Additive-dominance genetic model analyses for late-maturity alpha-amylase activity in a bread wheat factorial crossing population
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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.
KeywordsLate-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.
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Conflict of interest
The authors declare that they have no conflict of interest.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zhu J (1989) Estimation of genetic variance components in the general mixed model. PhD Thesis, Diss Abstr DA8924291. North Carolina State University, RaleighGoogle Scholar
- Zhu J (1993) Methods of predicting genotype value and heterosis for offspring of hybrids (in Chinese). J Biomath 8:32Google Scholar