ArticleG3 (Bethesda, Md.)2022
Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).
Article in G3 (Bethesda, Md.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 34 citations in OpenAlex.
- Mean and variance heterogeneity loci impact kernel compositional traits in maize.The plant genome · 2025Article
- Genetic Variation and Quantitative Trait Loci Analysis of the Maize Ionome in Response to Phosphorus Fertilisation.Plant, cell & environment · 2025Article
- 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
- 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
- Near-infrared reflectance spectroscopy phenomic prediction can perform similarly to genomic prediction of maize agronomic traits across environments.The plant genome · 2024Article
- Optimizing selection based on BLUPs or BLUEs in multiple sets of genotypes differing in their population parameters.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- An ARF gene mutation creates flint kernel architecture in dent maize.Nature communications · 2024Article
- Performance of phenomic selection in rice: Effects of population size and genotype-environment interactions on predictive ability.PloS one · 2024Article
- Phenomic Selection for Hybrid Rapeseed Breeding.Plant phenomics (Washington, D.C.) · 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
- Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery.Frontiers in plant science · 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
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection is a well-investigated approach that facilitates and supports selection decisions for complex traits and has meanwhile become a standard tool in modern plant breeding. Phenomic selection has only recently been suggested and uses the same statistical procedures to predict the targeted traits but replaces marker data with near-infrared spectroscopy data. It may represent an attractive low-cost, high-throughput alternative but has not been sufficiently studied until now. Here, we used 400 genotypes of maize (Zea mays L.) comprising elite lines of the Flint and Dent heterotic pools as well as 6 Flint landraces, which were phenotyped in multienvironment trials for anthesis-silking-interval, early vigor, final plant height, grain dry matter content, grain yield, and phosphorus concentration in the maize kernels, to compare the predictive abilities of genomic as well as phenomic prediction under different scenarios. We found that both approaches generally achieved comparable predictive abilities within material groups. However, phenomic prediction was less affected by population structure and performed better than its genomic counterpart for predictions among diverse groups of breeding material. We therefore conclude that phenomic prediction is a promising tool for practical breeding, for instance when working with unknown and rather diverse germplasm. Moreover, it may make the highly monopolized sector of plant breeding more accessible also for low-tech institutions by combining well established, widely available, and cost-efficient spectral phenotyping with the statistical procedures elaborated for genomic prediction - while achieving similar or even better results than with marker data.
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