, Volume 144, Issue 6, pp 651–664 | Cite as

Genome wide association study (GWAS) for grain yield in rice cultivated under water deficit

  • Gabriel Feresin Pantalião
  • Marcelo Narciso
  • Cléber Guimarães
  • Adriano Castro
  • José Manoel Colombari
  • Flavio Breseghello
  • Luana Rodrigues
  • Rosana Pereira Vianello
  • Tereza Oliveira Borba
  • Claudio Brondani


The identification of rice drought tolerant materials is crucial for the development of best performing cultivars for the upland cultivation system. This study aimed to identify markers and candidate genes associated with drought tolerance by Genome Wide Association Study analysis, in order to develop tools for use in rice breeding programs. This analysis was made with 175 upland rice accessions (Oryza sativa), evaluated in experiments with and without water restriction, and 150,325 SNPs. Thirteen SNP markers associated with yield under drought conditions were identified. Through stepwise regression analysis, eight SNP markers were selected and validated in silico, and when tested by PCR, two out of the eight SNP markers were able to identify a group of rice genotypes with higher productivity under drought. These results are encouraging for deriving markers for the routine analysis of marker assisted selection. From the drought experiment, including the genes inherited in linkage blocks, 50 genes were identified, from which 30 were annotated, and 10 were previously related to drought and/or abiotic stress tolerance, such as the transcription factors WRKY and Apetala2, and protein kinases.


Oryza sativa L. SNPs Rice core collection Genotyping by sequencing 



National Council for Scientific and Technological Development (CNPq) for the grants to CB and RPV; the Coordination for the Improvement of Higher Education Personnel/Ministry of Education (CAPES/MEC) for the grants to GFP; and the Brazilian Agricultural Research Corporation (EMBRAPA) for financial support for this research.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10709_2016_9932_MOESM1_ESM.docx (32 kb)
List of rice accessions drought-evaluated in field (Porangatu, F) and greenhouse (Sitis platform, S) experiments (DOCX 31 kb)
10709_2016_9932_MOESM2_ESM.docx (22 kb)
Putative annotation of rice transcripts identified by SNP markers related to yield in drought and control experiments (DOCX 22 kb)
10709_2016_9932_MOESM3_ESM.docx (21 kb)
Arabidopsis, Brachypodium, maize and sorghum transcripts homologous of rice transcripts identified by SNP markers related to yield in drought and control experiments (DOCX 21 kb)


  1. Abadie T, Cordeiro CMT, Fonseca JR, Alves RBN, Burle ML, Brondani C, Rangel PHN, Castro EM, Silva HT, Freire MS, Zimmermann FJP, Magalhães JR (2005) Construção de uma coleção nuclear de arroz para o Brasil. Pesqui Agropecu Bras 40:129–136CrossRefGoogle Scholar
  2. Alonso JM, Ecker JR (2006) Moving forward in reverse: genetic technologies to enable genome-wide phenomic screens in Arabidopsis. Nat Rev Genet 7:524–536CrossRefPubMedGoogle Scholar
  3. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265CrossRefPubMedGoogle Scholar
  4. 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 47:507–516CrossRefGoogle Scholar
  5. Biffani S, Dimauro C, Macciotta N, Rossoni A, Stella A, Biscarini F (2015) Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis. Genet Sel Evol 47:4CrossRefPubMedPubMedCentralGoogle Scholar
  6. Biscarini F, Marini S, Stevanato P, Broccanello C, Bellazzi R, Nazzicari N (2015) Developing a parsimonius predictor for binary traits in sugar beet (Beta vulgaris). Mol Breed 35:10CrossRefGoogle Scholar
  7. Biscarini F, Cozzi P, Casella L, Riccardi P, Vattari A, Orasen G, Perrini R, Tacconi G, Tondelli A, Biselli C, Cattivelli L, Spindel J, McCouch S, Abbruscato P, Valé G, Piffanelli P, Greco R (2016) Genome-Wide Association Study for traits related to plant and grain morphology, and root architecture in temperate rice accessions. PLoS One 11:e0155425CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES (2007) TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 19:2633–2635CrossRefGoogle Scholar
  9. Chen L, Song Y, Li S, Zhang L, Zou C, Yu D (2012) The role of WRKY transcription factors in plant abiotic stresses. Biochim Biophys Acta 1819:120–128CrossRefPubMedGoogle Scholar
  10. Chen L, Wang QQ, Zhou L, Ren F, Li DD, Li XB (2013) Arabidopsis CBL-interacting protein kinase (CIPK6) is involved in plant response to salt/osmotic stress and ABA. Mol Biol Rep 40:4759–4767CrossRefPubMedGoogle Scholar
  11. Cosgrove DJ (2015) Plant expansins: diversity and interactions with plant cell walls. Curr Opin Plant Biol 25:162–172CrossRefPubMedPubMedCentralGoogle Scholar
  12. Courtois B, Audebert A, Dardou A, Roques S, Ghneim-Herrera T, Droc G, Frouin J, Rouan L, Goz E, Kilian A, Ahmadi N, Dingkuhn M (2013) Genome-wide association mapping of root traits in a japonica rice panel. PLoS One. doi: 10.1371/journal.pone.0078037 Google Scholar
  13. Earl DA, Vonholdt BM (2011) STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv Genet Resour 4:359–361CrossRefGoogle Scholar
  14. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SEA (2011) Robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. doi: 10.1371/journal.pone.0019379 PubMedPubMedCentralGoogle Scholar
  15. Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B, Higgins J, Defelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D (2002) The structure of haplotype blocks in the human genome. Science 296:2225–2229CrossRefPubMedGoogle Scholar
  16. He J, Zhao X, Laroche A, Lu ZX, Liu H, Li Z (2014) Genotyping-by-Sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding. Front Plant Sci 5:484CrossRefPubMedPubMedCentralGoogle Scholar
  17. Henry R (2014) Genomics strategies for germplasm characterization and the development of climate resilient crops. Front Plant Sci 5:68. doi: 10.3389/fpls.2014.00068 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Jain M, Aashima N, Arora R, Agarwal P, Ray S, Sharma P, Kapoor S, Tyagi AK, Khurana P (2007) F-box proteins in rice. Genome-wide analysis, classification, temporal and spatial gene expression during panicle and seed development, and regulation by light and abiotic stress. Plant Physiol 143:1467–1483CrossRefPubMedPubMedCentralGoogle Scholar
  19. Jiang SY, Ma Z, Ramachandran R (2010) Evolutionary history and stress regulation of the lectin superfamily in higher plants. BMC Evol Biol 10:79. doi: 10.1186/1471-2148-10-79 CrossRefPubMedPubMedCentralGoogle Scholar
  20. Jing Y, Lin R (2015) The VQ motif-containing protein family of plant-specific transcriptional regulators. Plant Physiol 169:371–378CrossRefPubMedPubMedCentralGoogle Scholar
  21. Jisha V, Dampanaboina L, Vadassery J, Mithöfer A, Kappara S, Ramanan R (2015) Overexpression of an AP2/ERF type transcription factor OsEREBP1 confers biotic and abiotic stress tolerance in rice. PLoS One. doi: 10.1371/journal.pone.0127831 PubMedPubMedCentralGoogle Scholar
  22. Kang Y, Sakiroglu M, Krom N, Stanton-Geddes J, Wang M, Lee YC, Young ND, Udvardi M (2015) Genome-wide association of drought-related and biomass traits with HapMap SNPs in Medicago truncatula. Plant, Cell Environ 38:1997–2011. doi: 10.1111/pce.12520 CrossRefGoogle Scholar
  23. Kawahara Y, Bastide MDL, Hamilton JP, Kanamori H, Mccombie WR, Ouyang S, Schwartz DC, Tanaka T, Wu J, Zhou S, Childs KL, Davidson RM, Lin H, Quesada-Ocampo L, Vaillancourt B, Sakai H, Lee SS, Kim J, Numa H, Itoh T, Buell CR, Matsumoto T (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice 6:1–10CrossRefGoogle Scholar
  24. Kilian J, Whitehead K, Horak J, Wanke D, Weinl S, Batistic O, D’Angelo C, Bauer EB, Kudla J, Harter K (2007) The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses. Plant J 50:347–363CrossRefPubMedGoogle Scholar
  25. Kole C, Muthamilarasan M, Henry R, Edwards D et al (2015) Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects. Front Plant Sci 6:563. doi: 10.3389/fpls.2015.00563 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Kumar K, Rao KP, Sharma P, Sinha AK (2008) Differential regulation of rice mitogen activated protein kinase kinase (MKK) by abiotic stress. Plant Physiol Biochem 46:891–897CrossRefPubMedGoogle Scholar
  27. Kumar A, Dixit S, Ram T, Yadaw RB, Mishra KK, Mandal NP (2014) Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. J Exp Bot 65:6265–6278CrossRefPubMedPubMedCentralGoogle Scholar
  28. Licausi F, Ohme-Takagi M, Perata P (2013) Apetala2/ethylene responsive factor (AP2/ERF) transcription factors: mediators of stress responses and developmental programs. New Phytol 199:639–649CrossRefPubMedGoogle Scholar
  29. Ma N, Wang Y, Qiu S, Kang Z, Che S, Wang G, Huang J (2013) Overexpression of OsEXPA8, a root-specific gene, improves rice growth and root system architecture by facilitating cell extension. PLoS One. doi: 10.1371/journal.pone.0075997 Google Scholar
  30. Morillo SA, Tax RE (2006) Functional analysis of receptor-like kinases in monocots and dicots. Curr Opinion Plant Biol 9:460–469CrossRefGoogle Scholar
  31. Perez-Clemente RM, Vives V, Zandalinas SI, Lopez-Climent MF, Munoz V, Gomez-Cadenas A (2013) Biotechnological approaches to study plant responses to stress. Biomed Res Int 2013:654120. doi: 10.1155/2013/654120 CrossRefPubMedGoogle Scholar
  32. Pritchard JK, Stephens M, Donnelly P (2000) Inference of population structure using multilocus genotype data. Genetics 155:945–959PubMedPubMedCentralGoogle Scholar
  33. Rebolledo MC, Dingkuhn M, Courtois B, Gibon Y, Clément-Vidal A, Cruz DF, Duitama J, Lorieux M, Luquet D (2015) Phenotypic and genetic dissection of component traits for early vigour in rice using plant growth modelling, sugar content analyses and association mapping. J Exp Bot 66:5555–5566CrossRefPubMedPubMedCentralGoogle Scholar
  34. Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644CrossRefPubMedPubMedCentralGoogle Scholar
  35. Shamsudin NAZ, Swamy BPM, Ratnam W, Sta Cruz MT, Raman A, Kumar A (2016) Marker assisted pyramiding of drought yield QTLs into a popular Malaysian rice cultivar, MR219. BMC Genet 17:30. doi: 10.1186/s12863-016-0334-0 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Shen H, Liu C, Zhang Y, Meng X, Zhou X, Chu C, Wang X (2012) OsWRKY30 is activated by MAP kinases to confer drought tolerance in rice. Plant Mol Biol 80:241–253CrossRefPubMedGoogle Scholar
  37. Sinha AK, Jaggi M, Raghuram B, Tuteja N (2011) Mitogen-activated protein kinase signaling in plants under abiotic stress. Plant Signal Behav 6:196–203CrossRefPubMedPubMedCentralGoogle Scholar
  38. Skirycz A, Claeys H, Bodt S, Oikawa A, Shinoda S, Andriankaja M, Maleux K, Eloy NB, Coppens F, Yoo SD, Saito K, Inzé D (2011) Pause-and-stop: the effects of osmotic stress on cell proliferation during early leaf development in Arabidopsis and a role for ethylene signaling in cell cycle arrest. Plant Cell 23:1876–1888CrossRefPubMedPubMedCentralGoogle Scholar
  39. Srivastava S, Vishwakarma RK, Arafat YA, Gupta SK, Khan BM (2015) Abiotic stress induces change in Cinnamoyl CoA Reductase (CCR) protein abundance and lignin deposition in developing seedlings of Leucaena leucocephala. Physiol Mol Biol Plants 21:197–205CrossRefPubMedPubMedCentralGoogle Scholar
  40. The R Foundation for statistical computing (2016) R: a language and environment for statistical computing. R Core Team, Vienna. Accessed 30 March 2016
  41. Todaka D, Shinozaki K, Yamaguchi-Shinozaki K (2015) Recent advances in the dissection of drought-stress regulatory networks and strategies for development of drought-tolerant transgenic rice plants. Front Plant Sci 6:84. doi: 10.3389/fpls.2015.00084 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Vikram P, Swamy MB, Dixit S, Ahmed UH, 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. doi: 10.1186/1471-2156-12-89 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Vikram P, Swamy BP, 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 5:14799. doi: 10.1038/srep14799 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Wang Y, Ma N, Qiu S, Zou H, Zang G, Kang Z, Wang G, Huang J (2014) Regulation of the alpha-expansin gene OsEXPA8 expression affects root system architecture in transgenic rice plants. Mol Breed 34:47–57CrossRefGoogle Scholar
  45. Wankhede DP, Misra M, Singh P, Sinha AK (2013) Rice mitogen activated protein kinase kinase and mitogen activated protein kinase interaction network revealed by in silico docking and yeast two-hybrid approaches. PLoS One 8:e65011. doi: 10.1371/journal.pone.0065011 CrossRefPubMedPubMedCentralGoogle Scholar
  46. Wu Y, Wei W, Pang X, Wang X, Zhang H, Dong B, Xing Y, Li X, Wang M (2014) Comparative transcriptome profiling of adesert evergreen shrub, Ammopiptanthus mongolicus, in response to drought and cold stresses. BMC Genom 15:671. doi: 10.1186/1471-2164-15-671 CrossRefGoogle Scholar
  47. Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88:76–82CrossRefPubMedPubMedCentralGoogle Scholar
  48. Zhang Z, Ersoz E, Lai CQ, Todhunter RJ, Tiwari HK, Gore MA, Bradbury PJ, Yu J, Arnett DK, Ordovas JM, Buckler ES (2010) Mixed linear model approach adapted for genome-wide association studies. Nat Genet 42:355–360CrossRefPubMedPubMedCentralGoogle Scholar
  49. Zhang Z, Ober U, Erbe M, Zhang H, Gao N, He J, Li J, Simianer H (2014) Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies. PLoS One 9:e93017CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Gabriel Feresin Pantalião
    • 1
  • Marcelo Narciso
    • 2
  • Cléber Guimarães
    • 2
  • Adriano Castro
    • 2
  • José Manoel Colombari
    • 2
  • Flavio Breseghello
    • 2
  • Luana Rodrigues
    • 2
  • Rosana Pereira Vianello
    • 2
  • Tereza Oliveira Borba
    • 2
  • Claudio Brondani
    • 2
  1. 1.Escola de AgronomiaUniversidade Federal de GoiásGoiâniaBrazil
  2. 2.Embrapa Arroz e FeijãoGoiâniaBrazil

Personalised recommendations