, 214:38 | Cite as

Identification of drought responsive QTLs during vegetative growth stage of rice using a saturated GBS-based SNP linkage map



Drought is a major abiotic constraint for rice production worldwide. The quantitative trait loci (QTLs) for drought tolerance traits identified in earlier studies have large confidence intervals due to low density linkage maps. Further, these studies largely focused on the above ground traits. Therefore, this study aims to identify QTLs for root and shoot traits at the vegetative growth stage using a genotyping by sequencing (GBS) based saturated SNP linkage map. A recombinant inbred line (RIL) population from a cross between Cocodrie and N-22 was evaluated for eight morphological traits under drought stress. Drought was imposed to plants grown in 75 cm long plastic pots at the vegetative growth stage. Using a saturated SNP linkage map, 14 additive QTLs were identified for root length, shoot length, fresh root mass, fresh shoot mass, number of tillers, dry root mass, dry shoot mass, and root-shoot ratio. Majority of the drought responsive QTLs were located on chromosome 1. The expression of QTLs varied under stress and irrigated condition. Shoot length QTLs qSL1.38 and qSL1.11 were congruent to dry shoot mass QTL qDSM1.38 and dry root mass QTL qDRM1.11, respectively. Analysis of genes present within QTL confidence intervals revealed many potential candidate genes such as laccase, Calvin cycle protein, serine threonine protein kinase, heat shock protein, and WRKY protein. Another important gene, Brevis radix, present in the root length QTL region, was known to modulate root growth through cell proliferation and elongation. The candidate genes and the QTL information will be helpful for marker-assisted pyramiding to improve drought tolerance in rice.


Candidate genes Drought tolerance Genotyping by sequencing Quantitative trait loci Recombinant inbred lines 



This research was supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture (Grant No. 2013-67013-21238). This manuscript is approved for publication by the Director of Louisiana Agricultural Experiment Station, USA as manuscript number 2018-306-31630.

Compliance with ethical standards

Conflict of interest

The authors have no conflicts of interest in this study.

Supplementary material

10681_2018_2117_MOESM1_ESM.tif (6.8 mb)
Fig. S1 (A) Experimental setup for drought experiment to evaluate root traits in rice, (B) drought stressed (left) and irrigated plants (right). Supplementary material 1 (TIFF 6983 kb)
10681_2018_2117_MOESM2_ESM.tif (2.3 mb)
Fig. S2 Frequency distributions for various root and shoot traits under drought stress condition in Cocodrie x N-22 F8 RIL population. The traits are root length, shoot length, fresh root mass, fresh shoot mass, number of tillers, dry root mass, dry shoot mass, and root-shoot ratio. The arrowheads indicate the trait means of Cocodrie, N-22, and the RIL population. Supplementary material 2 (TIFF 2399 kb)
10681_2018_2117_MOESM3_ESM.tif (1.9 mb)
Fig. S3 Difference in root length between N-22 and Cocodrie under drought stress and non-stress condition. Supplementary material 3 (TIFF 1989 kb)
10681_2018_2117_MOESM4_ESM.docx (23 kb)
Table S1 Additive QTLs for various root and shoot related traits identified by Interval Mapping (IM) in Cocodrie x N-22 RIL population under drought stress condition. Supplementary material 4 (DOCX 23 kb)
10681_2018_2117_MOESM5_ESM.docx (24 kb)
Table S2 Additive QTLs for various root and shoot related traits identified by Interval Mapping (IM) in the Cocodrie x N-22 RIL population under irrigated condition. Supplementary material 5 (DOCX 23 kb)
10681_2018_2117_MOESM6_ESM.docx (37 kb)
Table S3 Epistatic QTLs for various root and shoot related traits identified by Interval Mapping (IM) in the Cocodrie x N-22 RIL population under drought stress condition. Supplementary material 6 (DOCX 36 kb)
10681_2018_2117_MOESM7_ESM.docx (16 kb)
Table S4 Mapping of segregation distortion loci in the Cocodrie x N-22 mapping population. Supplementary material 7 (DOCX 16 kb)
10681_2018_2117_MOESM8_ESM.xlsx (68 kb)
Table S5 List of candidate genes contained in the QTL regions identified under drought stress condition. Supplementary material 8 (XLSX 68 kb)
10681_2018_2117_MOESM9_ESM.xlsx (44 kb)
Table S6 List of gene ontology terms identified for each trait using agriGO and their classification into various sub-classes. Supplementary material 9 (XLSX 43 kb)


  1. Ali ML, Pathan MS, Zhang J, Bai G, Sarkarung S, Nguyen HT (2000) Mapping QTLs for root traits in a recombinant inbred population from two indica ecotypes in rice. Theor Appl Genet 101:756–766CrossRefGoogle Scholar
  2. Basu S, Ramegowda V, Kumar A, Pereira A (2016) Plant adaptation to drought stress. Version 1. F1000Res 5:F1000. Faculty Rev. Google Scholar
  3. Blum A (2002) Drought tolerance-is it a complex trait? In: Saxena NP, OToole JC (ed) Field screening for drought tolerance in crop plants with emphasis on rice. Proceedings of an International workshop on field screening for drought tolerance in rice, December 2000, International Crop Research Institute for Semi-arid Tropics, Patancheru, India, pp 17–24Google Scholar
  4. Champoux MC, Wang G, Sarkarung S, Mackill DJ, OToole JC, Huang N, McCouch SR (1995) Locating genes associated with root morphology and drought avoidance in rice via linkage to molecular markers. Theor Appl Genet 90:969–981CrossRefPubMedGoogle Scholar
  5. Comas LH, Becker SR, Cruz VM, Byrne PF, Dierig DA (2013) Root traits contributing to plant productivity under drought. Front Plant Sci 4:442CrossRefPubMedPubMedCentralGoogle Scholar
  6. Courtois B, Shen L, Petalcorin W, Carandang S, Mauleon R, Li Z (2003) Locating QTLs controlling constitutive root traits in the rice population IAC 165 x Co39. Euphytica 134:335–345CrossRefGoogle Scholar
  7. De Leon TB, Linscombe S, Subudhi PK (2016) Molecular dissection of seedling salinity tolerance in rice (Oryza sativa L.) using a high-density GBS-based SNP linkage map. Rice 9:52CrossRefPubMedPubMedCentralGoogle Scholar
  8. Dixit S, Singh A, Kumar A (2014) Rice breeding for high grain yield under drought: a strategic solution to a complex problem. Int J Agron 2014:1–15CrossRefGoogle Scholar
  9. Elshire RJ, Glaubitz JC, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6:1–10CrossRefGoogle Scholar
  10. Glaubitz JC, Casstevens TM, Lu F, Harriman J, Elshire RJ, Sun Q, Buckler ES (2014) TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9:1–11CrossRefGoogle Scholar
  11. Gorantla M, Babu PR, Lachagari VBR, Reddy AMM, Wusirika R, Bennetzen JL, Reddy AR (2007) Identification of stress-responsive genes in an indica rice (Oryza sativa L.) using ESTs generated from drought-stressed seedlings. J Exp Bot 58:253–265CrossRefPubMedGoogle Scholar
  12. Gowda M, Venu RC, Roopalakshmi K, Sreerekha MV, Kulkarni RS (2003) Advances in rice breeding, genetics, and genomics. Mol Breed 11:337–352CrossRefGoogle Scholar
  13. Hemamalini GS, Shashidhar HE, Hittalmani S (2000) Molecular marker assisted tagging of morphological and physiological traits under two contrasting moisture regimes at peak vegetative stage in rice (Oryza sativa L.). Euphytica 112:69–78CrossRefGoogle Scholar
  14. Hijmas RJ, Serraj R (2009) Modeling spatial and temporal variation of drought in rice production. In: Serraj R, Bennett J, Hardy B (eds) Drought frontiers in rice—crop improvement for improved rainfed production. World Scientific Publishing, IRRI, Singapore, pp 19–31CrossRefGoogle Scholar
  15. Holland JB, Nyquist WE, Martinez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112Google Scholar
  16. Jiang SC, Mei C, Liang S, Yu YT, Lu K, Wu Z, Wang XF, Zhang DP (2015) Crucial roles of the pentatricopeptide repeat protein SOAR1 in Arabidopsis response to drought, salt and cold stresses. Plant Mol Biol 99:369–385CrossRefGoogle Scholar
  17. Jiang Y, Qiu Y, Hu Y, Yu D (2016) Heterologous expression of AtWRKY57 confers drought tolerance in Oryza sativa. Front Plant Sci 7:145PubMedPubMedCentralGoogle Scholar
  18. Kamoshita A, Wade LJ, Ali ML, Pathan MS, Zhang J, Sarkarung S, Nguyen HT (2002) Mapping QTLs for root morphology of a rice population adapted to rainfed lowland conditions. Theor Appl Genet 104:880–893CrossRefPubMedGoogle Scholar
  19. Kamoshita A, Babu RC, Boopathi NM, Fukai S (2008) Phenotypic and genotypic analysis of drought-resistance traits for development of rice cultivars adapted to rainfed environments. Field Crops Res 109:1–23CrossRefGoogle Scholar
  20. Keppler BD, Showalter AM (2010) IRX14 and IRX14-LIKE, Two glycosyl transferases involved in glucuronoxylan biosynthesis and drought tolerance in Arabidopsis. Mol Plant 5:834–841CrossRefGoogle Scholar
  21. Kosambi DD (1944) The estimation of map distances from recombination values. Ann Eugen 12:172–175CrossRefGoogle Scholar
  22. Kulik A, Wawer I, Krzywinska E, Bucholc M, Dobrowokska G (2011) SnRk2 protein kinases-key regulators of plant responses to abiotic stresses. OMICS 15:859–872CrossRefPubMedPubMedCentralGoogle Scholar
  23. Kwasniewski M, Golec AD, Janiak A, Chwialkhowska K, Nowakowska U, Sablok G, Szarejko I (2016) Transcriptome analysis reveals the role of the root hairs as environmental sensors to maintain plant functions under water-deficiency conditions. J Exp Bot 67:1079–1094CrossRefPubMedGoogle Scholar
  24. Lam KC, Ibrahim RK, Behdad B, Dayanandan S (2007) Structure, function, and evolution of plant O-methyltransferases. Genome 50:1001–1013CrossRefPubMedGoogle Scholar
  25. Langmead B, Salzberg S (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359CrossRefPubMedPubMedCentralGoogle Scholar
  26. Li Z, Mu P, Li C, Zhang H, Li A, Gao Y, Wang X (2005) QTL mapping of root traits in a doubled haploid population from a cross between upland and lowland japonica rice in three environments. Theor Appl Genet 110:1244–1252CrossRefPubMedGoogle Scholar
  27. Linscombe SD, Jodari F, Bollich PK, Groth DE, White LM, Chu QR, Dunand RT, Sanders DE (2000) Registration of ‘Cocodrie’ rice. Crop Sci 40:294CrossRefGoogle Scholar
  28. Liu Q, Luo L, Wang X, Shen Z, Zheng L (2017) Comprehensive analysis of rice laccase gene (OsLAC) family and ectopic expression of OsLAC10 enhances tolerance to copper stress in Arabidopsis. Int J Mol Sci 18:209CrossRefPubMedCentralGoogle Scholar
  29. Meng L, Li H, Zhang L, Wang J (2015) QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in bi-parental populations. Crop J 33:269–283CrossRefGoogle Scholar
  30. Murray MG, Thompson WF (1980) Rapid isolation of high molecular weight plant DNA. Nucl Acid Res 8:4321–4326CrossRefGoogle Scholar
  31. Nagata K, Fukuta Y, Shimizu H, Yagi T, Terao T (2002) Quantitative trait loci for sink size and ripening traits in rice (Oryza sativa L.). Breed Sci 52:259–273CrossRefGoogle Scholar
  32. Nguyen TTT, Klueva N, Chamareck V, Aarti A, Magpantay G, Millena ACM, Pathan MS, Nguyen HT (2004) Saturation mapping of QTL regions and identification of putative candidate genes for drought tolerance in rice. Mol Gen Genom 272:35–46CrossRefGoogle Scholar
  33. Ooijen JW (1999) LOD significance threshholds for QTL analysis in experimental populations of diploid species. Heredity 83:613–624CrossRefPubMedGoogle Scholar
  34. Palanog AD, Swamy BPM, Shamsudin NAA, Dixit S, Hernandez JE, Boromeo TH, Cruz PCS, Kumar A (2014) Grain yield QTLs with consistent-effect under reproductive-stage drought stress in rice. Field Crops Res 161:46–54CrossRefGoogle Scholar
  35. Pandey S, Bhandari H (2009) Drought: economic costs and research implications. In: Serraj R, Bennett J, Hardy B (eds) Drought frontiers in rice—crop improvement for improved rainfed production. World Scientific Publishing, IRRI, Singapore, pp 3–17CrossRefGoogle Scholar
  36. Prince SJ, Beena R, Gomez SM, Senthivel S, Babu RC (2015) Mapping consistent rice (Oryza sativa L.) yield QTLs under drought stress in target rainfed environments. Rice 8:25CrossRefPubMedCentralGoogle Scholar
  37. Rodrigues A, Santiago J, Rubio S, Saez A, Osmont KS, Gadea J, Hardtke CS, Rodriguez P (2009) The short-rooted phenotype of the brevis radix mutant partly reflects root abscisic acid hypersensitivity. Plant Physiol 149:1917–1928CrossRefPubMedPubMedCentralGoogle Scholar
  38. Saikumar S, Gouda PK, Saiharini A, Varma CMK, Vineesha O, Padmavathi G, Shenoy VV (2014) Major QTL for enhancing rice grain yield under lowland reproductive drought stress identified using an O. sativa/O. glaberrima introgression line. Field Crops Res 163:119–131CrossRefGoogle Scholar
  39. Sandhu N, Singh A, Dixit S, Cruz MTS, 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 15:63CrossRefPubMedPubMedCentralGoogle Scholar
  40. SAS Institute Inc. (2011) Base SAS® 9.3 procedures guide. SAS Institute Inc, CaryGoogle Scholar
  41. Shamsudin NAA, Swamy BPM, Ratnam W, Sta Cruz MT, Raman A, Kumar A (2016a) Marker-assisted pyramiding of drought yield QTLs into a popular Malaysian rice cultivar, MR219. BMC Genet 17:30CrossRefPubMedPubMedCentralGoogle Scholar
  42. Shamsudin NAA, Swamy BPM, Ratnam W, Sta Cruz MT, Sandhu N, Raman AK, Kumar A (2016b) Pyramiding of drought yield QTLs into a high quality Malaysian rice cultivar MRQ74 improves yield under reproductive stage drought. Rice 9:21CrossRefPubMedPubMedCentralGoogle Scholar
  43. Shen L, Courtois B, McNally KL, Robin S, Li Z (2001) Evaluation of near-isogenic lines of rice introgressed with QTLs for root depth through marker-aided selection. Theor Appl Genet 103:75–83CrossRefGoogle Scholar
  44. Sinha P, Pazhamala T, Singh VK, Saxena RK, Krishnamurthy L, Azam S, Khan AW, Varshney RK (2016) Identification and validation of selected universal stress protein domain containing drought-responsive genes in pigeonpea (Cajanus cajan L.). Front Plant Sci 6:1065CrossRefPubMedPubMedCentralGoogle Scholar
  45. Srividhya A, Vemireddy LR, Ramanarao PV, Sridhar S, Jayaprada M, Anuradha G, Srilakshmi B, Reddy HK, Hariprasad AS, Siddiq EA (2011) Molecular mapping of QTLs for drought related traits at seedling stage under PEG induced stress conditions in rice. Am J Plant Sci 2:190–201CrossRefGoogle Scholar
  46. Swamy BPM, Kumar A (2013) Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotech Adv 31:1308–1318CrossRefGoogle Scholar
  47. Swamy BPM, Shamsudin NAA, Rahman SNA, Mauleon R, Ratnam W, Cruz MTS, Kumar A (2017) Association mapping of yield and yield-related traits under reproductive stage drought stress in rice (Oryza sativa L.). Rice 10:21CrossRefPubMedPubMedCentralGoogle Scholar
  48. Tian T, Liu Y, Yan H, You Q, Yi X, Du Z, Xu W, Su Z (2017) AgriGO v2.0: a GO analysis toolkit for the agricultural community, 2017 update. Nucl Acids Res. Google Scholar
  49. Uga Y, Sugimoto K, Ogawa S, Tane J, Ishitani M, Hara N, Kitomi Y, Inukai Y, Ono K, Kanno N et al (2013) Control of root system architecture by deeper rooting 1 increases rice yield under drought conditions. Nat Gent 45:1097–1102CrossRefGoogle Scholar
  50. Venuprasad R, Bool ME, Quiatchon L, Cruz MTS, Amante M, Atlin GN (2012) A large-effect QTL for rice grain yield under upland drought stress on chromosome 1. Mol Breed 30:535–547CrossRefGoogle Scholar
  51. Vikram P, Swamy BPM, Dixit S, Ahmed HU, Cruz MTS, 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 12:89CrossRefPubMedPubMedCentralGoogle Scholar
  52. Vikram P, Swamy BPM, Dixit S, Singh R, Singh BP, Miro B, Kohli A, Henry A, Singh NK, Kumar A (2015) Drought susceptibility of modern rice varieties: an effect of linkage of drought tolerance with undesirable traits. Sci Rep 5:14799CrossRefPubMedPubMedCentralGoogle Scholar
  53. Wang A, Yu X, Mao Y, Liu Y, Liu G, Liu Y, Niu X (2015) Overexpression of a small heat-shock-protein gene enhances tolerance to abiotic stresses in rice. Plant Breed 134:384–393CrossRefGoogle Scholar
  54. Xu CG, Li XQ, Xue Y, Huang YW, Gao J, Xing YZ (2004) Comparison of quantitative trait loci controlling seedling characteristics at two seedling stages using rice recombinant inbred lines. Theor Appl Genet 109:640–647PubMedGoogle Scholar
  55. Yadav R, Courtois B, Huang N, McLaren G (1997) Mapping genes controlling root morphology and root distribution in a doubled-haploid population of rice. Theor Appl Genet 94:619–632CrossRefGoogle Scholar
  56. Yan YS, Chen XY, Yang K, Sun ZX, Fu YP, Zhang YM, Fang RX (2011) Overexpression of an F-box protein gene reduces abiotic stress tolerance and promotes root growth in rice. Mol Plant 4:190–197CrossRefPubMedGoogle Scholar
  57. Yoshida S, Hasegawa S (1985) The rice root system: its development and function. Drought resistance in crops with emphasis on rice. International Rice Research Institute, Philippines, pp 97–114Google Scholar
  58. Zhang J, Zheng HG, Aarti A, Pantuwan G, Nguyen TT, Tripathy JN, Sarial AK, Robin S, Babu RC, Nguyen BD, Sarkarung S, Blum A, Nguyen HT (2001a) Locating genomic regions associated with components of drought resistance in rice: comparative mapping within and across species. Theor Appl Genet 103:19–29CrossRefGoogle Scholar
  59. Zhang WP, Shen XY, Wu P, Hu B, Liao CY (2001b) QTLs and epistasis for seminal root length under a different water supply in rice (Oryza sativa L.). Theor Appl Genet 103:118–123CrossRefGoogle Scholar
  60. Zheng BS, Yang L, Zhang WP, Mao CZ, Wu YR, Yi KK, Liu FY, Wu P (2003) Mapping QTLs and candidate genes for rice root traits under different water-supply conditions and comparative analysis across three populations. Theor Appl Genet 107:1505–1515CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Plant, Environmental, and Soil SciencesLouisiana State University Agricultural CenterBaton RougeUSA

Personalised recommendations