Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
- PMID: 28393303
- PMCID: PMC5487692
- DOI: 10.1007/s00122-017-2897-1
Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat
Abstract
Genomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is 'genomic selection' which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31-0.74 for LR seedling, 0.12-0.56 for LR APR, 0.31-0.65 for SR APR, 0.70-0.78 for YR seedling, and 0.34-0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.
Conflict of interest statement
The authors have no conflict of interest.
Figures


Similar articles
-
Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat.Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.08.0082. Plant Genome. 2017. PMID: 28724084
-
Genomic prediction for rust resistance in diverse wheat landraces.Theor Appl Genet. 2014 Aug;127(8):1795-803. doi: 10.1007/s00122-014-2341-8. Epub 2014 Jun 26. Theor Appl Genet. 2014. PMID: 24965887
-
Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes.Theor Appl Genet. 2018 Jul;131(7):1405-1422. doi: 10.1007/s00122-018-3086-6. Epub 2018 Mar 27. Theor Appl Genet. 2018. PMID: 29589041 Free PMC article.
-
Molecular breeding for rust resistance in wheat genotypes.Mol Biol Rep. 2021 Jan;48(1):731-742. doi: 10.1007/s11033-020-06015-z. Epub 2021 Jan 3. Mol Biol Rep. 2021. PMID: 33389532 Review.
-
Quantitative Trait Loci Conferring Leaf Rust Resistance in Hexaploid Wheat.Phytopathology. 2018 Dec;108(12):1344-1354. doi: 10.1094/PHYTO-06-18-0208-RVW. Epub 2018 Nov 2. Phytopathology. 2018. PMID: 30211634 Review.
Cited by
-
Genome-wide association study of common resistance to rust species in tetraploid wheat.Front Plant Sci. 2024 Jan 3;14:1290643. doi: 10.3389/fpls.2023.1290643. eCollection 2023. Front Plant Sci. 2024. PMID: 38235202 Free PMC article.
-
Wheat Omics: Advancements and Opportunities.Plants (Basel). 2023 Jan 17;12(3):426. doi: 10.3390/plants12030426. Plants (Basel). 2023. PMID: 36771512 Free PMC article. Review.
-
Capturing Wheat Phenotypes at the Genome Level.Front Plant Sci. 2022 Jul 4;13:851079. doi: 10.3389/fpls.2022.851079. eCollection 2022. Front Plant Sci. 2022. PMID: 35860541 Free PMC article. Review.
-
Optimizing predictions in IRRI's rice drought breeding program by leveraging 17 years of historical data and pedigree information.Front Plant Sci. 2022 Sep 20;13:983818. doi: 10.3389/fpls.2022.983818. eCollection 2022. Front Plant Sci. 2022. PMID: 36204059 Free PMC article.
-
Genomic prediction for rust resistance in pea.Front Plant Sci. 2024 Jul 23;15:1429802. doi: 10.3389/fpls.2024.1429802. eCollection 2024. Front Plant Sci. 2024. PMID: 39109067 Free PMC article.
References
-
- Bernardo R, Yu J. Prospects for genomewide selection for quantitative traits in maize. Crop Sci. 2007;47:1082–1090. doi: 10.2135/cropsci2006.11.0690. - DOI
-
- Box GEP, Cox DR. An Analysis of Transformations. J R Stat Soc Ser B. 1964;26:211–252.
-
- Burgueño J, de los Campos G, Weigel K, Crossa J. Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci. 2012;52:707–719. doi: 10.2135/cropsci2011.06.0299. - DOI
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources
Research Materials
Miscellaneous