Across-years prediction of hybrid performance in maize using genomics
Inclusion of historical training data improved the genomics-based prediction of performance of maize hybrids, the extent depending on the phenotypic trait and genotype-by-year interaction.
Prediction of hybrid performance using existing phenotypic data on previous hybrids combined with molecular data collected on the parent lines allows to identify the most promising candidates from a huge number of possible hybrids at an early stage. Phenotypic data on yield and dry matter of 1970 grain maize hybrids from 19 years of a public breeding program were aggregated considering the underlying structure of factorial sets of hybrids. Pedigree records and 50 K SNP data were collected on their 170 Dent and 127 Flint parent lines. The performance of untested hybrids was predicted by best linear unbiased predictors (BLUP) on basis of pedigree or genomic data. For composition of training sets (TRN) and test sets (TST), three schemes for collecting factorials from specific years were employed which resulted in 490 scenarios. For each scenario, the predictive ability and genomic relationship between TRN and TST hybrids were determined. For extended TRNs, where earlier years were successively added to the TRN, the maximum relationship increased and the predictive ability improved, with the extent of the latter depending on the phenotypic trait and its genotype-by-year interaction. Genomic BLUP outperformed pedigree BLUP and better utilized the early years’ data, especially for prediction of hybrids from factorials in a more distant future. This study on hybrid prediction in grain maize illustrated that including historical phenotypic data for training, although consisting of less related genotypes, can improve genomic prediction and enables optimization of hybrid variety development.
We thank the staff of the Agricultural Experimental Research station, University of Hohenheim, for excellent technical assistance in conducting the field experiments, H. P. Piepho and H. F. Utz for their advice on the statistical analyses as well as W. Molenaar and two anonymous reviewers for valuable suggestions to improve the content of the manuscript. We are indebted to the group of R. Fries from Technische Universität München for the SNP genotyping of the parent inbred lines. This project was funded by the German Federal Ministry of Education and Research (BMBF) within the projects OPTIMAL (FKZ: 0315958B, 0315958F), SYNBREED (FKZ: 0315528D) and by the German Research Foundation (DFG, Grant No. ME 2260/5-1).
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Conflict of interest
The authors declare that they have no conflict of interest.
- Auinger HJ, Schönleben M, Lehermeier C, Schmidt M, Korzun V, Geiger HH, Piepho H-P, Gordillo A, Wilde P, Bauer E, Schön C-C (2016) Model training across multiple breeding cycles significantly improves genomic prediction accuracy in rye (Secale cereale L.). Theor Appl Genet 129:2043–2053. https://doi.org/10.1007/s00122-016-2756-5 CrossRefGoogle Scholar
- Bernardo R (1994) Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci 34:20–25. https://doi.org/10.2135/cropsci1994.0011183X003400010003x CrossRefGoogle Scholar
- Bernardo R (1996) Best linear unbiased prediction of maize single-cross performance. Crop Sci 36:50–56. https://doi.org/10.2135/cropsci1996.0011183X003600010009x CrossRefGoogle Scholar
- Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) Mixed models for S language environments: ASReml-R reference manual. Training Series QE02001. Queensland Department of Primary Industries and Fisheries, NSW Department of Primary Industries, BrisbaneGoogle Scholar
- Clark SA, Hickey JM, Daetwyler HD, Van der Werf JHJ (2012) The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes. Genet Sel Evol 44:4. https://doi.org/10.1186/1297-9686-44-4 CrossRefGoogle Scholar
- Duvick DN (1999) Heterosis: feeding people and protecting natural resources. In: Coors JG, Pandey S (eds) The genetics and exploitation of heterosis in crops. ASA-CSSA, Madison, pp 19–29Google Scholar
- Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner E-M, Hansen M, Joets J, Le Paslier M-C, McMullen MD, Montalent P, Rose M, Schön C-C, Sun Q, Walter H, Martin OC, Falque M (2011) A large maize (Zea mays L.) SNP genotyping array: development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PLoS ONE 6:e28334. https://doi.org/10.1371/journal.pone.0028334 CrossRefGoogle Scholar
- Henderson CR (1984) Applications of linear models in animal breeding. University of Guelph, GuelphGoogle Scholar
- Hickey JM, Dreisigacker S, Crossa J, Hearne S, Babu R, Prasanna BM, Grondona M, Zambelli A, Windhausen VS, Mathews K, Gorjanc G (2014) Evaluation of genomic selection training population designs and genotyping strategies in plant breeding programs using simulation. Crop Sci 54:1476–1488. https://doi.org/10.2135/cropsci2013.03.0195 CrossRefGoogle Scholar
- Lehermeier C, Krämer N, Bauer E, Bauland C, Camisan C, Campo L, Flament P, Melchinger AE, Menz M, Meyer N, Moreau L, Moreno-González J, Ouzunova M, Pausch H, Ranc N, Schipprack W, Schönleben M, Walter H, Charcosset A, Schön C-C (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198:3–16. https://doi.org/10.1534/genetics.114.161943 CrossRefGoogle Scholar
- Ly D, Chenu K, Gauffreteau A, Rincent R, Huet S, Gouache D, Martre P, Bordes J, Charmet G (2017) Nitrogen nutrition index predicted by a crop model improves the genomic prediction of grain number for a bread wheat core collection. Field Crops Res 214:331–340. https://doi.org/10.1016/j.fcr.2017.09.024 CrossRefGoogle Scholar
- Pérez-Rodríguez P, Crossa J, Rutkoski J, Poland J, Singh R, Legarra A, Autrique E, de los Campos G, Burgueño J, Dreisigacker S (2017) Single-step genomic and pedigree genotype × environment interaction models for predicting wheat lines in international environments. Plant Genome 10:2. https://doi.org/10.3835/plantgenome2016.09.0089 CrossRefGoogle Scholar
- R Core Team (2017) R: a language and environment for statistical computing. https://www.r-project.org
- Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodríguez VM, Moreno-González J, Melchinger AE, Bauer E, Schön C-C, Meyer N, Giauffret C, Bauland C, Jamin P, Laborde J, Monod H, Flament P, Charcosset A, Moreau L (2012) Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192:715–728. https://doi.org/10.1534/genetics.112.141473 CrossRefGoogle Scholar
- Saatchi M, McClure MC, McKay SD, Rolf MM, Kim J, Decker JE, Taxis TM, Chapple RH, Ramey HR, Northcutt SL, Bauck S, Woodward B, Dekkers JCM, Fernando RL, Schnabel RD, Garrick DJ, Taylor JF (2011) Accuracies of genomic breeding values in American Angus beef cattle using K-means clustering for cross-validation. Genet Sel Evol 43:40CrossRefGoogle Scholar
- Schopp P, Riedelsheimer C, Utz HF, Schön C-C, Melchinger AE (2015) Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection. Theor Appl Genet 128:2189–2201. https://doi.org/10.1007/s00122-015-2577-y CrossRefGoogle Scholar
- Schrag TA, Westhues M, Schipprack W, Seifert F, Thiemann A, Scholten S, Melchinger AE (2018) Beyond genomic prediction: combining different types of omics data can improve prediction of hybrid performance in maize. Genetics 208:1373–1385. https://doi.org/10.1534/genetics.117.300374 CrossRefGoogle Scholar
- Wedzony M, Forster B, Zur I, Golemiec E, Szechynska-Hebda M, Dubas E, Gotebiowska G (2009) Progress in doubled haploid technology in higher plants. In: Touraev A, Forster BP, Jain SM (eds) Advances in haploid production in higher plants. Springer, Dordrecht, pp 1–33Google Scholar
- Westhues M, Schrag TA, Heuer C, Thaller G, Utz HF, Schipprack W, Thiemann A, Seifert F, Ehret A, Schlereth A, Stitt M, Nikoloski Z, Willmitzer L, Schön C-C, Scholten S, Melchinger AE (2017) Omics-based hybrid prediction in maize. Theor Appl Genet 130:1927–1939. https://doi.org/10.1007/s00122-017-2934-0 CrossRefGoogle Scholar