QTL mapping and genetic studies in the Attila x CDC Go spring wheat (Triticum aestivum L.) mapping population Open Access
- Other title
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Dean, Spaner (Department of Agricultural, Food, and Nutritional Science)
- Examining committee member and department
Rong-Cai, Yang (Department of Agricultural, Food, and Nutritional Science)
Stephen, Strelkov (Department of Agricultural, Food, and Nutritional Science)
Harpinder, Randhawa (Department of Agricultural, Food, and Nutritional Science)
Gavin, Humphreys (Department of Plant Science)
Michael, Gaenzle (Department of Agricultural, Food, and Nutritional Science)
Department of Agricultural, Food, and Nutritional Science
- Date accepted
- Graduation date
Doctor of Philosophy
- Degree level
Bread wheat (Triticum aestivum L.) is one of the most important staple crops in the world. Wheat breeders in Canada primarily aim to develop cultivars with favored agronomic traits such as short stature, early maturity, high yielding, preferred end-use quality such as high protein content, and at least moderate resistance to priority diseases, such as leaf rust, stem rust, yellow rusts, fusarium head blight and common bunt. Almost all the traits mentioned above are quantitatively inherited, and therefore controlled by many genes of small effect. We used a mapping population of 167 recombinant inbred lines (RILs) developed from a cross between two spring wheat cultivars, ‘Attila’ and ‘CDC Go’ in our study, and evaluated the RIL population for agronomic traits and grain protein content at organic (2008 to 2011) and conventionally managed environments (2008 to 2015), and resistances to diseases in the field from 2012 to 2014. Then we genotyped this population with the Wheat 90K single nucleotide polymorphic (SNP) array. Inclusive composite interval mapping was conducted using average phenotypic data and a subset of 1200 informative SNPs out of the Wheat 90K SNP array. In addition, we compared our results for agronomic traits and grain protein content with the previous study conducted by our group with DArT markers and with phenotypic data from 2008 to 2011. In the present study with phenotypic data from 2008 to 2011, five moderate- and eleven minor-effect QTLs were detected across all three organic environments, including 13 QTLs that were not previously detected. Up to five QTLs were detected for each trait, except grain protein content, which individually accounted for 5.5 to 18.8% of phenotypic variance. For each trait, the total phenotypic and genetic variance explained by all detected QTLs varied from 9.3 to 39.4 and from 24.6 to 96.8%, respectively, which was much greater than our previous study. The results indicated that compared with 579 DArT markers that were used in our previous studies, the high-density SNP markers were useful in identifying three-fold more number of QTLs. Although direct comparison of the QTL results between the three and seven environments was not simple, we think that the increase in the number of testing environments neither improved new QTL detection nor their effect. For the combined phenotypic data across seven environments, we found a total of 6 minor- and 8 moderate-effect QTLs which individually explained 6.1-18.4% of the phenotypic variance. For wheat disease resistance, in the combined phenotypic data across all the environments, we found a total of 10 QTLs associated with resistances to four diseases, which included three QTL for each of leaf rust, stripe rust, and tan spot; one QTLs for resistance to common bunt.
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
- Citation for previous publication
Zou, J., Semagn, K., Iqbal, M., N’Diaye, A., Chen, H., Asif, M., Navabi, A., Perez-Lara, E., Pozniak, C., Yang, R.C. and Randhawa, H., 2016. Mapping QTLs Controlling Agronomic Traits in the ‘Attila’×‘CDC Go’Spring Wheat Population under Organic Management using 90K SNP Array. Crop Science.
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