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Euphytica

, 182:409 | Cite as

Identification of genetic factors affecting plant density response through QTL mapping of yield component traits in maize (Zea mays L.)

  • Jinjie Guo
  • Zongliang Chen
  • Zhipeng Liu
  • Baobao Wang
  • Weibin Song
  • Wei Li
  • Jing Chen
  • Jingrui Dai
  • Jinsheng Lai
Article

Abstract

It is generally believed that grain yield per unit area of modern maize hybrids is related to their adaptability to high plant population density. In this study, the effects of two different plant densities (52,500 and 90,000 plants/hm2) on 12 traits associated with yield were evaluated using a set of 231 F2:3 families derived from two elite inbred lines, Zheng58 and Chang7-2. Evaluation of the phenotypes expressed under the two plant density conditions showed that high plant density condition could decrease the value of 10 measured yield component traits, while the final grain yield per hectare and the rate of kernel production were increased. Twenty-seven quantitative trait loci (QTLs) for 10 traits were detected in both high and low plant density conditions; among them, some QTLs were shown to locate in five clusters. Thirty QTLs were only detected under high plant density. These results suggest that some of the yield component traits perhaps were controlled by a common set of genes, and that kernel number per row, ear length, row number per ear, cob diameter, cob weight, and ear diameter may be influenced by additional genetic mechanisms when grown under high plant density. The QTLs identified in this study provide useful information for marker-assisted selection of varieties targeting increased plant density.

Keywords

Maize (Zea mays L.) Yield components Plant density stress Quantitative trait loci Epistasis 

Abbreviations

QTL

Quantitative trait loci

SSR

Simple sequence repeats

EW

Ear weight

GW

Grain weight per ear

100KW

100-kernel weight

EL

Ear length

ED

Ear diameter

RN

Row number per ear

KNR

Kernel number per row

10KT

10-kernel thickness

CD

Cob diameter

CW

Cob weight

RKP

Rate of kernel production

GYH

Grain yield per hectare

LOD

Logarithm of odds

LPD

Low plant density

HPD

High plant density

Notes

Acknowledgment

This research was supported by the “973” program from the Ministry of Science and Technology of China (2009CB118400).

References

  1. Bateson W (1909) Mendel’s principles of heredity. Cambridge University Press, CambridgeGoogle Scholar
  2. Cao G, Zhu J, He C, Gao Y, Yan J, Wu P (2001) Impact of epistasis and QTL × environment interaction on the developmental behavior of plant height in rice (Oryza sativa L.). Theor Appl Genet 103:153–160CrossRefGoogle Scholar
  3. Casa AM, Brouwer C, Nagel A, Wang LJ, Zhang Q, Kresovich S, Wessler SR (2000) The MITE family heartbreaker (Hbr): molecular markers in maize. Proc Natl Acad Sci USA 97(18):10083–10089PubMedCrossRefGoogle Scholar
  4. Frova C, Krajewski P, Fonzo ND, Villa M, Sari-Gorla M(1999) Genetic analysis of drought tolerance in maize by molecular markers I. Yield components. Theor Appl Genet 99:280–288Google Scholar
  5. Gonzalo M, Vyn TJ, Holland JB, McIntyre LM (2006) Mapping density response in maize: a direct approach for testing genotype and treatment interactions. Genetics 173:331–348PubMedCrossRefGoogle Scholar
  6. Gonzalo M, Holland JB, Vyn TJ, McIntyre LM (2010) Direct mapping of density response in a population of B73 × Mo17 recombinant inbred lines of maize (Zea Mays L.). Heredity 104:583–599PubMedCrossRefGoogle Scholar
  7. Grant V (1981) Plant speciation. Columbia University Press, New YorkGoogle Scholar
  8. Han LZ, Qiao YL, Zhang SY, Zhang YY, Cao GL, Kim J, Lee K, Koh H (2007) Identification of quantitative trait loci for cold response of seedling vigor traits in rice. J Genet Genomics 34(3):239–246PubMedCrossRefGoogle Scholar
  9. Knapp SJ, Stroup WW, Ross WM (1985) Exact confidence intervals for heritability on a progeny mean basis. Crop Sci 25:192–194Google Scholar
  10. 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
  11. Lai JS, Li RQ, Xu X, Jin WW, Xu ML, Zhao HN, Xiang ZK, SongWB, Ying K, Zhang M, JiaoYP, Ni PX, Zhang JG, Li D, Guo XS, Ye KX, Jian M, Wang B, Zheng HS, Liang HQ, Zhang XQ, Wang SC, Chen SJ, Li JS, Fu Y, Springer NM, Yang HM, Wang J, Dai JR, Schnable PS,Wang J (2010) Genome-wide patterns of genetic variation among elite maize inbred lines. Nat Genet 42:1027–1030Google Scholar
  12. Lander ES, Green P, Abrahamson J, Barlow A, Daly MJ, Lincoln SE, Newburg L (1987) MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181PubMedCrossRefGoogle Scholar
  13. Laperche A, Maryse BH, Heumez E, Gardet O, Hanocq E, FlorenceDB Gouis JL (2007) Using genotype × nitrogen interaction variables to evaluate the QTL involved in wheat tolerance to nitrogen constraints. Theor Appl Genet 115:399–415PubMedCrossRefGoogle Scholar
  14. Li HY, Wang LF, Tang BJ, Wang ZH (2009) Research on the genetic structure and heterosis of Zhengdan958. J Maize Sci 17(1):28–31(in Chinese)Google Scholar
  15. Li M, Guo XH, Zhang M, Wang XP, Zhang GD, Tian YC, Wang ZL (2010) Mapping QTLs for grain yield and yield components under high and low phosphorus treatments in maize (Zea mays L.). Plant Sci 178:454–462CrossRefGoogle Scholar
  16. Lu GH, Tan JH, Yan JB, Ma XQ, Li JS, Chen SJ, Ma JC, Liu ZX, Dai JR (2006) Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol 48(10):1233–1243CrossRefGoogle Scholar
  17. Malosetti M, Ribaut JM, Vargas M, Crossa J, Eeuwijk FA (2008) A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize. Euphytica 161:241–257CrossRefGoogle Scholar
  18. Pelleschi S, Guy S, Kim JK, Pointe C, Mah′e A, Barthes L, Leonardi A, Prioul JL (1999) Ivr2, a candidate gene for a QTL of vacuolar invertase activity in maize leaves. Gene-specific expression under water stress. Plant Mol Biol 39: 373–380Google Scholar
  19. Phillips P, Whitlock M, Fowler K (2001) Inbreeding changes the shape of the genetic covariance matrix in Drosophila melanogaster. Genetics 158:1137–1145Google Scholar
  20. Ribaut JM, Jiang C, Gonzalez-de-Leon D, Edmeades GO, Hoisington DA (1997) Identification of quantitative trait loci under drought conditions in tropical maize.2. Yield components and marker-assisted selection strategies. Theor Appl Genet 94:887–896CrossRefGoogle Scholar
  21. Séne M, Thévenot C, Hoffmann D, Causse BénétrixM F, Prioul JL (2001) QTLs for grain dry milling properties, composition and vitreousness in maize recombinant inbred lines. Theor Appl Genet 102:591–599CrossRefGoogle Scholar
  22. Stuber CW, Edwards MD, Wendel J (1987) F1 Molecular marker facilitated investigations of quantitative trait loci in maize. II. Factors influencing yield and its component traits. Crop Sci 27:639–648CrossRefGoogle Scholar
  23. Wang S, Basten CJ, Zeng ZB (2006) Windows QTL Cartographer 2.5. Department of Statistics. North Carolina State University, RaleighGoogle Scholar
  24. Wang JK, Chapman SC, Bonnett DG, Rebetzke GJ (2009) Simultaneous selection of major and minor genes: use of QTL to increase selection efficiency of coleoptile length of wheat (Triticum aestivum L.). Theor Appl Genet 119:65–74Google Scholar
  25. Yue B, Xue WY, Luo LJ, Xing YZ (2008) Identification of quantitative trait loci for four morphologic traits under water stress in rice (Oryza sativa L.). J Genet Genomics 35:569–575PubMedCrossRefGoogle Scholar
  26. Zeng ZB (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedGoogle Scholar
  27. Zhang ZH, Su L, Li W, Chen W, Zhu YG (2005) A major QTL conferring cold tolerance at the early seedling stage using recombinant inbred lines of rice (Oryza sativa L.). Plant Sci 168:527–534CrossRefGoogle Scholar
  28. Zhu J (1998) Mixed-model approaches of mapping genes for complex quantitative traits. In: Wang LZ, Dai JR (eds) Proceedings of the genetics and crop breed of China. Chinese Agricultural Science and Technology Publication House, Beijing, pp 19–20Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Jinjie Guo
    • 1
  • Zongliang Chen
    • 1
  • Zhipeng Liu
    • 1
  • Baobao Wang
    • 1
  • Weibin Song
    • 1
  • Wei Li
    • 1
  • Jing Chen
    • 1
  • Jingrui Dai
    • 1
  • Jinsheng Lai
    • 1
  1. 1.State Key Laboratory of Agrobiotechnology and National Maize Improvement CenterChina Agricultural UniversityBeijingChina

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