ArticleTAG. Theoretical and applied genetics. Theoretische und angewandte Genetik2022
The performance of phenomic selection depends on the genetic architecture of the target trait.
Article in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-locus genome-wide association study on the rheological traits in wheat flour dough.BMC genomics · 2026Article
- Resistance gene againstFrontiers in plant science · 2026Review
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection.Plant communications · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 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
- Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Implementing multi-trait genomic selection to improve grain milling quality in oats (Avena sativa L.).The plant genome · 2024Article
- Using drone-retrieved multispectral data for phenomic selection in potato breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Multispectral-derived genotypic similarities from budget cameras allow grain yield prediction and genomic selection augmentation in single and multi-environment scenarios in spring wheat.Molecular breeding : new strategies in plant improvement · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Predictor bias in genomic and phenomic selection.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions.G3 (Bethesda, Md.) · 2023Article
- Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance.Frontiers in genetics · 2023Review
- Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom.Plants (Basel, Switzerland) · 2022Review
- 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
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Authors and funding
7 authors.
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Abstract
key messageThe phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
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