Genomic Prediction of Northern Corn Leaf Blight Resistance in Maize with Combined or Separated Training Sets for Heterotic Groups
Open accessAbstract
Northern corn leaf blight (NCLB), a severe fungal disease causing yield losses worldwide, is most effectively controlled by resistant varieties. Genomic prediction could greatly aid resistance breeding efforts. However, the development of accurate prediction models requires large training sets of genotyped and phenotyped individuals. Maize hybrid breeding is based on distinct heterotic groups that maximize heterosis (the dent and flint groups in Central Europe). The resulting allocation of resources to parallel breeding programs challenges the establishment of sufficiently sized training sets within groups. Therefore, using training sets combining both heterotic groups might be a possibility of increasing training set sizes and thereby prediction accuracies. The objectives of our study were to assess the prospect of genomic prediction of NCLB resistance in maize and the benefit of a training set that combines two heterotic groups. Our data comprised 100 dent and 97 flint lines, phenotyped for NCLB resistance per se and genotyped with high-density single-nucleotide polymorphism marker data. A genomic BLUP model was used to predict genotypic values. Prediction accuracies reached a maximum of 0.706 (dent) and 0.690 (flint), and there was a strong positive response to increases in training set size. The use of combined training sets led to significantly greater prediction accuracies for both heterotic groups. Our results encourage the application of genomic prediction in NCLB-resistance breeding programs and the use of combined training sets.
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Technow and Melchinger showed that using a more realistic but complex WGP model with lower marker density can result in higher prediction accuracy than using a less realistic WGP model with higher marker density.
“Technow and Melchinger [12] showed that using a more realistic but complex WGP model with a lower marker density can result in a higher prediction accuracy than using a less realistic WGP model with a higher marker density.”
From Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation (2015) · cites this paper as Technow and Melchinger [12]
The Discussion (‘Merit of combined training sets’; page unavailable) reports that prediction worked at comparatively low marker density and that increasing marker density did not improve accuracy. It does not show that a more realistic, complex model at lower density outperformed a simpler model at higher density; its discussion of a more complex BayesB-type algorithm says it did not yield greater accuracy. (LLM, conf=0.82)
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References 40 · Cited by 147
Research works printed but not linked: 20
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- Authors
- Technow, F., Bürger, A., & Melchinger, A. E.
- Affiliations
- University of Hohenheim (DE)
- Corresponding author
- Technow, Frank
- Venue
- G3 Genes|Genomes|Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-02-01
- Volume
- 3
- Issue
- 2
- Pages
- 197-203
- ISSN
- 2160-1836 (ISSN-L)
- PMC
- PMC3564980
- OpenAlex
- W2062105733
- Cited by
- 147
- References
- 40
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 13 · 2023: 9 · 2022: 14 · 2021: 12 · 2020: 10 · 2019: 17 · 2018: 12 · 2017: 9 · 2016: 8 · 2015: 15 · 2014: 14 · 2013: 1
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- G3 Genes Genomes Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Plant Disease Resistance and Genetics
- Keywords
- northern corn leaf blight, disease resistance, maize, genomic prediction, heterotic groups, training data size, genomic BLUP, single nucleotide polymorphisms, hybrid breeding, prediction accuracy
- MeSH terms
- Bayes Theorem, Zea mays, Genotype, Hybrid Vigor, Phenotype, Plant Diseases, Linkage Disequilibrium, Genome, Plant, Polymorphism, Single Nucleotide, Disease Resistance
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