Theoretical and Applied Genetics

, Volume 132, Issue 2, pp 347–353 | Cite as

Small ad hoc versus large general training populations for genomewide selection in maize biparental crosses

  • Sofía P. Brandariz
  • Rex BernardoEmail author
Original Article


Key message

For genomewide selection in each biparental population, it is better to use a smaller ad hoc training population than a single, large training population.


In genomewide selection, different types of training populations can be used for a biparental population made from homozygous parents (A and B). Our objective was to determine whether the response to selection (R) and predictive ability (rMP) in an A/B population are higher with a large training population that is used for all biparental crosses, or with a smaller ad hoc training population highly related to the A/B population. We studied 969 biparental maize (Zea mays L.) populations phenotyped at four to 12 environments. Parent–offspring marker imputation was done for 2911 single nucleotide polymorphism loci. For 27 A/B populations, training populations were constructed by pooling: (1) all prior populations with A as one parent (A/*, where * is a related inbred) and with B as one parent (*/B) [general combining ability (GCA) model]; (2) A/* or */B crosses only; (3) all */* crosses (same background model, SB); and (4) all */*, A/*, and */B crosses (SB + GCA model). The SB model training population was 450–6000% as large as the GCA model training populations, but the mean coefficient of coancestry between the training population and A/B population was lower for the SB model (0.44) than for the GCA model (0.71). The GCA model had the highest R and rMP for all traits. For yield, R was 0.22 Mg ha−1 with the GCA model and 0.15 Mg ha−1 with the SB model. We concluded that it is best to use an ad hoc training population for each A/B population.



Sofía P. Brandariz was supported by Ph.D. fellowship funded by Monsanto. We thank Drs. David Butruille, Mike Lohuis, and Sam Eathington for allowing us to use the data sets in this study, and Dr. Shengqiang Zhong for his valuable input and insights on our work with these data sets since 2011.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Agronomy and Plant GeneticsUniversity of MinnesotaSaint PaulUSA

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