Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years
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The calibration data for genomic prediction should represent the full genetic spectrum of a breeding program. Data heterogeneity is minimized by connecting data sources through highly related test units.
One of the major challenges of genome-enabled prediction in plant breeding lies in the optimum design of the population employed in model training. With highly interconnected breeding cycles staggered in time the choice of data for model training is not straightforward. We used cross-validation and independent validation to assess the performance of genome-based prediction within and across genetic groups, testers, locations, and years. The study comprised data for 1,073 and 857 doubled haploid lines evaluated as testcrosses in 2 years. Testcrosses were phenotyped for grain dry matter yield and content and genotyped with 56,110 single nucleotide polymorphism markers. Predictive abilities strongly depended on the relatedness of the doubled haploid lines from the estimation set with those on which prediction accuracy was assessed. For scenarios with strong population heterogeneity it was advantageous to perform predictions within a priori defined genetic groups until higher connectivity through related test units was achieved. Differences between group means had a strong effect on predictive abilities obtained with both cross-validation and independent validation. Predictive abilities across subsequent cycles of selection and years were only slightly reduced compared to predictive abilities obtained with cross-validation within the same year. We conclude that the optimum data set for model training in genome-enabled prediction should represent the full genetic and environmental spectrum of the respective breeding program. Data heterogeneity can be reduced by experimental designs that maximize the connectivity between data sources by common or highly related test units.
KeywordsPredictive Ability Breeding Population Genetic Group Single Nucleotide Polymorphism Marker Double Haploid Line
We thank Daniel Gianola, Sofia daSilva, and Torben Schulz-Streeck for helpful comments on the manuscript. We also thank Ruedi Fries and Hubert Pausch for processing of SNP arrays. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr “Synbreed—Synergistic plant and animal breeding” (FKZ: 0315528A).
Conflict of interest
The authors declare that they have no conflict of interest.
The authors declare that the experiments comply with the current laws of Germany.
- Crossa J, de los Campos G, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banzinger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724PubMedCentralPubMedCrossRefGoogle Scholar
- Ganal MW, Durstewitz G, Polley A, Bérard A, Buckler ES, Charcosset A, Clarke JD, Graner EM, Hansen M, Joets J, Le Paslier MC, McMullen MD, Montalent P, Rose M, Schön CC, 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:e28334PubMedCentralPubMedCrossRefGoogle Scholar
- Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2009) ASReml user guide release 3.0. VSN International Ltd. Hemel HempsteadGoogle Scholar
- Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G (2014) The impact of population structure on genomic prediction in stratified populations. Theor Appl Genet 127:749–762Google Scholar
- Holland JB, Nyquist WE, Cervantes-Martínez CT (2003) Estimating and interpreting heritability for plant breeding: an update. Plant Breed Rev 22:9–112Google Scholar
- Piepho HP, Williams ER, Fleck M (2006) A note on the analysis of designed experiments with complex treatment structure. HortScience 41:446–452Google Scholar
- Rincent R, Laloë D, Nicolas S, Altmann T, Brunel D, Revilla P, Rodríguez VM, Moreno-Gonzalez J, Melchinger AE, Bauer E, Schön CC, 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 192:715–728PubMedCentralPubMedCrossRefGoogle Scholar
- Saatchi M, McClure MC, McKay SD, Rolf MM, Kim JW, 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 43:40PubMedCentralPubMedCrossRefGoogle Scholar
- Utz HF, Melchinger AE, Schön CC (2000) Bias and sampling error of the estimated proportion of genotypic variance explained by quantitative trait loci determined from experimental data in maize using cross-validation and validation with independent samples. Genetics 154:1839–1849PubMedCentralPubMedGoogle Scholar
- Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrels ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE (2012) Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments. G3 2:1427–1436PubMedCentralPubMedCrossRefGoogle Scholar