Theoretical and Applied Genetics

, Volume 128, Issue 11, pp 2189–2201 | Cite as

Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection

  • Pascal Schopp
  • Christian Riedelsheimer
  • H. Friedrich Utz
  • Chris-Carolin Schön
  • Albrecht E. Melchinger
Original Article


Key message

Deterministic formulas accurately forecast the decline in predictive ability of genomic prediction with changing testers, target environments or traits and truncation selection.


Genomic prediction of testcross performance (TP) was found to be a promising selection tool in hybrid breeding as long as the same tester and environments are used in the training and prediction set. In practice, however, selection targets often change in terms of testers, target environments or traits leading to a reduced predictive ability. Hence, it would be desirable to estimate for given training data the expected decline in the predictive ability of genomic prediction under such settings by deterministic formulas that require only quantitative genetic parameters available from the breeding program. Here, we derived formulas for forecasting the predictive ability under different selection targets in the training and prediction set and applied these to predict the TP of lines based on line per se or testcross evaluations. On the basis of two experiments with maize, we validated our approach in four scenarios characterized by different selection targets. Forecasted and empirically observed predictive abilities obtained by cross-validation generally agreed well, with deviations between −0.06 and 0.01 only. Applying the prediction model to a different tester and/or year reduced the predictive ability by not more than 18 %. Accounting additionally for truncation selection in our formulas indicated a substantial reduction in predictive ability in the prediction set, amounting, e.g., to 53 % for a selected fraction α = 10 %. In conclusion, our deterministic formulas enable forecasting the predictive abilities of new selection targets with sufficient precision and could be used to calculate parameters required for optimizing the allocation of resources in multi-stage genomic selection.


Prediction Accuracy Predictive Ability Genomic Selection Double Haploid Line Genomic Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank F. Mauch, J. Jesse, H. Poeschel, R. Lutz, T. Schmidt, S. Pluskat and R. Volkhausen for their assistance in conducting the field experiments. Funding for this research came from the German Federal Ministry of Education and Research (BMBF) within the framework of the projects GABI-Energy (FK 0315045B) and Cornfed (FK 03115461A) as well as from Syngenta under a Ph.D. fellowship for PS.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

The authors declare that all experiments comply with the current laws in Germany.

Supplementary material

122_2015_2577_MOESM1_ESM.docx (143 kb)
Supplementary material 1 (DOCX 143 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Pascal Schopp
    • 1
  • Christian Riedelsheimer
    • 1
  • H. Friedrich Utz
    • 1
  • Chris-Carolin Schön
    • 2
  • Albrecht E. Melchinger
    • 1
  1. 1.Department of Applied Genetics, Institute of Plant Breeding, Seed Science and Population GeneticsUniversity of HohenheimStuttgartGermany
  2. 2.Plant Breeding, Center of Life and Food Sciences WeihenstephanTechnische Universität MünchenFreisingGermany

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