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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.
Afiliación
  • Zenke-Philippi C; Institute of Agronomy and Plant Breeding II, Justus Liebig University, Giessen, 35392, Germany.
  • Thiemann A; Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany.
  • Seifert F; Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany.
  • Schrag T; Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany.
  • Melchinger AE; Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany.
  • Scholten S; Biocenter Klein Flottbek, Developmental Biology and Biotechnology, University of Hamburg, Hamburg, 22609, Germany.
  • Frisch M; Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, Stuttgart, 70593, Germany.
BMC Genomics ; 17: 262, 2016 Mar 29.
Article en En | MEDLINE | ID: mdl-27025377
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.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Marcadores Genéticos / Zea mays / Transcriptoma / Vigor Híbrido Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2016 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Marcadores Genéticos / Zea mays / Transcriptoma / Vigor Híbrido Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: BMC Genomics Asunto de la revista: GENETICA Año: 2016 Tipo del documento: Article País de afiliación: Alemania
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