, Volume 204, Issue 2, pp 395–405 | Cite as

Identification of QTL for leaf angle and leaf space above ear position across different environments and generations in maize (Zea mays L.)

  • Xining Chen
  • De Xu
  • Zheng Liu
  • Tingting Yu
  • Xiupeng Mei
  • Yilin Cai


The yield of maize (Zea mays L.) is affected by the plant architecture which is associated with the distribution of light within canopy and utilization of solar energy within population. Plant architecture of maize is mainly characterized by leaf angle (LA) and leaf space (LS). To analyze the genetic mechanism of LA and LS above ear position in maize, a genetic linkage map composed of 212 simple sequence repeat markers was constructed based on a F2 population derived from the cross between the compact inbred line CY5 and the expanded inbred line YL106. The map spanned 1,153.39 cM in length with an average interval of 5.44 cM. By using the inclusive composite interval mapping method, QTLs for LA and LS above ear position were identified based on two field mapping populations consisted of 144 F2:3 families in three environments and 144 F4 families, respectively. In F2:3 population, three consistent QTLs qSecLA1a, qThiLA1a, and qThiLS7 were detected in both single-environment analysis and joint-environment analysis. The qSecLA1a between bnlg1803 and bnlg1007 on chromosome 1.02 explained 26.99 and 18.51 % of the phenotypic variation, qThiLA1a between bnlg1803 and bnlg1007 on chromosome 1.02 explained 24.14 and 22.00 % of the phenotypic variation and qThiLS7 between bnlg1305 and umc1787 on chromosome 7.02/7.03 explained 13.77 and 9.96 % of the phenotypic variation in single environment analysis while the three QTLs explained 29.10, 31.86 and 11.20 % of the phenotypic variation, respectively in joint environment analysis. Moreover, there were major QTLs near bnlg1803 on chromosome 1 for SecLA and ThiLA stably expressing in F2:3 and F4 generations. The results of this study could provide references for genetic modification and molecular marker-assisted selection for LA and LS in maize.


Maize Quantitative trait loci Leaf angle Leaf space 



The authors would like to thank the key Research Projects of Chongqing (cstc2012ggC80003, cstc2012ggC80006) for providing financial support.


  1. Boer MP, Wright D, Feng L, Podlich DW, Luo L, Cooper M, van Eeuwijk FA (2007) A mixed-model quantitative trait loci (QTL) analysis for multiple-environment trial data using environmental covariables for QTL-by-environment interactions, with an example in maize. Genetics 177:1801–1813PubMedCentralPubMedCrossRefGoogle Scholar
  2. Chen DH, Ronald P (1999) A rapid DNA minipreparation method suitable for AFLP and other PCR applications. Plant Mol Biol Rep 17:53–57CrossRefGoogle Scholar
  3. Duvick DN (2005) The contribution of breeding to yield advances in maize (Zea mays L.). Advances in agronomy, vol 86. Elsevier, San Diego, pp 83–145Google Scholar
  4. Khush GS (2001) Green revolution: the way forward. Nat Rev Genet 2:815–822PubMedCrossRefGoogle Scholar
  5. Knapp E, Fischer S, Zinti W, Sander M, Kaiser W, Deisenhofer J, Michel H (1985) Analysis of optical spectra from single crystals of Rhodopseudomonas viridis reaction centers. Proc Natl Acad Sci USA 82:8463–8467PubMedCentralPubMedCrossRefGoogle Scholar
  6. Kosambi DD (1944) The estimation of map distance from recombination values. Ann Eugen 12:172–175CrossRefGoogle Scholar
  7. 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 121:951–959PubMedCrossRefGoogle Scholar
  8. Ku LX, Zhang J, Guo SL, Liu HY, Zhao RF, Chen YH (2012) Integrated multiple population analysis of leaf architecture traits in maize (Zea mays L.). J Exp Bot 63(1):261–274PubMedCrossRefGoogle Scholar
  9. Lambert RJ, Johnson RR (1978) Leaf angle, tassel morphology, and the performance of maize hybrids. Crop Sci 18:499–502CrossRefGoogle Scholar
  10. Li H, Ye G, Wang J (2007) A modified algorithm for the improvement of composite interval mapping. Genetics 175(1):361–374PubMedCentralPubMedCrossRefGoogle Scholar
  11. Li H, Ribaut JM, Li Z, Wang J (2008) Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations. Theor Appl Genet 116:243–260PubMedCrossRefGoogle Scholar
  12. Liao XZ, Wang J, Zhou RH, Ren ZL, Jia JZ (2008) Mining favorable alleles of QTL conferring 1000-grain weight from synthetic wheat. Acta Agron Sin 34(11):1877–1884CrossRefGoogle Scholar
  13. 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. Hereditas 29(9):1131–1138PubMedCrossRefGoogle Scholar
  14. Mickelson SM, Stuber CS, Senior L, Kaeppler SM (2002) Quantitative trait loci controlling leaf and tassel trait in a B73 × Mo17 population of maize. Crop Sci 42:1902–1909CrossRefGoogle Scholar
  15. Pendleton JW, Smith GE, Winter SR, Johnston TJ (1968) Field investigations of the relationships of leaf angle in corn (Zea mays L.) to grain yield and apparent photosynthesis. Agron J 60:422–424CrossRefGoogle Scholar
  16. Peng B, Li Y, Wang Y, Liu C, Liu Z, Tan W, Zhang Y, Wang D, Shi Y, Sun B (2011) QTL analysis for yield components and kernel-related traits in maize across multi-environments. Theor Appl Genet 122:1305–1320PubMedCrossRefGoogle Scholar
  17. Stam P (1993) Construction of integrated genetic linkage maps by means of a new computer package: join map. Plant J 3(5):739–744Google Scholar
  18. Stuber CW, Edwards M, Wendel J (1987) Molecular marker-facilitated investigation of quantitative trait loci in maize. II. Factors infuencing yield and its component traits. Crop Sci 27:639–648CrossRefGoogle Scholar
  19. Tian F, Bradbury PJ, Brown PJ, Hung H, Sun Q, Sherry FG, 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 43:159–162PubMedCrossRefGoogle Scholar
  20. Tuberosa R, Sanguineti M, Landi P, Salvi S, Casarini E, Conti S (1988a) RFLP mapping of quantitative trait loci controlling abscisic acid concentration in leaves of drought-stressed maize (Zea mays L.). Theor Appl Genet 97:744–755CrossRefGoogle Scholar
  21. Tuberosa RP, Kim T, Sanguineti M, Phillips R (1988b) Mapping QTLs for ABA concentration in leaves of drought-stressed maize (Zea mays L.). Theor Appl Genet 97:744–755CrossRefGoogle Scholar
  22. Vazin F, Hassanzadeh M, Madani A, Nassiri-Mahallati M, Nasri M (2010) Modeling light interception and distribution in mixed canopy of common cocklebur (Xanthium stramarium) in competition with corn. Planta Daninha 28:455–462Google Scholar
  23. Wang YD, Duan MX, Xing JF, Zhao JR (2008) Progress and prospect in ideal plant type breeding in maize. J Maize Sci 16(3):47–50Google Scholar
  24. Yu YT, Zhang JM, Shi YS, Song YC, Wang TY, Li Y (2006) QTL analysis for plant height and leaf angle by using different population of Maize. J Maize Sci 14(2):88–92Google Scholar
  25. Zhang J, Ku LX, Zhang WQ, Chen YH (2010) QTL mapping of internodes length above upmost ear in maize. J Maize Sci 18(4):45–48Google Scholar
  26. Zhang J, Ku LX, Han ZP, Guo LS, Liu HJ, Zhang ZZ, Cao LR, Cui XJ, Chen YH (2014) The ZmCLA4 gene in the qLA4-1 QTL controls leaf angle in maize (Zea mays L.). J Exp Bot 65(17):5063–5076PubMedCrossRefGoogle Scholar
  27. Zhu LY, Chen JT, Li D, Zhang JH, Huang YQ, Zhao YF, Song ZQ, Liu ZZ (2013) QTL mapping for stalk related traits in maize (Zea mays L.) under different densities. J Integr Agr 12(2):218–228CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Xining Chen
    • 1
  • De Xu
    • 1
  • Zheng Liu
    • 1
  • Tingting Yu
    • 1
  • Xiupeng Mei
    • 1
  • Yilin Cai
    • 1
  1. 1.Key Laboratory of Biotechnology and Crop Quality Improvement, Maize Research Institute, Ministry of AgricultureSouthwest UniversityChongqingChina

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