ArticleBMC genomics2016
Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Integrating Genetic Diversity and Agronomic Innovations for Climate-Resilient Maize Systems.Plants (Basel, Switzerland) · 2025Review
- Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP.Frontiers in plant science · 2023Article
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Predicting hybrid rice performance using AIHIB model based on artificial intelligence.Scientific reports · 2022Article
- Incorporating Omics Data in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations.Plant methods · 2021Article
- Multi-omics-based prediction of hybrid performance in canola.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat.Genes · 2021Article
- Heterosis Breeding in Eggplant (Plants (Basel, Switzerland) · 2020Review
- Transcriptome-Based Prediction of Complex Traits in Maize.The Plant cell · 2020Article
- A directed learning strategy integrating multiple omic data improves genomic prediction.Plant biotechnology journal · 2019Article
- A metabolome-based core hybridisation strategy for the prediction of rice grain weight across environments.Plant biotechnology journal · 2019Article
- Modeling copy number variation in the genomic prediction of maize hybrids.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Small RNA-based prediction of hybrid performance in maize.BMC genomics · 2018Article
- Genome-wide regression models considering general and specific combining ability predict hybrid performance in oilseed rape with similar accuracy regardless of trait architecture.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2018Article
- Classification-driven framework to predict maize hybrid field performance from metabolic profiles of young parental roots.PloS one · 2018Article
- Omics-based hybrid prediction in maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2017Article
- Genomic selection of crossing partners on basis of the expected mean and variance of their derived lines.PloS one · 2017Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRidge 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.
resultsWe 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.
conclusionWe 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.
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