Theoretical and Applied Genetics

, Volume 120, Issue 1, pp 151–161 | Cite as

Accuracy of genotypic value predictions for marker-based selection in biparental plant populations

  • Robenzon E. Lorenzana
  • Rex BernardoEmail author
Original Paper


The availability of cheap and abundant molecular markers has led to plant-breeding methods that rely on the prediction of genotypic value from marker data, but published information is lacking on the accuracy of genotypic value predictions with empirical data in plants. Our objectives were to (1) determine the accuracy of genotypic value predictions from multiple linear regression (MLR) and genomewide selection via best linear unbiased prediction (BLUP) in biparental plant populations; (2) assess the accuracy of predictions for different numbers of markers (N M) and progenies (N P) used in estimation; and (3) determine if an empirical Bayes approach for modeling of the variances of individual markers and of epistatic effects leads to more accurate predictions in empirical data. We divided each of four maize (Zea mays L.) datasets, one Arabidopsis dataset, and two barley (Hordeum vulgare L.) datasets into an estimation set, where marker effects were calculated, and a test set, where genotypic values were predicted based on markers. Predictions were more accurate with BLUP than with MLR. Predictions became more accurate as N P and N M increased, until sufficient genome coverage was reached. Modeling marker variances with the empirical Bayes method sometimes led to slightly better predictions, but the accuracy with different variants of the empirical Bayes method was often inconsistent. In nearly all cases, the accuracy with BLUP was not significantly different from the highest accuracy across all methods. Accounting for epistasis in the empirical Bayes procedure led to poorer predictions. We concluded that among the methods considered, the quick and simple BLUP approach is the method of choice for predicting genotypic value in biparental plant populations.


Quantitative Trait Locus Multiple Linear Regression Epistatic Effect Recombinant Inbred Well Linear Unbiased 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.



We thank Monsanto and Syngenta Seeds for access to the BM-TC1 germplasm and Syn-DH data set.

Supplementary material

122_2009_1166_MOESM1_ESM.doc (414 kb)
Supplementary material 1 (DOC 413 kb)


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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

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

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