BioEnergy Research

, Volume 6, Issue 3, pp 903–916 | Cite as

Genetic and Morphometric Analysis of Cob Architecture and Biomass-Related Traits in the Intermated B73 × Mo17 Recombinant Inbred Lines of Maize

  • Constantin JansenEmail author
  • Natalia de Leon
  • Nick Lauter
  • Candice Hirsch
  • Leah Ruff
  • Thomas Lübberstedt


Expected future cellulosic ethanol production increases the demand for biomass in the US Corn Belt. With low nutritious value, low nitrogen content, and compact biomass, maize cobs can provide a significant amount of cellulosic materials. The value of maize cobs depends on cob architecture, chemical composition, and their relation to grain yield as primary trait. Eight traits including cob volume, fractional diameters, length, weight, tissue density, and grain yield have been analyzed in this quantitative trait locus (QTL) mapping experiment to evaluate their inheritance and inter-relations. One hundred eighty-four recombinant inbred lines of the intermated B73 × Mo17 (IBM) Syn 4 population were evaluated from an experiment carried out at three locations and analyzed using genotypic information of 1,339 public SNP markers. QTL detection was performed using (1) comparison-wise thresholds with reselection of cofactors (α = 0.001) and (2) empirical logarithm of odds score thresholds (P = 0.05). Several QTL with small genetic effects (R 2 = 2.9–13.4 %) were found, suggesting a complex quantitative inheritance of all traits. Increased cob tissue density was found to add value to the residual without a commensurate negative impact on grain yield and therefore enables for simultaneous selection for cob biomass and grain yield.


Cob biomass Maize Cob tissue density QTL IBM 

Supplementary material

12155_2013_9319_MOESM1_ESM.doc (14 kb)
ESM 1 (DOC 13.5 KB)
12155_2013_9319_MOESM2_ESM.docx (24 kb)
ESM 2 (DOCX 23.8 kb)


  1. 1.
    Halvorson AD, Johnson JMF (2009) Corn cob characteristics in irrigated central great plains studies. Agron J 101(2):390CrossRefGoogle Scholar
  2. 2.
    Jansen C, Lübberstedt T (2012) Turning maize cobs into a valuable feedstock. BioEnergy Res 5(1):20–31CrossRefGoogle Scholar
  3. 3.
    Zych D (2008) The viability of corn cobs as a bioenergy feedstock. Accessed 15 May 2012
  4. 4.
    Foley KM, Vander Hooven DIB (1981) Properties and industrial uses of corncobs. In: Pomeranz Y, Munck L (eds) Cereals—a renewable resource—theory and practice. The American Association of Cereal Chemists, St. PaulGoogle Scholar
  5. 5.
    Lenz LW (1948) Comparative histology of the female inflorescence of Zea mays L. Ann Mo Bot Gard 34(4):353–376CrossRefGoogle Scholar
  6. 6.
    Ross AJ (2002) Genetic analysis of ear length and correlated traits in maize. Dissertation, Iowa State UniversityGoogle Scholar
  7. 7.
    Beavis WD, Smith OS, Grant D, Fincher R (1994) Identification of quantitative trait loci using a small sample of topcrossed and F4 progeny from maize. Crop Sci 34:882–896CrossRefGoogle Scholar
  8. 8.
    Lorenzana RE, Lewis MF, Jung HJG, Bernardo R (2010) Quantitative trait loci and trait correlations for maize stover cell wall composition and glucose release for cellulosic ethanol. Crop Sci 50(2):541–555CrossRefGoogle Scholar
  9. 9.
    Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49(1):1–12CrossRefGoogle Scholar
  10. 10.
    Veldboom LR, Lee M, Woodman WL (1994) Molecular marker-facilitated studies in an elite maize population: I. linkage analysis and determination of QTL for morphological traits. Theor Appl Genet 88:7–16, Theoretische und angewandte GenetikCrossRefGoogle Scholar
  11. 11.
    Upadyayula N, da Silva HS Bohn MO, Rocheford TR (2006) Genetic and QTL analysis of maize tassel and ear inflorescence architecture. TAG Theor Appl Genet 112(4):592–606, Theoretische und angewandte GenetikCrossRefGoogle Scholar
  12. 12.
    Veldboom LR, Lee M (1996) Genetic mapping of quantitative trait loci in maize in stress and nonstress environments: I. grain yield and yield components. Crop Sci 36:1310–1319CrossRefGoogle Scholar
  13. 13.
    Zhang H, Zheng Z, Liu X, Li Z, He C, Liu D et al (2010) QTL mapping for ear length and ear diameter under different nitrogen regimes in maize. Afr J Agric Res 5(8):626–630Google Scholar
  14. 14.
    Li M, Guo X, Zhang M, Wang X, Zhang G, Tian Y et al (2010) Mapping QTLs for grain yield and yield components under high and low phosphorus treatments in maize (Zea mays L.). Plant Sci 178(5):454–462CrossRefGoogle Scholar
  15. 15.
    Wang Y, Yao J, Zhang Z, Zheng Y (2006) The comparative analysis based on maize integrated QTL map and meta-analysis of plant height QTLs. Chin Sci Bull 51(18):2219–2230CrossRefGoogle Scholar
  16. 16.
    Marsan PA, Gorni C, Chittò A, Redaelli R, van Vijk R, Stam P, Mottoet M (2001) Identification of QTLs for grain yield and grain-related traits of maize (Zea mays L.) using an AFLP map, different testers, and cofactor analysis. TAG Theor Appl Genet 102(2–3):230–243, Theoretische und angewandte GenetikCrossRefGoogle Scholar
  17. 17.
    Doerge RW, Churchill GA (1996) Permutation tests for multiple loci affecting a quantitative character. Genetics 142:285–294PubMedGoogle Scholar
  18. 18.
    Lee M, Sharopova N, Beavis WD, Grant D, Maria Katt M, Blair D, Hallauer A (2002) Expanding the genetic map of maize with the intermated B73 x Mo17 (IBM) population. Plant Mol Biol 48(5–6):453–461PubMedCrossRefGoogle Scholar
  19. 19.
    Nelson PT, Coles ND, Holland JB, Bubeck DM, Smith S, Goodman MM (2008) Molecular characterization of maize inbreds with expired U.S. plant variety protection. Crop Sci 48(5):1673–1685CrossRefGoogle Scholar
  20. 20.
    Schnable et al (2009) The B73 maize genome: complexity, diversity, and dynamics. Science 326(5956):1112–1115PubMedCrossRefGoogle Scholar
  21. 21.
    Abertondo VJ (2007) Phenotypic analysis of intermated B73 x Mo17 (IBM) populations, Master Thesis, Iowa State UniversityGoogle Scholar
  22. 22.
    Lorenz AJ, Coors JG, Hansey CN, Kaeppler SM, de Leon N (2010) Genetic analysis of cell wall traits relevant to cellulosic ethanol production in maize (L.). Crop Sci 50(3):842–852CrossRefGoogle Scholar
  23. 23.
    Holland JB (2006) Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci 46(2):642–654CrossRefGoogle Scholar
  24. 24.
    Arbuckle JL (2006) Amos 7.0 user’s guide. SPSS, ChicagoGoogle Scholar
  25. 25.
    Wright S (1934) The methods of path coefficients. Ann Math Stat 5(3):161–215CrossRefGoogle Scholar
  26. 26.
    Wright S (1921) Correlation and causation. J Agric Res 20(7):557–585Google Scholar
  27. 27.
    Wang S, Basten CJ, Zeng Z-B (2010) Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, RaleighGoogle Scholar
  28. 28.
    Lauter N, Moscou MJ, Habiger J, Moose SP (2008) Quantitative genetic dissection of shoot architecture traits in maize: towards a functional genomics approach. Plant Genome J 1(2):99–110CrossRefGoogle Scholar
  29. 29.
    Mangin B, Goffinet B, Rebaï A (1994) Constructing confidence intervals for QTL location. Genetics 138(4):1301–1308PubMedGoogle Scholar
  30. 30.
    Bohn M, Novais J, Fonseca R, Tuberosa R, Grift TE (2006) Genetic evaluation of root complexity in maize. Acta Agron Hung 54(3):291–303CrossRefGoogle Scholar
  31. 31.
    Kearsey MJ, Farquhar AGL (1998) Short Review QTL analysis in plants; where are we now ? Heredity 80:137–142PubMedCrossRefGoogle Scholar
  32. 32.
    Zuk O, Hechter E, Sunyaev SR, Lander ES (2012) The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A 109(4):1193–1198PubMedCrossRefGoogle Scholar
  33. 33.
    Visscher PM, Hill WG, Wray NR (2008) Heritability in the genomics era—concepts and misconceptions. Nat Rev Genet 9(4):255–266PubMedCrossRefGoogle Scholar
  34. 34.
    Melchinger AE, Utz HF, Schön CC (1998) Quantitative trait locus (QTL) mapping using different testers and independent population samples in maize reveals low power of QTL detection and large bias in estimates of QTL effects. Genetics 149(1):383–403PubMedGoogle Scholar
  35. 35.
    Openshaw S, Frascaroli E (1997) QTL detection and marker-assisted selection for complex traits in maize. 52nd Annual Corn and Sorghum Research Conference, pp 44–53Google Scholar
  36. 36.
    Xu S (2003) Theoretical basis of the Beavis effect. Genetics 165(4):2259–2268PubMedGoogle Scholar
  37. 37.
    Schön CC, Utz HF, Groh S, Truberg B, Openshaw S, Melchinger AE (2004) Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits. Genetics 167(1):485–498PubMedCrossRefGoogle Scholar
  38. 38.
    Wassom JJ, Wong JC, Martinez E, King JJ, DeBaene J, Hotchkiss JR, Mikkilineni V, Bohn MO, Rocheford TR (2008) QTL associated with maize kernel oil, protein, and starch concentrations; kernel mass; and grain yield in Illinois High Oil × B73 backcross-derived lines. Crop Sci 48(1):243–252CrossRefGoogle Scholar
  39. 39.
    Da Silva HSP (2009) Genetic, genomic, and breeding approaches to further explore kernel composition traits and grain yield in maize. Dissertation, University of Illinois, Urbana–ChampaignGoogle Scholar
  40. 40.
    Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ, Myles S, Holland JB, Flint-Garcia S, McMullen MD, Buckler ES, Rocheford TR (2011) Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet 7(11):e1002383PubMedCrossRefGoogle Scholar
  41. 41.
    Vollbrecht E, Schmidt R (2009) Development of the inflorescences. In: Bennetzen J, Hake S (eds) Handbook of maize: its biology. Springer, New YorkGoogle Scholar
  42. 42.
    Vollbrecht E, Springer PS, Goh L, Buckler ES, Martienssen R (2005) Architecture of floral branch systems in maize and related grasses. Nature 436(7054):1119–1126PubMedCrossRefGoogle Scholar
  43. 43.
    Gallavotti A, Long JA, Stanfield S, Yang X, Jackson D, Vollbrecht E, Schmidt RJ (2010) The control of axillary meristem fate in the maize ramosa pathway. Development 137:2849–2856PubMedCrossRefGoogle Scholar
  44. 44.
    Forestan C, Varotto S (2011) The role of PIN auxin efflux carriers in polar auxin transport and accumulation and their effect on shaping maize development. Molecular Plant 1–12 doi: 10.1093/mp/ssr103. Accessed 15 May 2012
  45. 45.
    Reese M (2009) Corn cobs for ethanol production process heating: a feasibility report of collection, storage and use of corn cobs as a renewable ethanol production process heating fuel. Accessed 15 May 2012
  46. 46.
    Sawyer J, Mallarino A, Hanway JJ (2007) Nutrient removal when harvesting corn stover. Iowa State University Research. Integrated Crop. Accessed 15 May 2012
  47. 47.
    Hoffman LD, USDA, Available from: Accessed: 5 June, 2012
  48. 48.
    Hallauer AR, Ross AJ, Lee M (2010) Long-term divergent selection for ear length in maize. Plant breeding reviews: long-term selection: crops, animals, and bacteria. Wiley, New YorkGoogle Scholar
  49. 49.
    Meuwissen TH, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157(4):1819–1829PubMedGoogle Scholar
  50. 50.
    Bernardo R, Yu J (2007) Prospects for genomewide selection for quantitative traits in maize. Crop Sci 47:1082–1090CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Constantin Jansen
    • 1
    Email author
  • Natalia de Leon
    • 2
  • Nick Lauter
    • 3
  • Candice Hirsch
    • 4
  • Leah Ruff
    • 5
  • Thomas Lübberstedt
    • 1
  1. 1.Department of AgronomyIowa State UniversityAmesUSA
  2. 2.Department of AgronomyUniversity of Wisconsin–MadisonMadisonUSA
  3. 3.USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State UniversityAmesUSA
  4. 4.Department of Plant BiologyMichigan State UniversityEast LansingUSA
  5. 5.Department of AgronomyNorth Carolina State UniversityRaleighUSA

Personalised recommendations