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Theoretical and Applied Genetics

, Volume 126, Issue 9, pp 2257–2266 | Cite as

QTL mapping of stalk bending strength in a recombinant inbred line maize population

  • Haixiao Hu
  • Wenxin Liu
  • Zhiyi Fu
  • Linda Homann
  • Frank Technow
  • Hongwu Wang
  • Chengliang Song
  • Shitu Li
  • Albrecht E. MelchingerEmail author
  • Shaojiang ChenEmail author
Original Paper

Abstract

Stalk bending strength (SBS) is a reliable indicator for evaluating stalk lodging resistance of maize plants. Based on biomechanical considerations, the maximum load exerted to breaking (F max), the breaking moment (M max) and critical stress (σ max) are three important parameters to characterize SBS. We investigated the genetic architecture of SBS by phenotyping F max, M max and σ max of the fourth internode of maize plants in a population of 216 recombinant inbred lines derived from the cross B73 × Ce03005 evaluated in four environments. Heritability of F max, M max and σ max was 0.81, 0.79 and 0.75, respectively. F max and σ max were positively correlated with several other stalk characters. By using a linkage map with 129 SSR markers, we detected two, three and two quantitative trait loci (QTL) explaining 22.4, 26.1 and 17.2 % of the genotypic variance for F max, M max and σ max, respectively. The QTL for F max, M max and σ max located in adjacent bins 5.02 and 5.03 as well as in bin 10.04 for F max were detected with high frequencies in cross-validation. As our QTL mapping results suggested a complex polygenic inheritance for SBS-related traits, we also evaluated the prediction accuracy of two genomic prediction methods (GBLUP and BayesB). In general, we found that both explained considerably higher proportions of the genetic variance than the values obtained in QTL mapping with cross-validation. Nevertheless, the identified QTL regions could be used as a starting point for fine mapping and gene cloning.

Keywords

Quantitative Trait Locus Quantitative Trait Locus Analysis Quantitative Trait Locus Mapping Genomic Selection Segregation Distortion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

SBS

Stalk bending strength

RPR

Rind penetrometer resistance

NIRS

Near-infrared reflectance spectroscopy

FIAG

The fourth internode above ground

Fmax

The maximum load exerted to breaking

Mmax

Breaking moment

σmax

Critical stress

Ld

Larger diameter of cross section

Sd

Smaller diameter of cross section

InL

Internode length

FreW

Fresh weight of the internode

DryW

Dry weight of the internode

InW

Internode water content

FreW/V

Fresh weight of internode per unit volume

DryW/V

Dry weight of internode per unit volume

ADL/V

Acid detergent lignin content per unit volume

CEL/V

Cellulose content per unit volume

Notes

Acknowledgments

We thank Lianying Yu, Baohua Liu and Yan Luo of China Agricultural University for their help in the three-point bending test. We also thank H. Fritz Utz, Tobias Schrag and Xuefei Mi for their suggestions on the data analysis. This research was supported by grants from the Modern Maize Industry Technology System Foundation of China (No. nycytx-02) to S. Chen, DFG, Grant No. 1070/1, International Research Training Group “Sustainable Resource Use in North China” to A.E. Melchinger and National Natural Science Foundation of China (No. 10972234) to Z. Fu.

Supplementary material

122_2013_2132_MOESM1_ESM.doc (213 kb)
Supplementary material 1 (DOC 213 kb)

References

  1. Appenzeller L, Doblin M, Barreiro R, Wang H, Niu X, Kollipara K, Carrigan L, Tomes D, Chapman M, Dhugga KS (2004) Cellulose synthesis in maize: isolation and expression analysis of the cellulose synthase (CesA) gene family. Cellulose 11:287–299CrossRefGoogle Scholar
  2. Bai Q (2005) Inheritance of stover quality traits and their determination by near-infrared reflectance spectroscopy (NIRS) in silage maize. Dissertation, China Agricultural UniversityGoogle Scholar
  3. Bohn M, Groh S, Khariallah MM, Hoisington DA, Utz HF, Melchinger AE (2001) Re-evaluation of the prospects of marker-assisted selection for improving insect resistance to Diatraea spp. in tropical maize by cross validation and independent validation. Theor Appl Genet 103:1059–1067CrossRefGoogle Scholar
  4. Broman KW, Sen S (2009) A Guide to QTL Mapping with R/qtl. SpringerGoogle Scholar
  5. Charcosset A, Gallais A (1996) Estimation of the contribution of quantitative trait loci (QTL) to the variance of a quantitative trait by means of genetic markers. Theor Appl Genet 93:1193–1201CrossRefGoogle Scholar
  6. Ching A, Rafalski JA, Luck S, Butruilie MG (2010) Genetic loci associated with mechanical stalk strength in maize. United States Patent Publication. No. US2010/0015623A1Google Scholar
  7. Colbert TR, Darrah LL, Zuber MS (1984) Effect of recurrent selection for stalk crushing strength of agronomic characteristics and soluble stalk solids in maize. Crop Sci 24:473–478CrossRefGoogle Scholar
  8. Doerge RW, Churchill GA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142:285–294PubMedGoogle Scholar
  9. Flint-Garcia SA, Jampatong C, Darrah LL, Mcmullen MD (2003) Quantitative trait locus analysis of stalk strength in four maize populations. Crop Sci 43:13–22CrossRefGoogle Scholar
  10. Gao M, Guo K, Yang Z, Li X (2003) Study on mechanical properties of corn stalk. Trans CSAM 34:47–49Google Scholar
  11. Gere JM, Timoshenko SP (1984) Mechanics of Materials. Van Nostrand Reinhold Company Ltd, New YorkGoogle Scholar
  12. Gou L, Huang J, Zhang B, Li T, Sun Y, Zhao M (2007) Effect of population density on stalk lodging resistant mechanism and agronomic characteristics of maize. Acta Agronomica Sinia 33:1688–1695Google Scholar
  13. Haley CS, Knott SA (1992) A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity 69:315–324PubMedCrossRefGoogle Scholar
  14. Hallauer AR, Carena MJ, Miranda Filho JB (2010) Quantitative Genetics in Maize Breeding. Springer, New YorkGoogle Scholar
  15. Hansey CN, Leon N (2011) Biomass yield and cell wall composition of corn with alternative morphologies planted at variable densities. Crop Sci 51:1005–1015CrossRefGoogle Scholar
  16. Hu H, Meng Y, Wang H, Liu H, Chen S (2012) Identifying quantitative trait loci and determining closely related stalk traits for rind penetrometer resistance in a high-oil maize population. Theor Appl Genet 124:1439–1447PubMedCrossRefGoogle Scholar
  17. Jannink J, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177PubMedCrossRefGoogle Scholar
  18. Jansen RC, Stam P (1994) High resolution of quantitative traits into multiple loci via interval mapping. Genetics 136:1447–1455PubMedGoogle Scholar
  19. Jia Z, Bai Y (1992) Study on identification of lodging in maize inbred line. China seeds 3:30–32Google Scholar
  20. Kärkkäinen H, Sillanpää M (2012) Back to basics for Bayesian model building in genomic selection. Genetics 191:969–987PubMedCrossRefGoogle Scholar
  21. Kokubo A, Kuraishi S, Sakurai N (1989) Culm strength of barley correlation among maximum bending stress, cell wall dimensions, and cellulose content. Plant Physiol 91:876–882PubMedCrossRefGoogle Scholar
  22. Kokubo A, Sakurai N, Kuraishi S, Takeda K (1991) Culm brittleness of barley (Hordeum vulgare L.) mutants is caused by smaller number of cellulose molecules in cell wall. Plant Physiol 97:509–514PubMedCrossRefGoogle Scholar
  23. Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756PubMedGoogle Scholar
  24. Lauer J (1995) High-Oil Corn: advantages and risks. Field Crops 28.31–3Google Scholar
  25. Li Y, Qian Q, Zhou Y, Yan M, Sun L, Zhang M, Fu Z, Wang Y, Han B, Pang X, Chen M, Li J (2003) BRITTLE CULM1, which encodes a COBRA-like protein, affects the mechanical properties of rice plants. Plant Cell 15:2020–2031PubMedCrossRefGoogle Scholar
  26. Lorenz AJ, Chao S, Asoro FG, Heffner EL, Hayashi T, Iwata H, Smith HI, Sorrells ME, Jannink JL (2011) Genomic selection in plant breeding: knowledge and prospects. Adv Agron 110:77–123CrossRefGoogle Scholar
  27. Ma Q (2009) The expression of caffeic acid 3-O-methyltransferase in two wheat genotypes differing in lodging resistance. J Exp Botany 60:2763–2771CrossRefGoogle Scholar
  28. Melchinger AE, Utz HF, Piepho HP, Zeng Z, Schön CC (2007) The role of epistasis in the manifestation of heterosis: a system-oriented approach. Genetics 177:1815–1825PubMedCrossRefGoogle Scholar
  29. Meuwissen TH, Hayes BJ, Goddard M (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829PubMedGoogle Scholar
  30. Mode CJ, Robinson HF (1959) Pleiotropism and the genetic variance and covariance. Biometrics 15:518–537CrossRefGoogle Scholar
  31. Ordas B, Malvar R, Santiago R, Butron A (2010) QTL mapping for Mediterranean corn borer resistance in European flint germplasm using recombinant inbred lines. BMC Genomics 11:174–183PubMedCrossRefGoogle Scholar
  32. Papst C, Bohn M, Utz HF, Melchinger AE, Klein D, Eder J (2004) QTL mapping for European corn borer resistance (Ostrinia nubilalis Hb.), agronomic and forage quality traits of testcross progenies in early-maturing European maize (Zea mays L.) germplasm. Theor Appl Genet 108:1545–1554PubMedCrossRefGoogle Scholar
  33. Piepho H-P, Mohring J, Schulz-Streek T, Ogutu JO (2012) A stage-wise approach of the analysis of multi-environment trials. Biometrical J 00:1–17Google Scholar
  34. SAS Institute Inc (2008) SAS user’s guide, version9.2. SAS institute Cary, NCGoogle Scholar
  35. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464CrossRefGoogle Scholar
  36. Shenk JS, Westerhaus MO (1991) Population structuring of near infrared spectra and modified partial least square regression. Crop Sci 31:1548–1555CrossRefGoogle Scholar
  37. Sibale EM, Darrah LL, Zuber MS (1992) Comparison of two rind penetrometers for measurement of stalk strength in maize. Maydica 37:111–114Google Scholar
  38. Stojsin R, Ivanovic M, Kojic L, Stojsin D (1991) Inheritance of grain yield and several stalk characteristics significant in resistance to stalk lodging maize (Zea mays L.). Maydica 36:75–81Google Scholar
  39. Sun X (1987) Studies on the resistance of the culms of rice to lodging. Scientia Agricultura Scinica 20:32–37Google Scholar
  40. R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/
  41. Technow F, Melchinger AE (2013) Genomic prediction of dichotomous traits with Bayesian logistic models. Theor Appl Genet 126:1133–1143PubMedCrossRefGoogle Scholar
  42. Technow F, Bürger A, Melchinger AE (2013) Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups. G3 3:197–203Google Scholar
  43. Thompson DL (1963) Stalk strength of corn as measured by crushing strength and rind thickness. Crop Sci 3:323–329CrossRefGoogle Scholar
  44. Utz HF (2010) PLABSTAT: a computer program for statistical analysis of plant breeding experiments. Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, GermanyGoogle Scholar
  45. Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross validation and validation with independent samples. Genetics 154:1839–1849PubMedGoogle Scholar
  46. Wang H (2009) QTL analysis and genetic relationship between kernel compositions and stalk nutrition quality traits in high oil maize population. Dissertation, China Agricultural UniversityGoogle Scholar
  47. Wang J, Li H, Zhang L, Li C, Meng L (2010) Users’ Manual of QTL IciMapping V3.0, BeijingGoogle Scholar
  48. Würschum T, Liu W, Gowda M, Maurer HP, Fischer S, Schechert, Reif JC (2012) Comparison of biometrical models for joint linkage association mapping. Heredity 108:332–340Google Scholar
  49. Xu S (2008) Quantitative trait locus mapping can benefit from segregation distortion. Genetics 180:2201–2208PubMedCrossRefGoogle Scholar
  50. Yuan Z, Feng B, Zhao A, Liang A (2002) Dynamic analysis and comprehensive evaluation of crop-stem lodging resistance. Trans CSAE 18(6):30–31Google Scholar
  51. Zeng Z (1994) Precision mapping of quantitative trait loci. Genetics 136:1457–1468PubMedGoogle Scholar
  52. Zhang L, Wang S, Li H, Deng Q, Zheng A, Li S, Li P, Li Z, Wang J (2010) Effects of missing marker and segregation distortion on QTL mapping in F2 populations. Theor Appl Genet 121:1071–1082PubMedCrossRefGoogle Scholar
  53. Zuber MS, Grogan CO (1961) A new technique for measuring stalk strength in corn. Crop Sci 1:378–380CrossRefGoogle Scholar
  54. Zuber MS, Colbert TR, Bauman LF (1977) Effect of brown-midrib-3 mutant in maize (Zea mays) on stalk strength. Z Pflanzenzuecht 79:310–314Google Scholar
  55. Zuber MS, Colbert TR, Darrah LL (1980) Effect of recurrent selection for crushing strength on several stalk components in maize. Crop Sci 20:711–717CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Haixiao Hu
    • 1
    • 4
  • Wenxin Liu
    • 1
    • 2
  • Zhiyi Fu
    • 3
  • Linda Homann
    • 4
  • Frank Technow
    • 4
  • Hongwu Wang
    • 5
  • Chengliang Song
    • 3
  • Shitu Li
    • 3
  • Albrecht E. Melchinger
    • 4
    Email author
  • Shaojiang Chen
    • 1
    • 2
    Email author
  1. 1.National Maize Improvement Center of ChinaChina Agricultural University (West Campus)BeijingChina
  2. 2.Beijing Key Laboratory of Crop Genetic Improvement, College of Agronomy and BiotechnologyChina Agricultural University (West Campus)BeijingChina
  3. 3.Applicational Mechanics Department, College of ScienceChina Agricultural University (East Campus)BeijingChina
  4. 4.Institute of Plant Breeding, Seed Science, and Population GeneticsUniversity of HohenheimStuttgartGermany
  5. 5.Institute of Crop ScienceChinese Academy of Agricultural SciencesBeijingChina

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