Theoretical and Applied Genetics

, Volume 126, Issue 1, pp 101–108 | Cite as

QTLs associated with root traits increase yield in upland rice when transferred through marker-assisted selection

  • K. A. Steele
  • A. H. Price
  • J. R. Witcombe
  • Roshi Shrestha
  • B. N. Singh
  • J. M. Gibbons
  • D. S. Virk
Original Paper


Altering root morphology of rice (Oryza sativa L.) cultivars could improve yields in drought-prone upland ecosystems. Marker-assisted backcross breeding was used to introgress four QTLs for root traits into an upland rice cultivar. The QTLs had previously been identified under experimental conditions in a different genetic background. The introgressed lines and the recurrent parent were grown for 6 years by resource-poor farmers in upland sites in Eastern India and yields recorded. In combination the QTLs significantly increased yield by 1 t ha−1 under relatively favourable field conditions. In less favourable trials, the QTL effects were not detected due to greater heterogeneity in soil–water availability in very low yielding environments and consequent yield variability. Root studies under controlled conditions showed that lines with the introgressions had longer roots throughout tillering than the recurrent parent (14 cm longer 2 weeks after sowing). Therefore, both improved roots and increased yield can be attributed to the introgression of QTLs. This is the first demonstration that marker-assisted backcross breeding (MABC) to introgress multiple root QTLs identified under controlled conditions is an effective strategy to improve farmers’ yields of upland rice. The strategy was used to breed a novel upland rice cultivar that has been released in India as Birsa Vikas Dhan 111.



The on-farm trials were funded by the Rockerfeller Foundation and DFID. We gratefully acknowledge the assistance of the Gramin Vikas Trust, India. The rhizotron experiments were funded by the Generation Challenge programme project “Targeting Drought-Avoidance Root Traits to Enhance Rice Productivity under Water-Limited Environments”.


  1. Ashraf M (2010) Inducing drought tolerance in plants: recent advances. Biotechnol Adv 28:169–183PubMedCrossRefGoogle Scholar
  2. Bernier J, Serraj R, Kumar A, Venuprasad R, Impa S, Gowda V, Owane R, Spaner D, Atlin G (2009) Increased water uptake explains the effect of qtl12.1 a large-effect drought-resistance QTL in upland rice. Field Crop Res 110:139–146CrossRefGoogle Scholar
  3. Ceccarelli S, Grando S (1996) Drought as a challenge for the plant breeder. Plant Growth Reg 20:149–155CrossRefGoogle Scholar
  4. Clark LJ, Price AH, Steele KA, Whalley WR (2008) Evidence from near-isogenic lines that root penetration increases with root diameter and bending stiffness in rice. Funct Plant Biol 35:1163–1171CrossRefGoogle Scholar
  5. Collard BCY, Mackill DJ (2008) Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Phil Trans R Soc B 363:557–572PubMedCrossRefGoogle Scholar
  6. Coudert Y, Perin C, Courtois B, Khong NG, Gantet P (2010) Genetic control of root development in rice, the model cereal. Trends Plant Sci 15:219–226PubMedCrossRefGoogle Scholar
  7. Courtois B, Ahmadi N, Khowaja F, Price AH, Rami J-F, Frouin J, Hamelin C, Ruiz M (2009) Rice root genetic architecture: meta-analysis from a Drought QTL Database. Rice 2:115–128CrossRefGoogle Scholar
  8. Ehdaie E, Whitkus RW, Waines JG (2003) Root Biomass, water-use efficiency, and performance of wheat–rye translocations of chromosomes 1 and 2 in spring bread wheat ‘Pavon’. Crop Sci 43:710–717CrossRefGoogle Scholar
  9. Foulkes MJ, Sylvester-Bradley R, Weightman R, Snape J (2007) Identifying physiological traits associated with improved drought resistance in winter wheat. Field Crops Res 103:11–24CrossRefGoogle Scholar
  10. Gowda VRP, Henry A, Yamauchi A, Shashidhar HE, Serraj R (2011) Root biology and genetic improvement of drought avoidance in rice. Field Crop Res 122:1–13CrossRefGoogle Scholar
  11. Hinsinger P, Bengough AG, Vetterlein D, Young IM (2009) Rhizosphere: biophysics, biogeochemistry and ecological relevance. Plant Soil 321:117–152CrossRefGoogle Scholar
  12. Hothorn T, Bretz F, Westfall P (2008) Simultaneous inference in general parametric models. Biometr J 50:346–363CrossRefGoogle Scholar
  13. Joshi A, Witcombe JR (1996) Farmer participatory crop improvement. II. Participatory varietal selection, a case study in India. Exp Agric 32:461–477CrossRefGoogle Scholar
  14. Joshi KD, Musa AM, Johansen C, Gyawali S, Harris D, Witcombe JR (2007) Highly client-oriented breeding, using local preferences and selection, produces widely adapted rice varieties. Field Crops Res 100:107–116CrossRefGoogle Scholar
  15. Khowaja FS, Norton GJ, Courtois B, Price AH (2009) Improved resolution in the position of drought-related QTLs in a single mapping population of rice by meta-analysis. BMC Genomics 10:276. doi: 10.1186/1471-2164-10-276 PubMedCrossRefGoogle Scholar
  16. Landi P, Giuliani S, Salvi S, Ferri M, Tuberosa R, Sanguineti MC (2010) Characterization of root-yield-1.06, a major constitutive QTL for root and agronomic traits in maize across water regimes. J Exp Bot 61:3553–3562PubMedCrossRefGoogle Scholar
  17. MacMillan K, Emrich K, Piepho H-P, Mullins C, Price A (2006) Assessing the importance of genotype × environment interaction for root traits in rice using a mapping population II: conventional QTL analysis. Theor Appl Genet 113:953–964PubMedCrossRefGoogle Scholar
  18. Mandal NP, Sinha PK, Variar M, Shukla VD, Perraju P, Mehta A, Pathak AR, Dwivedi JL, Rathi SPS, Bhandarkar S, Singh BN, Singh DN, Panda S, Mishra NC, Singh YV, Pandya R, Singh MK, Sanger RBS, Bhatt JC, Sharma RK, Raman A, Kumar A, Atlin G (2010) Implications of genotype × input interactions in breeding superior genotypes for favorable and unfavorable rainfed upland environments. Field Crops Res 118:135–144CrossRefGoogle Scholar
  19. Pinheiro JC, Bates DM (2004) Mixed-effects models in S and S-PLUS. Springer Science and Business Media, New YorkGoogle Scholar
  20. Pray C, Nagarajan L, Li L, Huang J, Hu R, Selvaraj KN, Napasintuwong O, Chandra Babu R (2011) Potential impact of biotechnology on adaption of agriculture to climate change: the case of drought tolerant rice breeding in Asia. Sustainability 3:1723–1741CrossRefGoogle Scholar
  21. Price AH, Steele KA, Moore BJ, Wyn-Jones G (2002) Upland rice grown in soil-filled chambers and exposed to contrasting water-deficit regimes: II. Mapping QTL for root morphology and distribution. Field Crops Res 76:25–43CrossRefGoogle Scholar
  22. R Development Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
  23. Steele KA, Singh DN, Kumar, R, Prasad SC, Virk DS, Gangwar JS and Witcombe JR (2002) Combining molecular marker technology and participatory techniques: a case study for drought-tolerant rice in eastern India II: Farmer evaluation of SLS-MAS bulks in participatory plant breeding. In: Proceedings of a DFID Plant Sciences Research Programme/IRRI Conference in breeding rainfed rice for drought-prone environments: integrating conventional and participatory plant breeding in South and Southeast Asia. IRRI, Los Banos, Laguna, Philippines, 12–15 March 2002, pp 20–22Google Scholar
  24. Steele KA, Price AH, Shashidhar HE, Witcombe JR (2006) Marker-assisted selection to introgress of rice QTLs controlling root traits and aroma into an Indian upland rice variety. Theor Appl Genet 112:208–221PubMedCrossRefGoogle Scholar
  25. Steele KA, Virk DS, Kumar R, Prasad SC, Witcombe JR (2007) Field evaluation of upland rice lines selected for QTLs controlling root traits. Field Crops Res 101:181–186CrossRefGoogle Scholar
  26. Tuberosa R, Salvi S (2006) Genomics-based approaches to improve drought tolerance of crops. Trends Plant Sci 11:405–412PubMedCrossRefGoogle Scholar
  27. Venuprasad R, Impa S, Veeresh-Gowda RP, Atlin GN, Serraj R (2011) Rice near-isogenic-lines (NILs) contrasting for grain yield under lowland drought stress. Field Crops Res 123:38–46CrossRefGoogle Scholar
  28. Verulkar SB, Mandal NP, Dwivedi JL, Singh BN, Sinha PK, Mahato RN, Swain P, Dongre P, Payasi D, Singh ON, Bose LK, Robin S, Chandrababu R, Senthil S, Jain A, Shashidhar HE, Hittalmani S, Vera Cruz C, Paris T, Hijmans R, Raman A, Haefele S, Serraj R, Atlin G, Kumar A (2010) Breeding resilient and productive rice genotypes adapted to drought-prone rainfed ecosystems of India. Field Crops Res 117:197–208CrossRefGoogle Scholar
  29. Vikram P, Mallikarjuna Swamy BP, Dixit S, Ahmed HU, Sta Cruz MT, Singh AK, Kumar A (2011) qDTY1.1, a major QTL for rice grain yield under reproductive-stage drought stress with a consistent effect in multiple elite genetic backgrounds. BMC Genet 12:89PubMedCrossRefGoogle Scholar
  30. Virk DS, Singh DN, Kumar R, Prasad SC, Gangwar JS, Witcombe JR (2003) Collaborative and consultative participatory plant breeding of rice for the rainfed uplands of eastern India. Euphytica 132:95–108CrossRefGoogle Scholar
  31. Waines JG, Ehdaie B (2007) Domestication and crop physiology: roots of green-revolution wheat. Ann Bot 100:991–998PubMedCrossRefGoogle Scholar
  32. Witcombe JR, Joshi KD, Virk DS, Sthapit BR (2011) Impact of introduction of modern varieties on crop diversity. In: Lenne JM, Wood D (eds) Agrobiodiversity management for food security—a critical review. CAB International, Wallingford, pp 87–90Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • K. A. Steele
    • 1
  • A. H. Price
    • 2
  • J. R. Witcombe
    • 1
  • Roshi Shrestha
    • 3
  • B. N. Singh
    • 4
  • J. M. Gibbons
    • 5
  • D. S. Virk
    • 1
  1. 1.Centre for Advanced Research in International Agricultural Development (CARIAD)Bangor UniversityGwyneddUK
  2. 2.Institute of Biological and Environmental Sciences, Cruickshank BuildingUniversity of AberdeenAberdeenUK
  3. 3.Department of BiologyUniversity of MilanMilanItaly
  4. 4.Birsa Agricultural UniversityRanchiIndia
  5. 5.School of the Environment, Natural Resources and Geography (SENRGY)Bangor UniversityGwyneddUK

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