, Volume 243, Issue 2, pp 459–471 | Cite as

A comprehensive meta-analysis of plant morphology, yield, stay-green, and virus disease resistance QTL in maize (Zea mays L.)

  • Yijun Wang
  • Jing Xu
  • Dexiang Deng
  • Haidong Ding
  • Yunlong Bian
  • Zhitong Yin
  • Yarong Wu
  • Bo Zhou
  • Ye Zhao
Original Article


Main conclusion

The meta-QTL and candidate genes will facilitate the elucidation of molecular bases underlying agriculturally important traits and open new avenues for functional markers development and elite alleles introgression in maize breeding program.

A large number of QTLs attributed to grain productivity and other agriculturally important traits have been identified and deposited in public repositories. The integration of fruitful QTL becomes a major issue in current plant genomics. To this end, we first collected QTL for six agriculturally important traits in maize, including yield, plant height, ear height, leaf angle, stay-green, and maize rough dwarf disease resistance. The meta-analysis method was then employed to retrieve 113 meta-QTL. Additionally, we also isolated candidate genes for target traits by the bioinformatic technique. Several candidates, including some well-characterized genes, GA3ox2 for plant height, lg1 and lg4 for leaf angle, zfl1 and zfl2 for flowering time, were co-localized with established meta-QTL intervals. Intriguingly, in a relatively narrow meta-QTL region, the maize ortholog of rice yield-related gene GW8/OsSPL16 was believed to be a candidate for yield. Leveraging results presented in this study will provide further insights into the genetic architecture of maize agronomic traits. Moreover, the meta-QTL and candidate genes reported here could be harnessed for the enhancement of stress tolerance and yield performance in maize and translation to other crops.


Agronomic trait Bioinformatics Candidate gene Maize breeding Meta-analysis Quantitative trait locus Yield performance 







Genome-wide association studies


Quantitative trait locus


SQUAMOSA promoter binding protein

Supplementary material

425_2015_2419_MOESM1_ESM.tif (18 kb)
QTL distribution in the maize genome. QTL for yield, plant height, and ear height are located in all ten chromosomes. QTL for leaf angle are in all ten chromosomes except chromosomes 6, 7, and 10. QTL for stay-green are positioned in chromosomes 4, 7, and 10. QTL for maize rough dwarf disease resistance are distributed in chromosomes 2, 6, 7, 8, and 10 (TIFF 18 kb)
425_2015_2419_MOESM2_ESM.pptx (397 kb)
Consensus map of collected maize QTL (PPTX 397 kb)
425_2015_2419_MOESM3_ESM.tif (97 kb)
Characterization of meta-QTL MQTL26 for maize yield. Four meta-QTL (from MQTL24 to MQTL27) are identified by the meta-analysis of eighteen QTL, including fourteen QTL for yield, two QTL for plant height, one QTL for ear height and leaf angle each. The meta-QTL MQTL26 is within a relatively narrow interval with estimated size of 346 kb. The maize ortholog of rice yield-related genes GW8/OsSPL16 in MQTL26 region is believed to be a candidate for yield. Four meta-QTL regions are delimited in color (TIFF 97 kb)
425_2015_2419_MOESM4_ESM.tif (92 kb)
Characterization of meta-QTL MQTL77 for maize yield. Two meta-QTL (MQTL76 and MQTL77) are identified by the meta-analysis of twenty-four yield-related QTL, including nine QTL for kernel weight, six QTL for ear length, three QTL for kernel number per row and kernel ratio each, and one QTL for ear diameter, ear weight, and kernel row number each. Of note, eighteen QTL collected from independent analysis display similar confidence intervals and varied peaks. The MQTL77 is at a nearly 12 cM interval containing sixty-five putative transcripts. The maize ortholog of rice yield-related genes DEP1 in MQTL77 region is believed to be a candidate for yield. Two meta-QTL regions are delimited in color (TIFF 92 kb)
425_2015_2419_MOESM5_ESM.xlsx (118 kb)
Putative transcripts in meta-QTL regions (XLSX 117 kb)
425_2015_2419_MOESM6_ESM.doc (106 kb)
Other leaf architecture-related genes (DOC 106 kb)
425_2015_2419_MOESM7_ESM.doc (44 kb)
Rubisco, leaf photosynthesis and senescence-related genes (DOC 43 kb)
425_2015_2419_MOESM8_ESM.docx (15 kb)
The overlap of meta-QTL reported by Xu et al. (2012) and in this study (DOCX 14 kb)


  1. Ajnone-Marsan P, Monfredini G, Ludwig WF, Melchinger AE, Franceschini P, Pagnotto G, Motto M (1995) In an elite cross of maize a major quantitative trait locus controls one-fourth of the genetic variation for grain yield. Theor Appl Genet 90:415–424CrossRefPubMedGoogle Scholar
  2. Bommert P, Nagasawa NS, Jackson D (2013) Quantitative variation in maize kernel row number is controlled by the FASCIATED EAR2 locus. Nat Genet 45:334–337CrossRefPubMedGoogle Scholar
  3. Bouchet S, Servin B, Bertin P, Madur D, Combes V, Dumas F, Brunel D, Laborde J, Charcosset A, Nicolas S (2013) Adaptation of maize to temperate climates: mid-density genome-wide association genetics and diversity patterns reveal key genomic regions, with a major contribution of the Vgt2 (ZCN8) locus. PLoS One 8:e71377PubMedCentralCrossRefPubMedGoogle Scholar
  4. Buckler ES, Holland JB, Bradbury PJ, Acharya CB, Brown PJ, Browne C, Ersoz E, Flint-Garcia S, Garcia A, Glaubitz JC, Goodman MM, Harjes C, Guill K, Kroon DE, Larsson S, Lepak NK, Li H, Mitchell SE, Pressoir G, Peiffer JA, Rosas MO, Rocheford TR, Romay MC, Romero S, Salvo S, Sanchez Villeda H, da Silva HS, Sun Q, Tian F, Upadyayula N, Ware D, Yates H, Yu J, Zhang Z, Kresovich S, McMullen MD (2009) The genetic architecture of maize flowering time. Science 325:714–718CrossRefPubMedGoogle Scholar
  5. Chardon F, Virlon B, Moreau L, Falque M, Joets J, Decousset L, Murigneux A, Charcosset A (2004) Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta-analysis and synteny conservation with the rice genome. Genetics 168:2169–2185PubMedCentralCrossRefPubMedGoogle Scholar
  6. Chuck G, Whipple C, Jackson D, Hake S (2010) The maize SBP-box transcription factor encoded by tasselsheath4 regulates bract development and the establishment of meristem boundaries. Development 137:1243–1250CrossRefPubMedGoogle Scholar
  7. Claeys H, De Bodt S, Inzé D (2014) Gibberellins and DELLAs: central nodes in growth regulatory networks. Trends Plant Sci 19:231–239CrossRefPubMedGoogle Scholar
  8. Fujioka S, Yamane H, Spray CR, Gaskin P, Macmillan J, Phinney BO, Takahashi N (1988) Qualitative and quantitative analyses of gibberellins in vegetative shoots of normal, dwarf-1, dwarf-2, dwarf-3, and dwarf-5 seedlings of Zea mays L. Plant Physiol 88:1367–1372PubMedCentralCrossRefPubMedGoogle Scholar
  9. Goffinet B, Gerber S (2000) Quantitative trait loci: a meta-analysis. Genetics 155:463–473PubMedCentralPubMedGoogle Scholar
  10. Hao Z, Li X, Liu X, Xie C, Li M, Zhang D, Zhang S (2010) Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize. Euphytica 174:165–177CrossRefGoogle Scholar
  11. Hartwig T, Chuck GS, Fujioka S, Klempien A, Weizbauer R, Potluri DP, Choe S, Johal GS, Schulz B (2011) Brassinosteroid control of sex determination in maize. Proc Natl Acad Sci USA 108:19814–19819PubMedCentralCrossRefPubMedGoogle Scholar
  12. Hedden P (2003) The genes of the Green Revolution. Trends Genet 19:5–9CrossRefPubMedGoogle Scholar
  13. Huang YF, Madur D, Combes V, Ky CL, Coubriche D, Jamin P, Jouanne S, Dumas F, Bouty E, Bertin P, Charcosset A, Moreau L (2010) The genetic architecture of grain yield and related traits in Zea maize L. revealed by comparing intermated and conventional populations. Genetics 186:395–404PubMedCentralCrossRefPubMedGoogle Scholar
  14. Ikeda M, Miura K, Aya K, Kitano H, Matsuoka M (2013) Genes offering the potential for designing yield-related traits in rice. Curr Opin Plant Biol 16:213–220CrossRefPubMedGoogle Scholar
  15. Jiao Y, Wang Y, Xue D, Wang J, Yan M, Liu G, Dong G, Zeng D, Lu Z, Zhu X, Qian Q, Li J (2010) Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat Genet 42:541–544CrossRefPubMedGoogle Scholar
  16. Ku LX, Zhao WM, Zhang J, Wu LC, Wang CL, Wang PA, Zhang WQ, Chen YH (2010) Quantitative trait loci mapping of leaf angle and leaf orientation value in maize (Zea mays L.). Theor Appl Genet 121:951–959CrossRefPubMedGoogle Scholar
  17. Ku L, Wei X, Zhang S, Zhang J, Guo S, Chen Y (2011) Cloning and characterization of a putative TAC1 ortholog associated with leaf angle in maize (Zea mays L.). PLoS One 6:e20621PubMedCentralCrossRefPubMedGoogle Scholar
  18. Lee S, Seo PJ, Lee HJ, Park CM (2012) A NAC transcription factor NTL4 promotes reactive oxygen species production during drought-induced leaf senescence in Arabidopsis. Plant J 70:831–844CrossRefPubMedGoogle Scholar
  19. Li XH, Liu XD, Li MS, Zhang SH (2003) Identification of quantitative trait loci for anthesis-silking interval and yield components under drought stress in maize. Acta Botanica Sinica 45:852–857Google Scholar
  20. Li YL, Niu SZ, Dong YB, Cui DQ, Wang YZ, Liu YY, Wei MG (2007) Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC2F2 population and comparison with its F2:3 population in popcorn. Theor Appl Genet 115:129–140CrossRefPubMedGoogle Scholar
  21. Li JZ, Zhang ZW, Li YL, Wang QL, Zhou YG (2011) QTL consistency and meta-analysis for grain yield components in three generations in maize. Theor Appl Genet 122:771–782CrossRefPubMedGoogle Scholar
  22. Li X, Zhu C, Yeh CT, Wu W, Takacs EM, Petsch KA, Tian F, Bai G, Buckler ES, Muehlbauer GJ, Timmermans MC, Scanlon MJ, Schnable PS, Yu J (2012) Genic and nongenic contributions to natural variation of quantitative traits in maize. Genome Res 22:2436–2444PubMedCentralCrossRefPubMedGoogle Scholar
  23. Lid SE, Meeley RB, Min Z, Nichols S, Olsen OA (2004) Knock-out mutants of two members of the AGL2 subfamily of MADS-box genes expressed during maize kernel development. Plant Sci 167:575–582CrossRefGoogle Scholar
  24. Liu R, Jia H, Cao X, Huang J, Li F, Tao Y, Qiu F, Zheng Y, Zhang Z (2012) Fine mapping and candidate gene prediction of a pleiotropic quantitative trait locus for yield-related trait in Zea mays. PLoS One 7:e49836PubMedCentralCrossRefPubMedGoogle Scholar
  25. Liu Y, Wang L, Sun C, Zhang Z, Zheng Y, Qiu F (2014) Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments. Theor Appl Genet 127:1019–1037CrossRefPubMedGoogle Scholar
  26. Lu M, Zhou F, Xie CX, Li MS, Xu YB, Marilyn W, Zhang SH (2007) Construction of a SSR linkage map and mapping of quantitative trait loci (QTL) for leaf angle and leaf orientation with an elite maize hybrid. Yi Chuan 29:1131–1138CrossRefPubMedGoogle Scholar
  27. Lu M, Xie CX, Li XH, Hao ZF, Li MS, Weng JF, Zhang DG, Bai L, Zhang SH (2011) Mapping of quantitative trait loci for kernel row number in maize across seven environments. Mol Breeding 28:143–152CrossRefGoogle Scholar
  28. Luan J, Wang F, Li Y, Zhang B, Zhang J (2012) Mapping quantitative trait loci conferring resistance to rice black-streaked virus in maize (Zea mays L.). Theor Appl Genet 125:781–791CrossRefPubMedGoogle Scholar
  29. Makarevitch I, Thompson A, Muehlbauer GJ, Springer NM (2012) Brd1 gene in maize encodes a brassinosteroid C-6 oxidase. PLoS ONE 7:e30798PubMedCentralCrossRefPubMedGoogle Scholar
  30. 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 119:913–930CrossRefPubMedGoogle Scholar
  31. Miao Y, Zentgraf U (2007) The antagonist function of Arabidopsis WRKY53 and ESR/ESP in leaf senescence is modulated by the jasmonic and salicylic acid equilibrium. Plant Cell 19:819–830PubMedCentralCrossRefPubMedGoogle Scholar
  32. Miura K, Ikeda M, Matsubara A, Song XJ, Ito M, Asano K, Matsuoka M, Kitano H, Ashikari M (2010) OsSPL14 promotes panicle branching and higher grain productivity in rice. Nat Genet 42:545–549CrossRefPubMedGoogle Scholar
  33. Moreno MA, Harper LC, Krueger RW, Dellaporta SL, Freeling M (1997) liguleless1 encodes a nuclear-localized protein required for induction of ligules and auricles during maize leaf organogenesis. Genes Dev 11:616–628CrossRefPubMedGoogle Scholar
  34. Multani DS, Briggs SP, Chamberlin MA, Blakeslee JJ, Murphy AS, Johal GS (2003) Loss of an MDR transporter in compact stalks of maize br2 and sorghum dw3 mutants. Science 302:81–84CrossRefPubMedGoogle Scholar
  35. Peiffer JA, Romay MC, Gore MA, Flint-Garcia SA, Zhang Z, Millard MJ, Gardner CA, McMullen MD, Holland JB, Bradbury PJ, Buckler ES (2014) The genetic architecture of maize height. Genetics 196:1337–1356PubMedCentralCrossRefPubMedGoogle Scholar
  36. Sabadin PK, de Souza CLJ, de Souza AP, Franco AAG (2008) QTL mapping for yield components in a tropical maize population using microsatellite markers. Hereditas 145:194–203CrossRefGoogle Scholar
  37. Salvi S, Sponza G, Morgante M, Tomes D, Niu X, Fengler KA, Meeley R, Ananiev EV, Svitashev S, Bruggemann E, Li B, Hainey CF, Radovic S, Zaina G, Rafalski JA, Tingey SV, Miao GH, Phillips RL, Tuberosa R (2007) Conserved noncoding genomic sequences associated with a flowering-time quantitative trait locus in maize. Proc Natl Acad Sci USA 104:11376–11381PubMedCentralCrossRefPubMedGoogle Scholar
  38. Salvi S, Castelletti S, Tuberosa R (2009) An updated consensus map for flowering time QTLs in maize. Maydica 54:501–512Google Scholar
  39. Sibov ST, de Souza CL, Jr Garcia AA, Silva AR, Garcia AF, Mangolin CA, Benchimol LL, de Souza AP (2003) Molecular mapping in tropical maize (Zea mays L.) using microsatellite markers. 2. Quantitative trait loci (QTL) for grain yield, plant height, ear height and grain moisture. Hereditas 139:107–115CrossRefPubMedGoogle Scholar
  40. Sosnowski O, Charcosset A, Joets J (2012) BioMercator V3: an upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics 28:2082–2083PubMedCentralCrossRefPubMedGoogle Scholar
  41. Tang H, Yan JB, Huang YQ, Zheng YL, Li JS (2005) QTL mapping of five agronomic traits in maize. Yi Chuan Xue Bao 32:203–209PubMedGoogle Scholar
  42. 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 13:145PubMedCentralCrossRefPubMedGoogle Scholar
  43. Teng F, Zhai L, Liu R, Bai W, Wang L, Huo D, Tao Y, Zheng Y, Zhang Z (2013) ZmGA3ox2, a candidate gene for a major QTL, qPH3.1, for plant height in maize. Plant J 73:405–416CrossRefPubMedGoogle Scholar
  44. Thomas H, Ougham H (2014) The stay-green trait. J Exp Bot 65:3889–3900CrossRefPubMedGoogle Scholar
  45. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Flint-Garcia S, Rocheford TR, McMullen MD, Holland JB, Buckler ES (2011) Genome-wide association study of leaf architecture in the maize nested association mapping population. Nat Genet 43:159–162CrossRefPubMedGoogle Scholar
  46. Truntzler M, Barrière Y, Sawkins MC, Lespinasse D, Betran J, Charcosset A, Moreau L (2010) Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes. Theor Appl Genet 121:1465–1482CrossRefPubMedGoogle Scholar
  47. Upadyayula N, da Silva HS, Bohn MO, Rocheford TR (2006) Genetic and QTL analysis of maize tassel and ear inflorescence architecture. Theor Appl Genet 112:592–606CrossRefPubMedGoogle Scholar
  48. Veldboom LR, Lee M (1994) Molecular-marker-facilitated studies of morphological traits in maize. II: determination of QTLs for grain yield and yield components. Theor Appl Genet 89:451–458CrossRefPubMedGoogle Scholar
  49. Veyrieras JB, Goffinet B, Charcosset A (2007) MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinformatics 8:49PubMedCentralCrossRefPubMedGoogle Scholar
  50. Wallace JG, Larsson SJ, Buckler ES (2014) Entering the second century of maize quantitative genetics. Heredity 112:30–38PubMedCentralCrossRefPubMedGoogle Scholar
  51. Walsh J, Waters CA, Freeling M (1998) The maize gene liguleless2 encodes a basic leucine zipper protein involved in the establishment of the leaf blade-sheath boundary. Genes Dev 12:208–218PubMedCentralCrossRefPubMedGoogle Scholar
  52. Wang Y, Deng D (2014) Molecular basis and evolutionary pattern of GA-GID1-DELLA regulatory module. Mol Genet Genomics 289:1–9CrossRefPubMedGoogle Scholar
  53. Wang H, Nussbaum-Wagler T, Li B, Zhao Q, Vigouroux Y, Faller M, Bomblies K, Lukens L, Doebley JF (2005) The origin of the naked grains of maize. Nature 436:714–719PubMedCentralCrossRefPubMedGoogle Scholar
  54. Wang S, Wu K, Yuan Q, Liu X, Liu Z, Lin X, Zeng R, Zhu H, Dong G, Qian Q, Zhang G, Fu X (2012a) Control of grain size, shape and quality by OsSPL16 in rice. Nat Genet 44:950–954CrossRefPubMedGoogle Scholar
  55. Wang Y, Deng D, Zhang R, Wang S, Bian Y, Yin Z (2012b) Systematic analysis of plant-specific B3 domain-containing proteins based on the genome resources of 11 sequenced species. Mol Biol Rep 39:6267–6282CrossRefPubMedGoogle Scholar
  56. Wang Y, Huang Z, Deng D, Ding D, Zhang R, Wang S, Bian Y, Yin Z, Xu X (2013) Meta-analysis combined with syntenic metaQTL mining dissects candidate loci for maize yield. Mol Breeding 31:601–614CrossRefGoogle Scholar
  57. Welcker C, Sadok W, Dignat G, Renault M, Salvi S, Charcosset A, Tardieu F (2011) A common genetic determinism for sensitivities to soil water deficit and evaporative demand: meta-analysis of quantitative trait loci and introgression lines of maize. Plant Physiol 157:718–729PubMedCentralCrossRefPubMedGoogle Scholar
  58. Wen W, Li D, Li X, Gao Y, Li W, Li H, Liu J, Liu H, Chen W, Luo J, Yan J (2014) Metabolome-based genome-wide association study of maize kernel leads to novel biochemical insights. Nat Commun 5:3438PubMedCentralPubMedGoogle Scholar
  59. Weng J, Xie C, Hao Z, Wang J, Liu C, Li M, Zhang D, Bai L, Zhang S, Li X (2011) Genome-wide association study identifies candidate genes that affect plant height in Chinese elite maize (Zea mays L.) inbred lines. PLoS One 6:e29229PubMedCentralCrossRefPubMedGoogle Scholar
  60. Weng J, Li B, Liu C, Yang X, Wang H, Hao Z, Li M, Zhang D, Ci X, Li X, Zhang S (2013) A non-synonymous SNP within the isopentenyl transferase 2 locus is associated with kernel weight in Chinese maize inbreds (Zea mays L.). BMC Plant Biol 13:98PubMedCentralCrossRefPubMedGoogle Scholar
  61. Winkler RG, Freeling M (1994) Physiological genetics of the dominant gibberellin-non responsive maize dwarf, Dwarf8 and Dwarf9. Planta 193:341–348CrossRefGoogle Scholar
  62. Xiang K, Reid LM, Zhang ZM, Zhu XY, Pan GT (2012) Characterization of correlation between grain moisture and ear rot resistance in maize by QTL meta-analysis. Euphytica 183:185–195CrossRefGoogle Scholar
  63. Xu J, Liu Y, Liu J, Cao M, Wang J, Lan H, Xu Y, Lu Y, Pan G, Rong T (2012) The genetic architecture of flowering time and photoperiod sensitivity in maize as revealed by QTL review and meta analysis. J Integr Plant Biol 54:358–373CrossRefPubMedGoogle Scholar
  64. Yang XJ, Lu M, Zhang SH, Zhou F, Qu YY, Xie CX (2008) QTL mapping of plant height and ear position in maize (Zea mays L.). Yi Chuan 30:1477–1486CrossRefPubMedGoogle Scholar
  65. Yang G, Li Y, Wang Q, Zhou Y, Zhou Q, Shen B, Zhang F, Liang X (2012) Detection and integration of quantitative trait loci for grain yield components and oil content in two connected recombinant inbred line populations of high-oil maize. Mol Breeding 29:313–333CrossRefGoogle Scholar
  66. Yoo SC, Cho SH, Zhang H, Paik HC, Lee CH, Li J, Yoo JH, Lee BW, Koh HJ, Seo HS, Paek NC (2007) Quantitative trait loci associated with functional stay-green SNU-SG1 in rice. Mol Cells 24:83–94PubMedGoogle Scholar
  67. Zhang WQ, Ku LX, Zhang J, Han ZP, Chen YH (2013) QTL analysis of kernel ratio, kernel depth, and 100-kernel weight in maize (Zea mays L.). Acta Agronomica Sinica 39:455–463CrossRefGoogle Scholar
  68. Zhang HX, Weng JF, Zhang XC, Liu CL, Yong HJ, Hao ZF, Li XH (2014a) Genome-wide association analysis of kernel row number in maize. Acta Agronomica Sinica 40:1–6CrossRefGoogle Scholar
  69. Zhang J, Ku LX, Han ZP, Guo SL, Liu HJ, Zhang ZZ, Cao LR, Cui XJ, Chen YH (2014b) The ZmCLA4 gene in the qLA4-1 QTL controls leaf angle in maize (Zea mays L.). J Exp Bot 65:5063–5076CrossRefPubMedGoogle Scholar
  70. Zheng HJ, Wu AZ, Zheng CC, Wang YF, Cai R, Shen XF, Xu RR, Liu P, Kong LJ, Dong ST (2009) QTL mapping of maize (Zea mays) stay-green traits and their relationship to yield. Plant Breeding 128:54–62CrossRefGoogle Scholar
  71. Zheng DB, Yang XH, Li JS, Yan JB, Zhang SL, He ZH, Huang YQ (2013) QTL identification for plant height and ear height based on SNP mapping in maize (Zea mays L.). Acta Agronomica Sinica 39:549–556CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Yijun Wang
    • 1
  • Jing Xu
    • 1
  • Dexiang Deng
    • 1
  • Haidong Ding
    • 2
  • Yunlong Bian
    • 1
  • Zhitong Yin
    • 1
  • Yarong Wu
    • 1
  • Bo Zhou
    • 1
  • Ye Zhao
    • 1
  1. 1.Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, Key Laboratory of Plant Functional Genomics of the Ministry of EducationYangzhou UniversityYangzhouChina
  2. 2.College of Bioscience and BiotechnologyYangzhou UniversityYangzhouChina

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