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

BMC Genomics

Research Article

Hans-Peter Piepho¹  Sidi Boubacar Ould Estaghvirou¹  Joseph O Ogutu¹  Torben Schulz-Streeck²  Milena Ouzunova³  Carsten Knaak³  Andres Gordillo⁴ 

[1] Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany;Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Fruwirthstrasse 23, 70599, Stuttgart, Germany;KWS SAAT AG, 373555, Einbeck, Germany;KWS SAAT AG, 373555, Einbeck, Germany;KWS-Lochow GMBH, Ferdinand-von-Lochow-Strasse 5, 29303, Bergen, Germany;

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

DOI  :  10.1186/1471-2164-14-860

 received in 2013-06-10, accepted in 2013-11-22,  发布年份 2013

来源: Springer

【 摘 要 】

BackgroundIn 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.ResultsThe 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.ConclusionsThe 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.

【 授权许可】

Unknown   
© Ould Estaghvirou et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.