Genomics-Assisted Breeding for Improving Stress Tolerance of Graminaceous Crops to Biotic and Abiotic Stresses: Progress and Prospects



Advances in genomics research have led to the development of high-quality reference genome data, genome-wide molecular markers, quantitative trait loci (QTL), and high-throughput genotyping platforms for cereal crops. The availability of these genomic resources has facilitated the development of breeding technologies such as genomics-assisted breeding (GAB). GAB is an advanced form of marker-assisted breeding where genome-wide genetic selection and high-density genotyping are performed to generate elite varieties with better agronomic traits. Marker-assisted selection (MAS) is a genotypic variation based indirect selection method that reduces the time and cost of breeding. The different approaches of MAS include marker-assisted backcrossing (MABC) or introgression of agronomically important alleles or QTLs with relatively large effect, marker-assisted recurrent selection (MARS) for introduction of complex traits and genomic selection (GS) based on overall molecular markers distributed throughout the genome. In view of these, the present chapter discusses the application of genetic and genomic resources in identification and mapping of stress-tolerant genes/QTLs and their application in molecular breeding. In addition, the chapter also summarizes the current status of marker-assisted selection approach for improving tolerance to drought and virus infection in major graminaceous crops. The challenges and future prospects of GAB in enhancing crop productivity under stress conditions have also been summarized.


Marker assisted selection Millets Drought QTLs Yield traits Crop production Cereal crops 



The authors’ work on cereal genetics and genomics is supported by the core grant of National Institute of Plant Genome Research, New Delhi, India. Roshan K Singh acknowledges the research fellowship received from Council of Scientific and Industrial Research, Govt. of India, India. Mehanathan Muthamilarasan and Annvi Dhaka acknowledge the research fellowship received from University Grants Commission, Govt. of India, India.


  1. Alexandrov N, Tai S, Wang W, Mansueto L, Palis K, Fuentes RR, Ulat VJ, Chebotarov D, Zhang G, Li Z, Mauleon R, Hamilton RS, McNally KL (2015) SNP–seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Res 43:D1023–D1027PubMedCrossRefGoogle Scholar
  2. Almeida GD, Makumbi D, Magorokosho C, Nair S, Borém A, Ribaut JM, Bänziger M, Prasanna BM, Crossa J, Babu R (2013) QTL mapping in three tropical maize populations reveals a set of constitutive and adaptive genomic regions for drought tolerance. Theor Appl Genet 126:583–600 PubMedCrossRefGoogle Scholar
  3. Appleby N, Edwards D, Batley J (2009) New technologies for ultra–high throughput genotyping in plants. Methods Mol Biol 513:19–39PubMedCrossRefGoogle Scholar
  4. Babu CR, Nguyen BD, Chamarerk V (2003) Genetic analysis of drought resistance in rice by molecular markers: association between secondary traits and field performance. Crop Sci 43:1457–1469CrossRefGoogle Scholar
  5. Barnabas B, Jäger K, Feher A (2008) The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ 31:11–38PubMedGoogle Scholar
  6. Bedada G, Westerbergh A, Müller T, Galkin E, Bdolach E, Moshelion M, Fridman E, Schmid KJ (2014) Transcriptome sequencing of two wild barley (Hordeum spontaneum L.) ecotypes differentially adapted to drought stress reveals ecotype–specific transcripts. BMC Genomics 15:995PubMedPubMedCentralCrossRefGoogle Scholar
  7. Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664CrossRefGoogle Scholar
  8. Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar
  9. Cardoso–Silva CB, Costa EA, Mancini MC, Balsalobre TW, Canesin LE, Pinto LR, Carneiro MS, Garcia AA, de Souza AP, Vicentini R (2014) De novo assembly and transcriptome analysis of contrasting sugarcane varieties. PLoS One 9:e88462PubMedPubMedCentralCrossRefGoogle Scholar
  10. Chen G, Wang X, Hao J, Yan J, Ding J (2015) Genome–wide association implicates candidate genes conferring resistance to maize rough dwarf disease in maize. PLoS One 10:e0142001PubMedPubMedCentralCrossRefGoogle Scholar
  11. Close TJ, Bhat PR, Lonardi S, Wu Y, Rostoks N, Ramsay L, Druka A, Stein N, Svensson JT, Wanamaker S, Bozdag S, Roose ML, Moscou MJ, Chao S, Varshney RK, Szucs P, Sato K, Hayes PM, Matthews DE, Kleinhofs A, Muehlbauer GJ, DeYoung J, Marshall DF, Madishetty K, Fenton RD, Condamine P, Graner A, Waugh R (2009) Development and implementation of high–throughput SNP genotyping in barley. BMC Genomics 10:582PubMedPubMedCentralCrossRefGoogle Scholar
  12. Crosbie TM, Eathington SR, Johnson GR, Edwards M, Reiter R, Stark S, Mohanty RG, Oyervides M, Buehler RE, Walker AK, Dobert R, Delannay X, Pershing JC, Hall MA, Lamkey KR (2006) Plant breeding: past, present, and future. In: Lamkey KR, Lee M (eds) Plant breeding: the Arnel R. Hallauer International Symposium. Blackwell, Ames, pp 3–50Google Scholar
  13. Cui Y, Lee MY, Huo N, Bragg J, Yan L, Yuan C, Li C, Holditch SJ, Xie J, Luo MC, Li D, Yu J, Martin J, Schackwitz W, Gu YQ, Vogel JP, Jackson AO, Liu Z, Garvin DF (2012) Fine mapping of the Bsr1 barley stripe mosaic virus resistance gene in the model grass Brachypodium distachyon. PLoS ONE 7, e38333PubMedPubMedCentralCrossRefGoogle Scholar
  14. Diab AA, Teulat–Merah B, This D, Ozturk NZ, Benscher D, Sorrells ME (2004) Identification of drought–inducible genes and differentially expressed sequence tags in barley. Theor Appl Genet 109:1417–1425PubMedCrossRefGoogle Scholar
  15. Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47:S154–S163CrossRefGoogle Scholar
  16. Fitzgerald TL, Powell JJ, Schneebeli K, Hsia MM, Gardiner DM, Bragg JN, McIntyre CL, Manners JM, Ayliffe M, Watt M, Vogel JP, Henry RJ, Kazan K (2015) Brachypodium as an emerging model for cereal–pathogen interactions. Ann Bot 115:717–731PubMedPubMedCentralCrossRefGoogle Scholar
  17. Fox SE, Preece J, Kimbrel JA, Marchini GL, Sage A, Youens-Clark K, Cruzan MB, Jaiswal P (2013) Sequencing and de novo transcriptome assembly of Brachypodium sylvaticum (Poaceae). Appl Plant Sci 5:3Google Scholar
  18. Ganal MW, Roder MS (2007) Microsatellite and SNP markers in wheat breeding. In: Varshney RK, Tuberosa R (eds) Genomic assisted crop improvement: genomics applications in crops, vol2. Springer, Dordrecht, pp 1–24Google Scholar
  19. Guo J, Su G, Zhang J, Wang G (2008a) Genetic analysis and QTL mapping of maize yield and associate agronomic traits under semi–arid land condition. Afr J Biotechnol 7:1829–1838CrossRefGoogle Scholar
  20. Guo P, Baum M, Varshney R, Graner A, Grando S, Ceccarelli S (2008b) QTLs for chlorophyll and chlorophyll fluorescence parameters in barley under post–flowering drought. Euphytica 163:203–214CrossRefGoogle Scholar
  21. Guo P, Baum M, Grando S, Ceccarelli S, Bai G, Li R, von Korff M, Varshney RK, Graner A, Valkoun J (2009) Differentially expressed genes between drought–tolerant and drought–sensitive barley genotypes in response to drought stress during the reproductive stage. J Exp Bot 60:3531–3544PubMedPubMedCentralCrossRefGoogle Scholar
  22. Gupta PK, Varshney RK (2000) The development and use of microsatellite markers for genetic analysis and plant breeding with emphasis on bread wheat. Euphytica 113:163–185CrossRefGoogle Scholar
  23. Habash DZ, Kehel Z, Nachit M (2009) Genomic approaches for designing durum wheat ready for climate change with a focus on drought. J Exp Bot 60:2805–2815PubMedCrossRefGoogle Scholar
  24. Habier D, Fernando RL, Dekkers JC (2007) The impact of genetic relationship information on genome–assisted breeding values. Genetics 177:2389–2397PubMedPubMedCentralGoogle Scholar
  25. Han B, Wang C, Tang Z, Ren Y, Li Y, Zhang D, Dong Y, Zhao X (2015) Genome–wide analysis of microsatellite markers based on sequenced database in Chinese spring wheat (Triticum aestivum L.). PLoS One 10:e0141540PubMedPubMedCentralCrossRefGoogle Scholar
  26. Hayano-Kanashiro C, Calderón-Vázquez C, Ibarra-Laclette E, Herrera-Estrella L, Simpson J (2009) Analysis of gene expression and physiological responses in three Mexican maize landraces under drought stress and recovery irrigation. PLoS One 4:e7531PubMedPubMedCentralCrossRefGoogle Scholar
  27. Hibino H (1990) Resistances in rice to tungro–associated viruses. Plant Dis 74:923CrossRefGoogle Scholar
  28. Horn F, Habekuß A, Stich B (2014) Genes involved in barley yellow dwarf virus resistance of maize. Theor Appl Genet 127:2575–2584Google Scholar
  29. Hospital F, Charcosset A (1997) Marker–assisted introgression of quantitative trait loci. Genetics 147:1469–1485PubMedPubMedCentralGoogle Scholar
  30. Huang L, Zhang F, Zhang F, Wang W, Zhou Y, Fu B, Li Z (2014) Comparative transcriptome sequencing of tolerant rice introgression line and its parents in response to drought stress. BMC Genomics 15:1026PubMedPubMedCentralCrossRefGoogle Scholar
  31. Humbert S, Subedi S, Cohn J, Zeng B, Bi YM, Chen X, Zhu T, McNicholas PD, Rothstein SJ (2013) Genome–wide expression profiling of maize in response to individual and combined water and nitrogen stresses. BMC Genomics 14:3PubMedPubMedCentralCrossRefGoogle Scholar
  32. Jhonson GR (2004) Marker–assisted selection in Janicke J, ed. Plant Breeding Rev 24:293–310Google Scholar
  33. Johnson SM, Lim FL, Finkler A, Fromm H, Slabas AR, Knight MR (2014) Transcriptomic analysis of Sorghum bicolor responding to combined heat and drought stress. BMC Genomics 15:456PubMedPubMedCentralCrossRefGoogle Scholar
  34. Jones MW, Redinbaugh MG, Anderson RJ, Louie R (2004) Identification of quantitative trait loci controlling resistance to maize chlorotic dwarf virus. Theor Appl Genet 110:48–57PubMedCrossRefGoogle Scholar
  35. Jones MW, Boyd EC, Redinbaugh MG (2011) Responses of maize (Zea mays L.) near isogenic lines carrying Wsm1, Wsm2, and Wsm3 to three viruses in the Potyviridae. Theor Appl Genet 123:729–740PubMedCrossRefGoogle Scholar
  36. Kato Y, Hirotsu S, Nemoto K, Yamagishi J (2008) Identification of QTLs controlling rice drought tolerance at seedling stage in hydroponic culture. Euphytica 160:423–430CrossRefGoogle Scholar
  37. Kojima H, Nishio Z, Kobayashi F, Saito M, Sasaya T, Kiribuchi-Otobe C, Seki M, Oda S, Nakamura T (2015) Identification and validation of a quantitative trait locus associated with wheat yellow mosaic virus pathotype I resistance in a Japanese wheat variety. Plant Breeding 134:373–378CrossRefGoogle Scholar
  38. Kumar S, Sehgal SK, Kumar U, Prasad PV, Joshi AK, Gill BS (2012) Genomic characterization of drought tolerance–related traits in spring wheat. Euphytica 186:265–276CrossRefGoogle Scholar
  39. Kumpatla SP, Buyyarapu R, Abdurakhmonov IY and Mammadov JA (2012) In: IY Ibrokhim (ed) Genomics–assisted plant breeding in the 21st century: technological advances and progress. Plant Breeding, ISBN: 978–953–307–932–5, InTech, RijekaGoogle Scholar
  40. Lanceras JC, Pantuwan GP, Jongdee B, Toojinda T (2004) Quantitative trait loci associated with drought tolerance at reproductive stage in rice. Plant Physiol 135:384–399 PubMedPubMedCentralCrossRefGoogle Scholar
  41. Lee JH, Muhsin M, Atienza GA, Kwak DY, Kim SM, De Leon TB, Angeles ER, Coloquio E, Kondoh H, Satoh K, Cabunagan RC, Cabauatan PQ, Kikuchi S, Leung H, Choi IR (2010) Single nucleotide polymorphisms in a gene for translation initiation factor (eIF4G) of rice (Oryza sativa) associated with resistance to Rice tungro spherical virus. Mol Plant-Microbe Interact 23:29–38PubMedCrossRefGoogle Scholar
  42. Leung H, Raghavan C, Zhou B, Oliva R, Choi IR, Lacorte V, Jubay ML, Cruz CV, Gregorio G, Singh RK, Ulat VJ, Borja FN, Mauleon R, Alexandrov NN, McNally KL, Sackville HR (2015) Allele mining and enhanced genetic recombination for rice breeding. Rice 8:34PubMedPubMedCentralCrossRefGoogle Scholar
  43. Li JY, Wang J, Zeigler RS (2014) The 3,000 rice genomes project: new opportunities and challenges for future rice research. Gigascience 3:8PubMedPubMedCentralCrossRefGoogle Scholar
  44. Liu J, Li J, Qu J, Yan S (2015a) Development of genome–wide insertion and deletion polymorphism markers from next–generation sequencing data in rice. Rice 8:63PubMedGoogle Scholar
  45. Liu Z, Xin M, Qin J, Peng H, Ni Z, Yao Y, Sun Q (2015b) Temporal transcriptome profiling reveals expression partitioning of homeologous genes contributing to heat and drought acclimation in wheat (Triticum aestivum L.). BMC Plant Biol 15:152PubMedPubMedCentralCrossRefGoogle Scholar
  46. Luo M, Liu J, Lee RD, Scully BT, Guo B (2010) Monitoring the expression of maize genes in developing kernels under drought stress using oligo–microarray. J Integr Plant Biol 52:1059–1074 PubMedCrossRefGoogle Scholar
  47. Lüpken T, Stein N, Perovic D, Habekuss A, Krämer I, Hähnel U, Steuernagel B, Scholz U, Zhou R, Ariyadasa R, Taudien S, Platzer M, Martis M, Mayer K, Friedt W, Ordon F (2013) Genomics–based high–resolution mapping of the BaMMV/BaYMV resistance gene rym11 in barley (Hordeum vulgare L.). Theor Appl Genet 126:1201–1212PubMedCrossRefGoogle Scholar
  48. Maccaferri M, Cane’ MA, Sanguineti MC, Salvi S, Colalongo MC, Massi A, Clarke F, Knox R, Pozniak CJ, Clarke JM, Fahima T, Dubcovsky J, Xu S, Ammar K, Karsai I, Vida G, Tuberosa R (2014) A consensus framework map of durum wheat (Triticum durum Desf.) suitable for linkage disequilibrium analysis and genome–wide association mapping. BMC Genomics 15:873PubMedPubMedCentralCrossRefGoogle Scholar
  49. Mace ES, Singh V, Van Oosterom EJ, Hammer GL, Hunt CH, Jordan DR (2012) QTL for nodal root angle in sorghum (Sorghum bicolor L. Moench) co–locate with QTL for traits associated with drought adaptation. Theor Appl Genet 124:97–109PubMedCrossRefGoogle Scholar
  50. Mammadov J, Aggarwal R, Buyyarapu R, Kumpatla S (2012) SNP markers and their impact on plant breeding. Int J Plant Genomics 2012:728398PubMedPubMedCentralCrossRefGoogle Scholar
  51. Marconi TG, Costa EA, Miranda HR, Mancini MC, Cardoso-Silva CB, Oliveira KM, Pinto LR, Mollinari M, Garcia AA, Souza AP (2011) Functional markers for gene mapping and genetic diversity studies in sugarcane. BMC Res Notes 4:264PubMedPubMedCentralCrossRefGoogle Scholar
  52. Mathews KL, Malosetti M, Chapman S, McIntyre L, Reynolds M, Shorter R, van Eeuwijk F (2008) Multi–environment QTL mixed models for drought stress adaptation in wheat. Theor Appl Genet 117:1077–1109PubMedCrossRefGoogle Scholar
  53. McMullen MD, Simcox KD (1995) Genomic organization of disease and insect resistance genes in maize. Mol Plant-Microbe Interact 8:811–815CrossRefGoogle Scholar
  54. Messmer R, Fracheboud Y, Bänziger M, Vargas M, Stamp P, Ribaut JM (2009) Drought stress and tropical maize: QTL–by–environment interactions and stability of QTLs across environments for yield components and secondary traits. Theor Appl Genet 119:913–930PubMedCrossRefGoogle Scholar
  55. Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome–wide dense marker maps. Genetics 157:1819–1829PubMedPubMedCentralGoogle Scholar
  56. Mir RR, Zaman-Allah M, Sreenivasulu N, Trethowan R, Varshney RK (2010) Integrated genomics, physiology and breeding approaches for improving drought tolerance in crops. Theor Appl Genet 125:625–645CrossRefGoogle Scholar
  57. Muthamilarasan M, Theriappan P, Prasad M (2013) Recent advances in crop genomics for ensuring food security. Curr Sci 105:155–158Google Scholar
  58. Myles S, Peiffer J, Brown PJ, Ersoz ES, Zhang Z, Costich DE, Buckler ES (2009) Association mapping: critical considerations shift from genotyping to experimental design. Plant Cell 21:2194–2202PubMedPubMedCentralCrossRefGoogle Scholar
  59. Nagaraja RR, Madhusudhana R, Murali Mohan S, Chakravarthi DV, Mehtre SP, Seetharama N, Patil JV (2013) Mapping QTL for grain yield and other agronomic traits in post–rainy sorghum [Sorghum bicolor (L.) Moench]. Theor Appl Genet 126:1921–1939CrossRefGoogle Scholar
  60. Nakaya A, Isobe SN (2012) Will genomic selection be a practical method for plant breeding? Ann Bot 110:1303–1316PubMedPubMedCentralCrossRefGoogle Scholar
  61. Namba S, Kashiwazaki S, Lu X, Tamura M, Tsuchizaki T (1998) Complete nucleotide sequence of wheat yellow mosaic bymovirus genomic RNAs. Arch Virol 143:631–643Google Scholar
  62. Nelson JC, Wang S, Wu Y, Li X, Antony G, White FF, Yu J (2011) Single–nucleotide polymorphism discovery by high–throughput sequencing in sorghum. BMC Genomics 12:352PubMedPubMedCentralCrossRefGoogle Scholar
  63. Ngugi K, Kimani W, Kiambi D, Mutitu EW (2013) Improving drought tolerance in Sorghum bicolor L. Moench: marker–assisted transfer of the stay–green Quantitative Trait Loci (QTL) from a characterized donor source into a local farmer variety. Int J Sci Res Knowl 1:154–162CrossRefGoogle Scholar
  64. Orjuela J, Deless EF, Kolade O, Chéron S, Ghesquière A, Albar L (2013) A recessive resistance to rice yellow mottle virus is associated with a rice homolog of the CPR5 gene, a regulator of active defense mechanisms. Mol Plant-Microbe Interact 26:1455–1463PubMedCrossRefGoogle Scholar
  65. Pandey G, Misra G, Kumari K, Gupta S, Parida SK, Chattopadhyay D, Prasad M (2013) Genome–wide development and use of microsatellite markers for large–scale genotyping applications in foxtail millet [Setaria italica (L.)]. DNA Res 20:197–207PubMedPubMedCentralCrossRefGoogle Scholar
  66. Parida SK, Dalal V, Singh AK, Singh NK, Mohapatra T (2009) Genic non–coding microsatellites in the rice genome: characterization, marker design and use in assessing genetic and evolutionary relationships among domesticated groups. BMC Genomics 10:140PubMedPubMedCentralCrossRefGoogle Scholar
  67. Peleg Z, Fahima T, Krugman T, Abbo S, Yakir D, Korol AB, Saranga Y (2009) Genomic dissection of drought resistance in durum wheat 9 wild emmer wheat recombinant inbreed line population. Plant Cell Environ 32:758–779PubMedCrossRefGoogle Scholar
  68. Petty ITD, Donald RGK, Jackson AO (1994) Multiple genetic determinants of barley stripe mosaic virus influence lesion phenotype on Chenopodium amaranticolor. Virology 198:218–226PubMedCrossRefGoogle Scholar
  69. Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares–Villegas JJ, Chapman SC (2010) Heat and drought adaptive QTL in a wheat population designed to minimize confounding agronomic effects. Theor Appl Genet 121:1001–1021PubMedPubMedCentralCrossRefGoogle Scholar
  70. Price AH, Townend J, Jones MP, Audebert A, Courtois B (2002) Mapping QTLs associated with drought avoidance in upland rice grown in the Philippines and West Africa. Plant Mol Biol 48:683–695PubMedCrossRefGoogle Scholar
  71. Priest HD, Fox SE, Rowley ER, Murray JR, Michael TP, Mockler TC (2014) Analysis of global gene expression in Brachypodium distachyon reveals extensive network plasticity in response to abiotic stress. PLoS One 9:e87499PubMedPubMedCentralCrossRefGoogle Scholar
  72. 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 8:53PubMedCrossRefGoogle Scholar
  73. Qu J, Liu J (2013) A genome–wide analysis of simple sequence repeats in maize and the development of polymorphism markers from next–generation sequence data. BMC Res Notes 6:403PubMedPubMedCentralCrossRefGoogle Scholar
  74. Rahman H, Pekic S, Lazic–Jancic V, Quarrie SA, Shah SM, Pervez A, Shah MM (2011) Molecular mapping of quantitative trait loci for drought tolerance in maize plants. Genet Mol Res 10:889–901PubMedCrossRefGoogle Scholar
  75. Ravi K, Vadez V, Isobe S, Mir RR, Guo Y, Nigam SN, Gowda MV, Radhakrishnan T, Bertioli DJ, Knapp SJ, Varshney RK (2011) Identification of several small main–effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.). Theor Appl Genet 122:1119–1132PubMedCrossRefGoogle Scholar
  76. Ribaut JM, Ragot M (2007) Marker–assisted selection to improve drought adaptation in maize: the backcross approach, perspectives, limitations, and alternatives. J Exp Bot 58:351–360PubMedCrossRefGoogle Scholar
  77. Ribaut JM, de Vicente MC, Delannay X (2010) Molecular breeding in developing countries: challenges and perspectives. Curr Opin Plant Biol 13:213–218 PubMedCrossRefGoogle Scholar
  78. Sabadin PK, Malosetti M, Boer MP, Tardin FD, Santos FG, Guimarães CT, Gomide RL, Andrade CL, Albuquerque PE, Caniato FF, Mollinari M, Margarido GR, Oliveira BF, Schaffert RE, Garcia AA, van Eeuwijk FA, Magalhaes JV (2012) Studying the genetic basis of drought tolerance in sorghum by managed stress trials and adjustments for phenological and plant height differences. Theor Appl Genet 124:1389–1402PubMedCrossRefGoogle Scholar
  79. Sehgal D, Yadav R (2010) Molecular markers based approaches for drought tolerance. In: Jain SM, Brar DS (eds) Molecular techniques in crop improvement. Springer, New York, pp 207–230CrossRefGoogle Scholar
  80. Semagn K, Beyene Y, Warburton ML, Tarekegne A, Mugo S, Meisel B, Sehabiague P, Prasanna BM (2013) Meta–analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water–stressed and well–watered environments. BMC Genomics 14:313PubMedPubMedCentralCrossRefGoogle Scholar
  81. Serraj R, Hash CT, Rivzi SMH (2005) Recent advances in markerassisted selection for drought tolerance in pearl millet. Plant Prod Sci 8:334–337CrossRefGoogle Scholar
  82. Soderlund C, Descour A, Kudrna D, Bomhoff M, Boyd L, Currie J, Angelova A, Collura K, Wissotski M, Ashley E, Morrow D, Fernandes J, Walbot V, Yu Y (2009) Sequencing, mapping, and analysis of 27,455 maize full–length cDNAs. PLoS Genet 5:e1000740PubMedPubMedCentralCrossRefGoogle Scholar
  83. Sonah H, Deshmukh RK, Sharma A, Singh VP, Gupta DK, Gacche RN, Rana JC, Singh NK, Sharma TR (2011) Genome–wide distribution and organization of microsatellites in plants: an insight into marker development in Brachypodium. PLoS One 6:e21298PubMedPubMedCentralCrossRefGoogle Scholar
  84. Suji KK, Prince KSJ, Mankhar PS, Kanagaraj P, Poornima R, Amutha K, Kavitha S, Biji KR, Gomez M, Babu RC (2012) Evaluation of rice (Oryza sativa L.) near isogenic lines with root QTLs for plant production and root traits in rainfed target populations of environment. Field Crop Res 137:89–96CrossRefGoogle Scholar
  85. Suzuki T, Murai MN, Hayashi T, Nasuda S, Yoshimura Y, Komatsuda T (2015) Resistance to wheat yellow mosaic virus in Madsen wheat is controlled by two major complementary QTLs. Theor Appl Genet 128:1569–1578PubMedCrossRefGoogle Scholar
  86. Tanksley SD, Nelson JC (1996) Advanced backcross QTL analysis: a method for the simultaneous discovery and transfer of valuable QTLs from unadapted germplasm into elite breeding lines. Theor Appl Genet 92:191–203PubMedCrossRefGoogle Scholar
  87. Tao Y, Liu Q, Wang H, Zhang Y, Huang X, Wang B, Lai J, Ye J, Liu B, Xu M (2013) Identification and fine–mapping of a QTL, qMrdd1, that confers recessive resistance to maize rough dwarf disease. BMC Plant Biol 13:145PubMedPubMedCentralCrossRefGoogle Scholar
  88. The International Brachypodium Initiative (2010) Genome sequencing and analysis of the model grass Brachypodium distachyon. Nature 463:763–768CrossRefGoogle Scholar
  89. Tuberosa R, Salvi S (2006) Genomics–based approaches to improve drought tolerance of crops. Trends Plant Sci 11:405–412PubMedCrossRefGoogle Scholar
  90. Vargas M, van Eeuwijk FA, Crossa J, Ribaut JM (2006) Mapping QTLs and QTL x environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods. Theor Appl Genet 112:1009–1023PubMedCrossRefGoogle Scholar
  91. Varshney RK, Graner A, Sorrells ME (2005) Genomics–assisted breeding for crop improvement. Trends Plant Sci 10:621–630PubMedCrossRefGoogle Scholar
  92. Varshney RK, Hoisington DA, Tyagi AK (2006) Advances in cereal genomics and applications in crop breeding. Trends Biotechnol 24:490–499PubMedCrossRefGoogle Scholar
  93. Varshney RK, Langridge P, Graner A (2007) Application of genomics to molecular breeding of wheat and barley. Adv Genet 58:121–155PubMedGoogle Scholar
  94. Varshney RK, Mohan SM, Gaur PM, Gangarao NVPR, Pandey MK, Bohra A, Sawargaonkar SL, Chitikineni A, Kimurto PK, Janila P, Saxena KB, Fikre A, Sharma M, Rathore A, Pratap A, Tripathi S, Datta S, Chaturvedi SK, Mallikarjuna N, Anuradha G, Babbar A, Choudhary AK, Mhase MB, Bharadwaj C, Mannur DM, Harer PN, Guo B, Liang X, Nadarajan N, Gowda CLL (2013) Achievements and prospects of genomics–assisted breeding in three legume crops of the semi–arid tropics. Biotechnol Adv 31:1120–1134 PubMedCrossRefGoogle Scholar
  95. Varshney RK, Kudapa HB, Pazhamala L, Chitikineni A, Thudi M, Bohra A, Gaur PM, Janila P, Fikre A, Kimurto PK, Ellis NTH (2015) Translational genomics in agriculture: some examples in grain legumes. Crit Rev Plant Sci 34:169–194CrossRefGoogle Scholar
  96. Venuprasad R, Dalid CO, Del Valle M, Zhao D, Espiritu M, Sta Cruz MT, Amante M, Kumar A, Atlin GN (2009) Identification and characterization of large–effect quantitative trait loci for grain yield under lowland drought stress in rice using bulk–segregant analysis. Theor Appl Genet 120:177–190PubMedCrossRefGoogle Scholar
  97. von Korff M, Grando S, Del Greco A, This D, Baum M, Ceccarelli S (2008) Quantitative trait loci associated with adaptation to Mediterranean dry land conditions in barley. Theor Appl Genet 117:653–669CrossRefGoogle Scholar
  98. Wang D, Pan Y, Zhao X, Zhu L, Fu B, Li Z (2011) Genome–wide temporal–spatial gene expression profiling of drought responsiveness in rice. BMC Genomics 12:149PubMedPubMedCentralCrossRefGoogle Scholar
  99. Wang S, Wong D, Forrest K, Allen A, Chao S, Huang BE, Maccaferri M, Salvi S, Milner SG, Cattivelli L, Mastrangelo AM, Whan A, Stephen S, Barker G, Wieseke R, Plieske J, International Wheat Genome Sequencing Consortium, Lillemo M, Mather D, Appels R, Dolferus R, Brown–Guedira G, Korol A, Akhunova AR, Feuillet C, Salse J, Morgante M, Pozniak C, Luo MC, Dvorak J, Morell M, Dubcovsky J, Ganal M, Tuberosa R, Lawley C, Mikoulitch I, Cavanagh C, Edwards KJ, Hayden M, Akhunov E (2014a) Characterization of polyploid wheat genomic diversity using a high–density 90,000 single nucleotide polymorphism array. Plant Biotechnol J 12:787–796PubMedPubMedCentralCrossRefGoogle Scholar
  100. Wang Q, Liu Y, He J, Zheng X, Hu J, Liu Y, Dai H, Zhang Y, Wang B, Wu W, Gao H, Zhang Y, Tao X, Deng H, Yuan D, Jiang L, Zhang X, Guo X, Cheng X, Wu C, Wang H, Yuan L, Wan J (2014b) STV11 encodes a sulphotransferase and confers durable resistance to rice stripe virus. Nat Commun 5:4768PubMedPubMedCentralCrossRefGoogle Scholar
  101. Xia X, Melchinger AE, Kuntze L, Lübberstedt T (1999) Quantitative trait loci mapping of resistance to sugarcane mosaic virus in maize. Phytopathology 89:660–667PubMedCrossRefGoogle Scholar
  102. Xiao YN, Li XH, George ML, Li MS, Zhang SH, Zheng YL (2005) Quantitative trait locus analysis of drought tolerance and yield in maize in China. Plant Mol Biol Report 23:155–165CrossRefGoogle Scholar
  103. Xiaoyun L, Kashiwazaki S, Tamura M, Namba S (1998) The 3’ terminal sequence of RNA1 of Wheat spindle streak mosaic virus canadian isolate (WSSMV-C). Eur J Plant Pathol 104:765–768Google Scholar
  104. Xu Y, Lu Y, Xie C, Gao S, Wan J, Prasanna BM (2012) Whole–genome strategies for marker–assisted plant breeding. Mol Breed 29:833–854CrossRefGoogle Scholar
  105. Xu J, Liu L, Xu Y, Chen C, Rong T, Ali F, Zhou S, Wu F, Liu Y, Wang J, Cao M, Lu Y (2013a) Development and characterization of simple sequence repeat markers providing genome–wide coverage and high resolution in maize. DNA Res 20:497–509PubMedPubMedCentralCrossRefGoogle Scholar
  106. Xu J, Li Y, Ma X, Ding J, Wang K, Wang S, Tian Y, Zhang H, Zhu XG (2013b) Whole transcriptome analysis using next–generation sequencing of model species Setaria viridis to support C4 photosynthesis research. Plant Mol Biol 83:77–87PubMedCrossRefGoogle Scholar
  107. Xu J, Yuan Y, Xu Y, Zhang G, Guo X, Wu F, Wang Q, Rong T, Pan G, Cao M, Tang Q, Gao S, Liu Y, Wang J, Lan H, Lu Y (2014) Identification of candidate genes for drought tolerance by whole–genome re–sequencing in maize. BMC Plant Biol 14:83PubMedPubMedCentralCrossRefGoogle Scholar
  108. Xue GP, McIntyre CL, Chapman S, Bower NI, Way H, Reverter A, Clarke B, Shorter R (2006) Differential gene expression of wheat progeny with contrasting levels of transpiration efficiency. Plant Mol Biol 61:863–881PubMedCrossRefGoogle Scholar
  109. Yadav RS, Sehgal D, Vadez V (2011) Using genetic mapping and genomics approaches in understanding and improving drought tolerance in pearl millet. J Exp Bot 62:397–408PubMedCrossRefGoogle Scholar
  110. Yang P, Habekuß A, Ordon F, Stein N (2014) Analysis of bymovirus resistance genes on proximal barley chromosome 4HL provides the basis for precision breeding for BaMMV/BaYMV resistance. Theor Appl Genet 127:1625–1634PubMedCrossRefGoogle Scholar
  111. Zambrano JL, Jones MW, Brenner E, Francis DM, Tomas A, Redinbaugh MG (2014) Genetic analysis of resistance to six virus diseases in a multiple virus–resistant maize inbred line. Theor Appl Genet 127:867–880PubMedCrossRefGoogle Scholar
  112. Zhang SH, Li XH, Wang ZH, George ML, Jeffers D, Wang F, Liu XD, Li MS, Yuan LX (2003) QTL mapping for resistance to SCMV in Chinese maize germplasm. Maydica 48:307–312Google Scholar
  113. Zhang Z, Deng Y, Tan J, Hu S, Yu J, Xue Q (2007) A genome–wide microsatellite polymorphism database for the indica and japonica rice. DNA Res 14:37–45PubMedPubMedCentralCrossRefGoogle Scholar
  114. Zhou G, Xu D, Xu D, Zhang M (2013) Southern rice black–streaked dwarf virus: a white–backed planthopper–transmitted fijivirus threatening rice production in Asia. Front Microbiol 4:270PubMedPubMedCentralGoogle Scholar
  115. Zhou G, Zhang Q, Tan C, Zhang XQ, Li C (2015) Development of genome–wide InDel markers and their integration with SSR, DArT and SNP markers in single barley map. BMC Genomics 16:804PubMedPubMedCentralCrossRefGoogle Scholar
  116. Zhu X, Wang H, Guo J, Wu Z, Cao A, Bie T, Nie M, You FM, Cheng Z, Xiao J, Liu Y, Cheng S, Chen P, Wang X (2012) Mapping and validation of quantitative trait loci associated with wheat yellow mosaic bymovirus resistance in bread wheat. Theor Appl Genet 124:177–188PubMedCrossRefGoogle Scholar
  117. Ziyomo C, Bernardo R (2013) Drought tolerance in maize: Indirect selection through secondary traits versus genomewide selection. Crop Sci 53:1269–1275CrossRefGoogle Scholar

Copyright information

© Springer (India) Pvt. Ltd. 2017

Authors and Affiliations

  1. 1.National Institute of Plant Genome ResearchNew DelhiIndia

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