, Volume 143, Issue 6, pp 671–680 | Cite as

Additive-dominance genetic model analyses for late-maturity alpha-amylase activity in a bread wheat factorial crossing population

  • Golam Rasul
  • Karl D. Glover
  • Padmanaban G. Krishnan
  • Jixiang Wu
  • William A. Berzonsky
  • Amir M. H. Ibrahim


Elevated level of late maturity α-amylase activity (LMAA) can result in low falling number scores, reduced grain quality, and downgrade of wheat (Triticum aestivum L.) class. A mating population was developed by crossing parents with different levels of LMAA. The F2 and F3 hybrids and their parents were evaluated for LMAA, and data were analyzed using the R software package ‘qgtools’ integrated with an additive-dominance genetic model and a mixed linear model approach. Simulated results showed high testing powers for additive and additive × environment variances, and comparatively low powers for dominance and dominance × environment variances. All variance components and their proportions to the phenotypic variance for the parents and hybrids were significant except for the dominance × environment variance. The estimated narrow-sense heritability and broad-sense heritability for LMAA were 14 and 54 %, respectively. High significant negative additive effects for parents suggest that spring wheat cultivars ‘Lancer’ and ‘Chester’ can serve as good general combiners, and that ‘Kinsman’ and ‘Seri-82’ had negative specific combining ability in some hybrids despite of their own significant positive additive effects, suggesting they can be used as parents to reduce LMAA levels. Seri-82 showed very good general combining ability effect when used as a male parent, indicating the importance of reciprocal effects. High significant negative dominance effects and high-parent heterosis for hybrids demonstrated that the specific hybrid combinations; Chester × Kinsman, ‘Lerma52’ × Lancer, Lerma52 × ‘LoSprout’ and ‘Janz’ × Seri-82 could be generated to produce cultivars with significantly reduced LMAA level.


Late-maturity alpha-amylase Bread wheat Additive-dominance Heterosis General combining ability Specific combining ability 



This research was a contribution of the Department of Plant Science, South Dakota State University. The first author was supported by Monsanto through the Monsanto Fellowships in Plant Breeding program. This study was also partially supported by USDA-NIFA Hatch projects 1005459 and SD00H492-13. The authors would like to show their gratitude to Jonathan Kleinjan, the Spring Wheat Breeding crews, and fellow graduate students for their assistance with field trials and screening procedures.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Cockerham CC (1980) Random and fixed effects in plant genetics. Theor Appl Genet 56:119–131CrossRefPubMedGoogle Scholar
  2. Gale MD, Ainsworth CC (1984) The relationship between a-amylase species found in developing and germinating wheat grain. Biochem Genet 22:1031–1036CrossRefPubMedGoogle Scholar
  3. Jenkins JN, Wu J, McCarty JC, Saha S, Gutierrez O, Hayes R, Stelly DM (2006) Genetic evaluation for thirteen chromosome substitution lines crossed with five commercial cultivars: I. Yield traits. Crop Sci 46:1169–1178CrossRefGoogle Scholar
  4. Jenkins JN, McCarty JC, Wu J, Saha S, Guitierrez O, Hayes R, Stelly DM (2007) Genetic effects of thirteen Gossypium barbadense L. chromosome substitution lines in topcrosses with upland cotton cultivars: II. Fiber quality traits. Crop Sci 47:561–570CrossRefGoogle Scholar
  5. Jenkins JN, McCarty JC, Wu J, Gutierrez OA (2009) Genetic variance components and genetic effects among eleven diverse upland cotton lines and their F2 hybrids. Euphytica 167:397–400CrossRefGoogle Scholar
  6. Mares DJ, Gale MD (1990) Control of a-amylase synthesis in wheat grains. In: Ringlund K, Mosleth E, Mares DJ (eds) Fifth international symposium on pre-harvest sprouting in cereals. Westview Press, Boulder, pp 183–194Google Scholar
  7. Mares D, Mrva K (2008) Late-maturity a-amylase: low falling number in wheat in the absence of preharvest sprouting. J Cereal Sci 47:6–17CrossRefGoogle Scholar
  8. McCaig TN, DePauw RM (1983) Falling numbers and alpha-amylase in sawfly-resistant wheats. In: Kruger JE, LaBerge DE (eds) Third international symposium on pre-harvest sprouting in cereals. Westview Press Inc, Boulder, pp 79–85Google Scholar
  9. McCarty JC, Wu J, Jenkins JN (2007) Use of primitive derived cotton accessions for agronomic and fiber traits improvement: variance components and genetic effects. Crop Sci 47:100–110CrossRefGoogle Scholar
  10. McCleary BV, McNally M, Monaghan D, Mugford DC (2002) Measurement of α-amylase activity in white wheat flour, milled malt, and microbial enzyme preparations using the Ceralpha Assay: collaborative study. J AOAC Int 85:1096–1102PubMedGoogle Scholar
  11. Mrva K, Wallwork M, Mares DJ (2006) Alpha-Amylase and programmed cell death in aleurone of ripening wheat grains. J Exp Bot 57(4):877–885CrossRefPubMedGoogle Scholar
  12. Nakatsu S, Miyamoto H, Amano Y (1996) Variation for α-amylase activity and dormancy in Hokkaido wheat varieties. In: Noda K, Mares DJ (eds) Seventh international symposium on pre-harvest sprouting in cereals. Centre for Academic Societies, Osaka, pp 411–418Google Scholar
  13. Rao CR (1971) Estimation of variance and covariance components MINQUE theory. J Multivar Anal 1:257–275CrossRefGoogle Scholar
  14. Rasul G (2008) Characterizing germplasm and mapping QTLs for pre-harvest sprouting resistance in spring wheat (Triticum aestivum L.). Canadian Thesis, University of Manitoba (Canada), Library and Archives Canada = Bibliothèque et Archives Canada, ISBN: 0494414553, 9780494414552Google Scholar
  15. Rasul G, Humphreys DG, Brule-Babel A, McCartney C, Knox RE, DePauw RM, Somers DJ (2009) Mapping QTLs for pre-harvest sprouting traits in the spring wheat cross “RL4452/AC Domain”. Euphytica 168:363–378CrossRefGoogle Scholar
  16. Rasul G, Humphreys GD, Wu J, Brûlé-Babel A, Fofana B, Glover KD (2012) Evaluation of preharvest sprouting traits in a collection of spring wheat germplasm using genotype and genotype × environment interaction model. Plant Breed 131:244–251CrossRefGoogle Scholar
  17. Tang B, Jenkins JN, Watson CE, McCarty JC, Creech RG (1996) Evaluation of Genetic variances, heritabilities, and correlations for yield and fiber traits among cotton F2 hybrid populations. Euphytica 91:315–322CrossRefGoogle Scholar
  18. Wu J, Zhu J, Xu F, Ji D (1995) Analysis of genetic effect × environment interactions for yield traits in upland cotton (in Chinese). Heredita 17:1–4Google Scholar
  19. Wu J, Jenkins JN, McCarty JC (2008) Testing variance components by two jackknife techniques. In: Gadbury GL (ed) Proceedings of applied statistics in agriculture. New Prairie Press, Kansas State University, Manhattan, pp 1–17Google Scholar
  20. Wu J, McCarty JC, Saha S, Jenkins JN, Hayes R (2009) Genetic changes in plant growth and their associations with chromosomes from Gossypium barbadence L. in G. hirsutum L. Genetica 137:57–66CrossRefPubMedGoogle Scholar
  21. Wu J, McCarty JC, Jenkins JN, Meredith WR (2010) Breeding potential of introgressions into upland cotton: genetic effect and heterosis. Plant Breed 129:526–532Google Scholar
  22. Wu J, Jenkins JN, McCarty JC (2014) Package ‘qgtools’, Tools for quantitative genetics data analyses, Version 1.0. Plant Science Department, South Dakota State University, Brookings, SD 57007, USAGoogle Scholar
  23. Zhu J (1989) Estimation of genetic variance components in the general mixed model. PhD Thesis, Diss Abstr DA8924291. North Carolina State University, RaleighGoogle Scholar
  24. Zhu J (1993) Methods of predicting genotype value and heterosis for offspring of hybrids (in Chinese). J Biomath 8:32Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Golam Rasul
    • 1
  • Karl D. Glover
    • 1
  • Padmanaban G. Krishnan
    • 2
  • Jixiang Wu
    • 1
  • William A. Berzonsky
    • 3
  • Amir M. H. Ibrahim
    • 4
  1. 1.Department of Plant ScienceSouth Dakota State UniversityBrookingsUSA
  2. 2.Department of Health and Nutritional SciencesSouth Dakota State UniversityBrookingsUSA
  3. 3.Bayer CropScience LPLincolnUSA
  4. 4.Department of Soil and Crop SciencesTexas A&M UniversityCollege StationUSA

Personalised recommendations