Skip to main content
SpringerLink
Log in

Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping

  • Published:
Molecular Breeding Aims and scope Submit manuscript

Abstract

Developing trait introgressed rice cultivars is essential to sustain yield under aerobic conditions. Here, we report DNA markers governing variability in root traits, water use efficiency (WUE) and other biometric traits like total leaf area by association mapping. A set of 173 diverse rice germplasm accessions were phenotyped for root traits in specially designed root structures and WUE using carbon isotope discrimination (Δ13C) during the monsoon season (July to October) of two consecutive years (2007 and 2008). The panel was genotyped using 291 SSR markers spanning the entire genome of rice. Root biomass varied between 1.8 and 16.3 g plant−1 while root length between 22 and 78 cm representing significant genetic variability. Similarly, Δ13C varied from 18 to 23 ‰. The SSR markers showed extensive polymorphism with around 73 % of all the markers revealing polymorphism information content values more than 0.5. Model-based structure analysis using the squared-allele frequency correlations revealed six subgroups among the panel with an average LD decay of about 10–20 cM. The Benjamini–Hochberg analysis was carried out to compute the false discovery rate combined with the analysis of effective LD. A total of 82 markers were involved in 175 significant (corrected P values and Q values <0.05) marker–trait associations (MTAs) across experiment 1 and experiment 2 and for the pooled data. Out of these, 22 markers were found to be associated with more than one trait. Common markers with significant associations were discovered for root biomass, total leaf area and total biomass suggesting the interdependency of these traits. Finally, 12 markers showed significant and stable MTAs across the experiments for different traits. An in silico analysis indicated that 45 % of the MTAs overlapped with previously reported QTLs and can be used for QTL introgression through breeding.

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

Similar content being viewed by others

References

  • Agrama HA, Eizenga C, Yan W (2007) Association mapping of yield and its components in rice cultivars. Mol Breed 19:341–356

    Article  Google Scholar 

  • Alonso-Blanco C, Aarts MG, Bentsink L, Keurentjes JJ, Reymond M, Vreugdenhil D, Koornneef M (2009) What has natural variation taught us about plant development, physiology, adaptation? Plant Cell 21:1877–1896

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Araus JL, Slafer GA, Reynolds MP, Royo C (2002) Plant breeding drought in C3 cereals: what should we breed for? Ann Bot 89:925–940

    Article  PubMed  PubMed Central  Google Scholar 

  • Araus JL, Slafer GA, Royo C, Serret MD (2008) Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci 27:377–412

    Article  Google Scholar 

  • Babu RC, Nguyen BD, Chamarerk V et al (2003) Genetic analysis of drought resistance in rice by molecular markers: association between secondary traits and field performance. Crop Sci 43:1457–1469

    Article  CAS  Google Scholar 

  • Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser 57:289–300

    Google Scholar 

  • Bernier J, Kumar A, Venuprasad R, Spaner D, Atlin GN (2007) A large-effect QTL for grain yield under reproductive-stage drought stress in upland rice. Crop Sci 47:507–516

    Article  Google Scholar 

  • Blum A (2009) Effective use of water (EUW) and not water-use efficiency (WUE) is the target of crop yield improvement under drought stress. Field Crops Res 112:119–123

    Article  Google Scholar 

  • Blum A (2011) Drought resistance: is it really a complex trait? Funct Plant Biol 3810:753–757

    Article  Google Scholar 

  • Bouman BA, Xiaoguang XY, Huaqui W, Zhiming W, Junfang Z, Changgui W, Bin C (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, May 26–31. Tsinghua University Press, Beijing, China, pp 175–181

  • Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, RamdossY Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23:2633–2635

    Article  CAS  PubMed  Google Scholar 

  • Breseghello F, Sorrells ME (2006) Association mapping of kernel size milling quality in wheat (Triticum aestivum L.) cultivars. Genetics 172:1165–1177

    Article  PubMed  PubMed Central  Google Scholar 

  • Christopher N, Toppa, Anjali S, Iyer P, Jill T, Ersond, Paul R (2013) 3D Phenotyping quantitative trait locus mapping identify core regions of the rice genome controlling root architecture. PNAS 110(18):E1695–E1704

    Article  Google Scholar 

  • Condon AG, Farquhar GD, Richards RA (1990) Genotypic variation in carbon isotope discrimination transpiration efficiency in wheat leaf gas exchange whole plant studies. Aust J Plan Physiol 17:9–22

    Article  Google Scholar 

  • Condon AG, Richards RA, Rebetzke J, Farquhar GD (2004) Breeding for high water-use efficiency. J Exp Bot 55:2447–2460

    Article  CAS  PubMed  Google Scholar 

  • Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, Droc G, Frouin J, Rouan L, Gozé E, Kilian A, Ahmadi N, Dingkuhn M (2013) Genome-wide association mapping of root traits in a japonica rice panel. PLoS One 8(11):e78037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cui K, Peng S, Xing Y, Yu S, Xu C, Zhang Q (2003) Molecular dissection of the genetic relationships of source, sink and transport tissue with yield traits in rice. Theor Appl Genet 106(4):649–658

    CAS  PubMed  Google Scholar 

  • Dingkuhn M, Farquhar GD, De Datta SK, O’toole JC (1991) Discrimination of 13C among upland rice having different water use efficiencies. Aust J Agric Res 42:1123–1131

    Article  CAS  Google Scholar 

  • Dixit S, Grondin A, Lee C-R, Henry A, Mitchell-Olds T, Kumar A (2015) Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci. BMC Genet 16:86

    Article  PubMed  PubMed Central  Google Scholar 

  • Dong X, Wang X, Zhang L et al (2013) Identification and characterization of OsEBS, a gene involved in enhanced plant biomass and spikelet number in rice. Plant Biotechnol J 11(9):1044–1057

    Article  CAS  PubMed  Google Scholar 

  • Earl DA (2012) Structure Harvester: a website program for visualizing STRUCTURE output implementing the Evanno method. Conserv Genet Resour 4:359–361

    Article  Google Scholar 

  • 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

    Article  CAS  PubMed  Google Scholar 

  • Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491

    CAS  PubMed  PubMed Central  Google Scholar 

  • Famoso AN, Zhao K, Clark RT, Tung CW, Wright MH (2011) Genetic architecture of aluminum tolerance in rice (Oryza sativa L.) determined through genome-wide association analysis QTL mapping. PLoS Genet 7(8):e1002221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Farquhar GD, Ehleringer JR, Hubick KT (1989) Carbon isotope discrimination photosynthesis. Annu Rev Plant Physiol 40:503–537

    Article  CAS  Google Scholar 

  • Fukai S, Cooper M (1995) Development of drought-resistant cultivars using physio-morphological traits in rice. Field Crops Res 40:67–86

    Article  Google Scholar 

  • Garris A, McCouch S, Kresovich S (2003) Population structure and its effects on haplotype diversity linkage disequilibrium surrounding the xa5 locus of rice (Oryza sativa L.). Genetics 165:759–769

    PubMed  PubMed Central  Google Scholar 

  • Garris A, Tai TH, Coburn J, KresovichS Mccouch S (2005) Genetic structure diversity in rice (Oryza sativa L.). Genetics 169:1631–1638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gowda VRP, Henry A, Akira Y, Shashidhar HE, Serraj R (2011) Root biology genetic improvement for drought avoidance in rice. Field Crops Res 122:1–13

    Article  Google Scholar 

  • Hardy OJ, Vekemans X (2002) SpaGeDi: a versatile computer program to analyze spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620

    Article  Google Scholar 

  • Hemamalini GS, Shashidhar HE, Hittalmani S (2000) Molecular marker assisted tagging of morphological physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.). Euphytica 112:69–76

    Article  CAS  Google Scholar 

  • Henry A, Dixit S, Mandal NP, Anantha MS, Torres R, Kumar A (2014) Grain yield and physiological traits of rice lines with the drought yield QTL qDTY12. 1 showed different responses to drought and soil characteristics in upland environments. Funct Plant Biol 41(11):1066–1077

    Article  CAS  Google Scholar 

  • Henry A, Swamy BM, Dixit S, Torres RD, Batoto TC, Manalili M et al (2015) Physiological mechanisms contributing to the QTL-combination effects on improved performance of IR64 rice NILs under drought. J Exp Bot 66:1787–1799

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hu FY, Tao DY, Sacks E et al (2003) Convergent evolution of perenniality in rice and sorghum. PNAS 100(7):4050–4054

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Huang X, Wei X, San T, Zhao Q, Feng Q, Zhao Y, Li C, ZhuC LuT, Zhang Z (2010) Genome-wide association studies of 14 agronomic traits in rice land races. Nat Genet 42:961–967

    Article  CAS  PubMed  Google Scholar 

  • Impa SM, Nadarajan S, Boominathan P, Shashidhar G, Bindhumadhava H, Sheshshayee MS (2005) Carbon isotope discrimination accurately reflects variability in WUE measured at a whole plant level in rice. Crop Sci 45:2517–2522

    Article  CAS  Google Scholar 

  • IPCC (2013) Climate change 2013: the physical science basis. Working Group I Contribution to the IPCC 5th Assessment Report. Geneva, IPCC

    Google Scholar 

  • Jiang GH, He YQ, Xu CG, Li XH, Zhang Q (2004) The genetic basis of stay-green in rice analyzed in a population of doubled haploid lines derived from an indica by japonica cross. Theor Appl Genet 108(4):688–698

    Article  CAS  PubMed  Google Scholar 

  • Jung JK, McCouch SR (2013) Getting to the roots of it: genetic hormonal control of root architecture. Front Plant Sci 4:186

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kamoshita A, Babu RC, BoopathiNM Fukai S (2008) Phenotypic genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crops Res 109:1–23

    Article  Google Scholar 

  • Krill AM, Kirst M, Kochian LV, Buckler ES, Hoekenga OA (2010) Association linkage analysis of aluminum tolerance genes in maize. PLoS One 5(4):1992–1998

    Article  Google Scholar 

  • Kumar A, Bernier J, Verulkar S, Lafitte HR, Atlin GN (2008) Breeding for drought tolerance: direct selection for yield, response to selection and use of drought-tolerant donors in upland and lowland-adapted populations. Field Crops Res 107:221–231

    Article  Google Scholar 

  • Lanceras JC, Pantuwan G, Jongdee B, Toojinda T (2004) Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiol 135:384–399

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Li ZC, Mu P, Li CP, Zhang HL, Li ZK, Gao YM, Wang XK (2005) QTL mapping of root traits in a doubled haploid population from a cross between upland lowland japonica rice in three environments. Theor Appl Genet 110:1244–1252

    Article  CAS  PubMed  Google Scholar 

  • Lian X, Xing Y, Yan H, Xu C, Li X, Zhang Q (2005) QTLs for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid. Theor Appl Genet 112(1):85–96

    Article  CAS  PubMed  Google Scholar 

  • Liu K, Muse S (2005) PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics 21:2128–2129

    Article  CAS  PubMed  Google Scholar 

  • Lynch JP (2007) Roots of the second green revolution. Aust J Bot 55:493–512

    Article  Google Scholar 

  • Mather DE, Hyes PM, Chalmers KJ et al (2004) Use of SSR marker data to study linkage disequilibrium population structure in Hordeum vulgare. Prospects for association mapping in barley. In: International barley genetics symposium, Brno, Czech Republic pp 302–307

  • McCouch S, Teytelman L, Xu Y et al (2002) Development mapping of 2240 new SSR markers for rice (Oryza sativa L). DNA Res 9:199–207

    Article  CAS  PubMed  Google Scholar 

  • Mishra KK, Vikram P, Yadaw RB, Swamy BPM, Dixit S, Sta Cruz MT, Maturan P, Marker S, Kumar A (2013) qDTY 12.1: a locus with a consistent effect on grain yield under drought in rice. BMC Genet 14:12

    Article  PubMed  PubMed Central  Google Scholar 

  • N’goran JAK, Laurent V, Risterucci AM, Lanaud C (2000) The genetic structure of cocoa populations (Theobroma cacao L.) revealed by RFLP analysis. Euphytica 115:83–90

    Article  Google Scholar 

  • Nguyen HT, Babu RC, Blum A (1997) Breeding for drought resistance in rice: physiological and molecular genetics considerations. Crop Sci 37:1426–1434

    Article  Google Scholar 

  • Passioura J (2012) Phenotyping for drought tolerance in grain crops: when is it useful to breeders? Funct Plant Biol 39:851–859

    Article  Google Scholar 

  • Peakall ROD, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol Ecol Notes 6(1):288–295

    Article  Google Scholar 

  • Perrier X, Flori A, Bonnot F (2003) Data analysis methods: genetic diversity of cultivated tropical plants. Science Publishers, Montpellier, pp 43–76

    Google Scholar 

  • Price AH, Steele KA, Moore BJ, Jones RGW (2002) Upland rice grown in soil-filled chambers exposed to contrasting water-deficit regimes II Mapping quantitative trait loci for root morphology and distribution. Field Crops Res 76:25–43

    Article  Google Scholar 

  • Pritchard JK, Przeworski M (2001) Linkage disequilibrium in humans models data. Am J Hum Genet 69:1–14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pritchard JK, Rosenberg NA (1999) Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 65:220–228

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959

    CAS  PubMed  PubMed Central  Google Scholar 

  • Raju BR, Narayanaswamy BR, Mohankumar MV, Sumanth KK, Rajanna MP, Udayakumar M, Sheshshayee MS (2014) Root traits and cellular level tolerance hold the key in maintaining higher spikelet fertility of rice under water limited conditions. Funct Plant Biol 41(9):930–939

    Article  Google Scholar 

  • Reynolds M, Tuberosa R (2008) Translational research impacting on crop productivity in drought-prone environments. Curr Opin Plant Biol 11:171–179

    Article  PubMed  Google Scholar 

  • Saghai-Maroof MA, Solima KM, Jorgenson RA, Allard RW (1984) Ribosomal DNA spacer-length polymorphisms in barley: mendelian inheritance, chromosomal location, population dynamics. PNAS 81:8014–8018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Samuel A, Ordonez J, Silva J, Oard JH (2010) Association mapping of grain quality flowering time in elite japonica rice germplasm. J Cereal Sci 51:337–343

    Article  Google Scholar 

  • Sandhu N, Singh A, Dixit S, Sta Cruz MT, Maturan PC, Jain RK, Kumar A (2014) Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress. BMC Genet 15:63

    Article  PubMed  PubMed Central  Google Scholar 

  • Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18(2):233–234

    Article  CAS  PubMed  Google Scholar 

  • Serraj R, Krishnamurthy L, Kashiwagi JW, Kumar J, Chra S, Crouch JH (2004) Variation in root traits of chickpea (Cicer arietinum L.) grown under terminal drought. Field Crops Res 88:115–127

    Article  Google Scholar 

  • Sheshshayee MS, Bindumadhava H, Shankar AG, Prasad TG, Udayakumar M (2003) Breeding strategies to exploit water use efficiency for crop improvement. J Plant Biol 30:253–268

    Google Scholar 

  • Sheshshayee S, Shashidhar GP, Madhura JN, Beena R, Prasad TG, Udayakumar M (2011) Phenotyping groundnuts for adaptation to drought. In: Mannevaux P (eds) Phenotyping document II2 Legumes CIMMYT, pp 371–391

  • Sheshshayee MS, Mohankumar MV, Raju BR, Pratibha MD, Rajanna MP, Mohanraju B, Udayakumar M (2013) Enhancing water use efficiency besides effective use of water is a potential strategy in developing rice cultivars suitable for semi-irrigated aerobic cultivation. International Dialogue on perception Prospects of Designer Rice Society for Advancement of Rice Research, Directorate of Rice Research, Hyderabad, India, pp 261–272

  • Songsri P, Jogloy S, Holbrook CC, Kesmala T, Vorasoot N, Akkasaeng C, Patanothai A (2008) Association of root, specific leaf area SPAD chlorophyll meter reading to water use efficiency of peanut under different available soil water. Agric Water Manag 96:790–798

    Article  Google Scholar 

  • Sunil CM, Shankaralingappa BC (2014) Rooting behaviour of aerobic rice under integrated package of agro-techniques. Asian J Agric Res 8(2):105–113

    Article  CAS  Google Scholar 

  • Swamy BPM, Kumar A (2013) Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotechnol Adv 31:1308–1318

    Article  CAS  PubMed  Google Scholar 

  • Swamy BPM, Ahmed HU, Henry A et al (2013) Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLoS One 8:e62795

    Article  PubMed  Google Scholar 

  • This D, Comstock J, Courtois B, Xu Y, AhmadiN Vonhof WM, Fleet C, Setter T, Mccouch S (2010) Genetic analysis of water use efficiency in rice (Oryza sativa L.) at the leaf level. Rice 3:72–86

    Article  Google Scholar 

  • Thudi M, Upadhyaya HD, Rathore A, Gaur PM, Krishnamurthy L, Manish R, Nayak Spurthi N, Chaturvedi SK, Basu PS, Gangarao NVPR, Fikre A, Kimurto P, Sharma PC, Sheshashayee MS, Tobita S, Kashiwagi J, Ito Osamu, Killian A, Varshney RK (2014) Genetic dissection of drought and heat tolerance in chickpea through genome-wide and candidate gene-based association mapping approaches. PLoS One 9(5):e96758

    Article  PubMed  PubMed Central  Google Scholar 

  • Tuberosa R (2012) Phenotyping for drought tolerance of crops in the genomics era. Front Physiol 3:1–26

    Article  Google Scholar 

  • Uga Y, Sugimoto K, Ogawa S, Rane J, Ishitani M (2013) Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nat Genet 45:1097–1102

    Article  CAS  PubMed  Google Scholar 

  • Venuprasad R, Lafitte HR, Atlin GN (2007) Response to direct selection for grain yield under drought stress in Rice. Crop Sci 47:285–293

    Article  Google Scholar 

  • Venuprasad R, Dalid CO, Del Valle M, Zhao D, Espiritu M, Sta Cruz MT, Amante M, Kumar A, Atlin G (2009) Identification and characterization of large-effect quantitative trait loci (QTL) for grain yield under lowland drought stress and aerobic conditions in rice using bulk segregant analysis (BSA). Theor Appl Genet 120:177–190

    Article  PubMed  Google Scholar 

  • 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 12:89

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wade LJ, Bartolome V, Mauleon R, Vasant VD, Prabakar SM, Chelliah M et al (2015) Environmental response and genomic regions correlated with rice root growth and yield under drought in the Oryza SNP panel across multiple study systems. PLoS One 10(4):e0124127

    Article  PubMed  PubMed Central  Google Scholar 

  • Waugh R, Jannink JL, Muehlbauer GJ, Ramsay L (2009) The emergence of whole genome association scans in barley. Curr Opin Plant Biol 12:218–222

    Article  CAS  PubMed  Google Scholar 

  • Wu W, Cheng S (2014) Root genetic research, an opportunity and challenge to rice improvement. Field Crops Res 165:111–124

    Article  Google Scholar 

  • Yan WG, Li Yong, Hesham A, Agrama Luo D, Gao F, Lu X, Guangjun R (2009) Association mapping of stigma spikelet characteristics in rice (Oryza sativa L.). Mol Breed 24(3):277–292

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu J, Pressoir G, Briggs W, Vroh BI, Yamasaki M, Doebley JF (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat Genet 38:203–208

    Article  CAS  PubMed  Google Scholar 

  • Yue B, Xue W, Xiong L, Yu X, Luo L, Cui K, Jin D, Xing Y, Zhang Q (2006) Genetic basis of drought resistance at reproductive stage in rice: separation of drought tolerance from drought avoidance. Genetics 172:1213–1228

    Article  PubMed  PubMed Central  Google Scholar 

  • Zhang P, Li J, Li X, Liu X, Zhao X, Lu Y (2011) Population structure genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR Markers. PLoS One 6(12):e27565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Zhao KT, Eizenga GC, Wright MH, Ali ML, Price AH, Norton GJ, Islam MR, Reynolds A, Mezey J, Mcclung AM, Bustamante CD, McCouch SR (2011) Genome-wide association mapping reveals rich genetic architecture of complex traits in Oryza sativa. Nat Commun 2(467):1–10

    Google Scholar 

  • Zheng BS, Yang L, Zhang WP (2003) Mapping QTLs and candidate genes for rice root traits under different water-supply conditions and comparative analysis across three populations. Theor Appl Genet 107(8):1505–1515

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Authors acknowledge the financial help from DBT program support (BT/01/COE/05/03) and Niche Area of Excellence Program of ICAR (F.No. 10-(6)/2005 EP&D). We also thank the DBT-HUB program that supported the fellowship to Raju BR. The authors gratefully acknowledge the Central Rice Research Institute (CRRI), Cuttack, Odisha, India, for kindly sharing seeds of germplasm accessions. The help rendered by Dr. Padmini Swain, Principal Scientist, Plant Physiology, CRRI, in identifying the germplasm accessions is acknowledged. Authors thank all the phenotyping team in UAS (Bangalore) and ZARS Mandya for their support. Special thanks to Mr. Lalith Kishor, GE Healthcare for his technical support during genotyping. The authors acknowledge the two anonymous referees whose comments significantly improved the overall effectiveness of the manuscript.

Author information

Author notes

  1. Bheemanahalli R. Raju

    Present address: International Rice Research Institute, Los Baños, Laguna, Philippines

Authors and Affiliations

  1. Department of Crop Physiology, University of Agricultural Sciences, GKVK, Bengaluru, 560065, India

    Bheemanahalli R. Raju, Malagondanahalli V. Mohankumar, Kambalimath K. Sumanth, Makarla Udayakumar, Trichy G. Prasad & Madavalam S. Sheshshayee

  2. Zonal Agricultural Research Station, VC Farm, Mandya, 571405, India

    Mavinahalli P. Rajanna

Authors
  1. Bheemanahalli R. Raju
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Malagondanahalli V. Mohankumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kambalimath K. Sumanth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mavinahalli P. Rajanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Makarla Udayakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Trichy G. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Madavalam S. Sheshshayee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Madavalam S. Sheshshayee.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raju, B.R., Mohankumar, M.V., Sumanth, K.K. et al. Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping. Mol Breeding 36, 35 (2016). https://doi.org/10.1007/s11032-016-0457-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11032-016-0457-z

Keywords

Search

Navigation