Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles. (December 2016)

• Record Type: Journal Article
• Authors: Zenke-Philippi, Carola; Thiemann, Alexander; Seifert, Felix; Schrag, Tobias; Melchinger, Albrecht; Scholten, Stefan; Frisch, Matthias
• Is Part Of: BMC genomics. Volume 17:Number 1(2016)
• Page Start: 1
• Page End: 8
• Publication Date: 2016-12
• Subjects: Genomes -- Periodicals; Gene mapping -- Periodicals; Genomics -- Periodicals; Base Sequence -- Periodicals; Chromosome Mapping -- Periodicals; Genetic Techniques -- Periodicals; Sequence Analysis, DNA -- Periodicals; 572.8605
• DOI: 10.1186/s12864-016-2580-y
• Languages: English
• ISSNs: 1471-2164

Abstract

Abstract 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.