Prediction of hybrid performance in maize with transcriptome data

Background: Ridge regression models can be used for predicting heterosis and hybrid performance. Their application to mRNA transcription profiles has not yet been investigated. Our objective was to compare the prediction accuracy of models employing mRNA transcription profiles with that of models employing genome-wide markers using a data set of 98 maize hybrids from a breeding program. Results: We predicted hybrid performance and mid-parent heterosis for grain yield and grain dry matter content and employed cross validation to assess the prediction accuracy. Prediction with a ridge regression model using random effects for mRNA transcription profiles resulted in similar prediction accuracies than employing the model to DNA markers. For hybrids, of which none of the parental inbred lines was part of the training set, the ridge regression model did not reach the prediction accuracy that was obtained with a model using transcriptome-based distances. Conclusion: We conclude that mRNA transcription profiles are a promising alternative to DNA markers for hybrid prediction, but further studies with larger data sets are required to investigate the superiority of alternative prediction models. Background The resources for field trials in a hybrid breeding program are restricted and only a fraction of all possible hybrids that could potentially be generated by crossing the inbred lines developed in each cycle of the breeding program can be phenotypically evaluated. The principle of hybrid prediction is to link the performance of phenotypically evaluated hybrids to predictors, such as DNA markers or mRNA transcription profiles, that can be assessed in the parental lines of the hybrids. For each state of the predictor, its effect on the phenotype is estimated and these effects are then used to predict the performance of new hybrids. DNA markers were employed for hybrid prediction in maize and proved to be superior to prediction approaches based solely on pedigree and phenotypic data [1–5]. First *Correspondence: matthias.frisch@uni-giessen.de 1Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392 Giessen, Germany Full list of author information is available at the end of the article results on using the mRNA transcriptome for hybrid prediction with distance-based approaches [6] or regressionbased approaches [7] showed promising results. Genomewide prediction of general combining ability (GCA) or testcross performance [8–10] can be regarded as a special case of hybrid prediction where one parental component (the tester) is known and the effects of the predictors assessed at the second parental component are used for hybrid prediction. In this context, first results of using metabolites as predictors were successful [10] but showed a lower prediction accuracy than using SNP markers as predictors. Two important situations can be distinguished in hybrid prediction. The first is that the parental lines of a potential hybrid have already been evaluated for testcross performance with other lines of the breeding pool. If such testcross data are available for both parental lines but the hybrid itself is not yet generated, then we refer to the hybrid as type 2 hybrid (testcross data for two parents available). The second situation is that the parental © 2016 Zenke-Philippi et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.