Abstract
Gibberella ear rot (GER) is a serious threat to maize cultivation, causing grain yield losses and contamination with mycotoxins. Genomic prediction (GP) has great potential to accelerate resistance breeding against GER. However, small training sets (TS) consisting of both phenotyped and genotyped individuals are a challenge for obtaining high prediction accuracy (ρ) in GP. A potential solution would be combining small-size populations across heterotic pools. However, genetic heterogeneity between populations in terms of segregating QTL, linkage disequilibrium (LD) pattern and genomic relationships can impair ρ of GP. In this study, we investigated the genetic architecture of GER severity, deoxynivalenol concentration (DON) and days to silking with genome-wide association studies within two elite panels of 130 dent and 114 flint lines from the maize breeding program of the University of Hohenheim tested in four environments. We also assessed the consistency of LD pattern and genomic relationships between the two heterotic pools. Furthermore, we compared four GP approaches differing in the composition of the TS with lines from a single or combined pool(s) and statistical models with marker effects identical or different but correlated between pools. We detected two and six QTL for DON within the dent and flint pool, respectively, but no common QTL. The LD pattern was consistent between pools for marker pairs ≤ 10 kb apart. GP across pools yielded low or even negative ρ. Combined-pool GP had no higher ρ than within-pool GP, regardless of the statistical model. Our findings underline the importance of investigating the genetic heterogeneity between populations prior to implementing GP using combined TS.
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Abbreviations
- AP:
-
Across-pool prediction
- CD:
-
Combined-pool prediction, assuming marker effects different but correlated across pools
- CI:
-
Combined-pool prediction, assuming identical marker effects across pools
- DON:
-
Deoxynivalenol
- DS:
-
Days to silking
- GBLUP:
-
Genomic best linear unbiased prediction
- GEBV:
-
Genomic estimated breeding values
- GER:
-
Gibberella ear rot
- GP:
-
Genomic prediction
- GWAS:
-
Genome-wide association studies
- LD:
-
Linkage disequilibrium
- TS:
-
Training set
- VS:
-
Validation set
- WP:
-
Within-pool prediction
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Acknowledgements
This research was supported by Deutsche Forschungsgemeinschaft (DFG) Grant No. ME 2260/6-1. Part of the SNP genotyping was funded by the German Ministry of Education and Research within the AgroClustEr “Synbreed – Synergistic plant and animal breeding” (FKZ: 0315528d). We are grateful to Vilson Mirdita and the staff of the Agricultural Research Station at Eckartsweier and Hohenheim for conducting the field trials for this study. We acknowledge Christian Riedelsheimer’s documentation on using the GenABEL R package and Haixiao Hu’s suggestions for performing genome-wide association studies. We appreciate advices from Pedro Correa Brauner and Willem Molenaar for improving the text of the manuscript.
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Han, S., Miedaner, T., Utz, H.F. et al. Genomic prediction and GWAS of Gibberella ear rot resistance traits in dent and flint lines of a public maize breeding program. Euphytica 214, 6 (2018). https://doi.org/10.1007/s10681-017-2090-2
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DOI: https://doi.org/10.1007/s10681-017-2090-2