Giessener Elektronische Bibliothek

GEB - Giessener Elektronische Bibliothek

Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles

Zenke-Philippi, Carola ; Thiemann, Alexander ; Seifert, Felix ; Schrag, Tobias ; Melchinger, Albrecht E. ; Scholten, Stefan ; Frisch, Matthias


Originalveröffentlichung: (2016) BMC Genomics 17:262 doi: 10.1186/s12864-016-2580-y
Zum Volltext im pdf-Format: Dokument 1.pdf (908 KB)


Bitte beziehen Sie sich beim Zitieren dieses Dokumentes immer auf folgende
URN: urn:nbn:de:hebis:26-opus-123257
URL: http://geb.uni-giessen.de/geb/volltexte/2016/12325/

Bookmark bei del.icio.us


Sammlung: Open Access - Publikationsfonds
Universität Justus-Liebig-Universität Gießen
Institut: Institute of Agronomy and Plant Breeding II
Fachgebiet: Agrarwissenschaften, Ökotrophologie und Umweltmanagement fachübergreifend
DDC-Sachgruppe: Landwirtschaft
Dokumentart: Aufsatz
Sprache: Englisch
Erstellungsjahr: 2016
Publikationsdatum: 07.11.2016
Kurzfassung auf Englisch: 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.
Lizenz: Lizenz-Logo  Creative Commons - Namensnennung