ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2012
Partial least squares regression, support vector machine regression, and transcriptome-based distances for prediction of maize hybrid performance with gene expression data.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- 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
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Using phenomic selection to predict hybrid values with NIR spectra measured on the parental lines: proof of concept on maize.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Incorporating gene expression and environment for genomic prediction in wheat.Frontiers in plant science · 2025Article
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- High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
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- De novo genome assembly and analyses of 12 founder inbred lines provide insights into maize heterosis.Nature genetics · 2023Article
- Genomic prediction of rice mesocotyl length indicative of directing seeding suitability using a half-sib hybrid population.PloS one · 2023Article
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Food Security: 3D Dynamic Display and Early Warning Platform Construction and Security Strategy.International journal of environmental research and public health · 2022Article
- Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Multi-omics-based prediction of hybrid performance in canola.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.Plant biotechnology journal · 2021Article
- Improving Genomic Prediction Using High-Dimensional Secondary Phenotypes.Frontiers in genetics · 2021Review
- Heterosis Breeding in Eggplant (Plants (Basel, Switzerland) · 2020Review
- Transcriptome-Based Prediction of Complex Traits in Maize.The Plant cell · 2020Article
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Authors and funding
7 authors.
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Abstract
The performance of hybrids can be predicted with gene expression data from their parental inbred lines. Implementing such prediction approaches in breeding programs promises to increase the efficiency of hybrid breeding. The objectives of our study were to compare the accuracy of prediction models employing multiple linear regression (MLR), partial least squares regression (PLS), support vector machine regression (SVM), and transcriptome-based distances (D(B)). For a factorial of 7 flint and 14 dent maize lines, the grain yield of the hybrids was assessed and the gene expression of the parental lines was profiled with a 56k microarray. The accuracy of the prediction models was measured by the correlation between predicted and observed yield employing two cross-validation schemes. The first modeled the prediction of hybrids when testcross data are available for both parental lines (type 2 hybrids), and the second modeled the prediction of hybrids when no testcross data for the parental lines were available (type 0 hybrids). MLR, SVM, and PLS resulted in a high correlation between predicted and observed yield for type 2 hybrids, whereas for type 0 hybrids D(B) had greater prediction accuracy. The regression methods were robust to the choice of the set of profiled genes and required only a few hundred genes. In contrast, for an accurate hybrid prediction with D(B), 1,000-1,500 genes were required, and the prediction accuracy depended strongly on the set of profiled genes. We conclude that for prediction within one set of genetic material MLR is a promising approach, and for transferring prediction models from one set of genetic material to a related one, the transcriptome-based distance D(B) is most promising.
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