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Open Access Highly Accessed Research article

Evaluation of approaches for estimating the accuracy of genomic prediction in plant breeding

Sidi Boubacar Ould Estaghvirou1, Joseph O Ogutu1*, Torben Schulz-Streeck12, Carsten Knaak2, Milena Ouzunova2, Andres Gordillo3 and Hans-Peter Piepho1

Author Affiliations

1 Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany

2 KWS SAAT AG, 373555 Einbeck, Germany

3 KWS-Lochow GMBH, Ferdinand-von-Lochow-Strasse 5, 29303 Bergen, Germany

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BMC Genomics 2013, 14:860  doi:10.1186/1471-2164-14-860

Published: 6 December 2013



In genomic prediction, an important measure of accuracy is the correlation between the predicted and the true breeding values. Direct computation of this quantity for real datasets is not possible, because the true breeding value is unknown. Instead, the correlation between the predicted breeding values and the observed phenotypic values, called predictive ability, is often computed. In order to indirectly estimate predictive accuracy, this latter correlation is usually divided by an estimate of the square root of heritability. In this study we use simulation to evaluate estimates of predictive accuracy for seven methods, four (1 to 4) of which use an estimate of heritability to divide predictive ability computed by cross-validation. Between them the seven methods cover balanced and unbalanced datasets as well as correlated and uncorrelated genotypes. We propose one new indirect method (4) and two direct methods (5 and 6) for estimating predictive accuracy and compare their performances and those of four other existing approaches (three indirect (1 to 3) and one direct (7)) with simulated true predictive accuracy as the benchmark and with each other.


The size of the estimated genetic variance and hence heritability exerted the strongest influence on the variation in the estimated predictive accuracy. Increasing the number of genotypes considerably increases the time required to compute predictive accuracy by all the seven methods, most notably for the five methods that require cross-validation (Methods 1, 2, 3, 4 and 6). A new method that we propose (Method 5) and an existing method (Method 7) used in animal breeding programs were the fastest and gave the least biased, most precise and stable estimates of predictive accuracy. Of the methods that use cross-validation Methods 4 and 6 were often the best.


The estimated genetic variance and the number of genotypes had the greatest influence on predictive accuracy. Methods 5 and 7 were the fastest and produced the least biased, the most precise, robust and stable estimates of predictive accuracy. These properties argue for routinely using Methods 5 and 7 to assess predictive accuracy in genomic selection studies.

Genomic selection; Ridge-regression BLUP; Predictive accuracy; Predictive ability; Heritability; SNP markers; Zea mays; Cross-validation; Plant breeding