiyar SK, Sadananda AR, Vaddadi S, Henry RJ (2012) Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol J 10:623–634. CrossRefPubMedGoogle Scholar
  41. Virmani SS, Sun ZX, Mou TM, Ali JA, Mao CX (2003) Two-line hybrid rice breeding manual. International Rice Research Institute, Los Baños (Philippines), p 88Google Scholar
  42. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370. CrossRefPubMedGoogle Scholar
  43. Xiao J, Li J, Grandillo S, Ahn SN, Yuan L (1998) Identification of trait-improving quantitative trait loci alleles from a wild rice relative, Oryza rufipogon. Genetics 150:899–909PubMedPubMedCentralGoogle Scholar
  44. Xie F (2009) Priorities of IRRI hybrid rice breeding. In: Xie F, Hardy B (eds) Accelerating hybrid rice development. International Rice Research Institute, Los Baños, pp 49–61Google Scholar
  45. Yadav S, Singh A, Singh MR, Goel N, Vinod KK, Mohapatra T, Singh AK (2013) Assessment of genetic diversity in Indian rice germplasm (Oryza sativa L.): use of random versus trait-linked microsatellite markers. J Genet 92:545–557CrossRefGoogle Scholar
  46. Yeh FC, Yang RC, Boyle TBJ, Ye ZH, Mao JX (1997) POPGENE, the user-friendly shareware for population genetic analysis. University of Alberta, EdmontonGoogle Scholar
  47. Young A, Boyle T, Brown T (1996) The population genetic consequences of habitat fragmentation for plants. Trend Ecol Evol 11:413–418. CrossRefGoogle Scholar
  48. Zhang T, Ni X-L, Jiang K-F, Deng H-F, He Q, Yang QH, Yang L, Wan XQ, Cao Y, Zheng J (2010) Relationship between heterosis and parental genetic distance based on molecular markers for functional genes related to yield traits in rice. Rice Sci 17:288–295. CrossRefGoogle Scholar
  49. Zhang P, Li J, Li X, Liu X, Zhao X, Lu Y (2011) Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS One 6:e27565. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© King Abdulaziz City for Science and Technology 2019

Authors and Affiliations

  • Amit Kumar
    • 1
    • 2
  • Vikram Jeet Singh
    • 1
  • S. Gopala Krishnan
    • 1
  • K. K. Vinod
    • 3
  • Prolay Kumar Bhowmick
    • 1
  • M. Nagarajan
    • 3
  • Ranjith Kumar Ellur
    • 1
  • Haritha Bollinedi
    • 1
  • Ashok Kumar Singh
    • 1
    Email author
  1. 1.Division of GeneticsICAR-Indian Agricultural Research Institute (ICAR-IARI)New DelhiIndia
  2. 2.Plant Breeding, ICAR-Research Complex for North Eastern Hill RegionUmiamIndia
  3. 3.Rice Breeding and Genetics Research CentreICAR-IARIAduthuraiIndia

Personalised recommendations