ArticlePloS one2015
Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- Structured multimodal deep learning improves genomic prediction in future environments.Genetics · 2026Article
- Predictive evolutionary genomics: principles, validation, and practice.The New phytologist · 2026Review
- Genomic prediction vs. gene-based crop models: a case study on rice trait prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Bridging tradition and innovation: a review of computer simulations in plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Ensemble-based genomic prediction for maize flowering time improves prediction accuracy and reveals novel insights into trait genetic variation.G3 (Bethesda, Md.) · 2026Article
- Multi-environment evaluation and genomic prediction of agronomic traits in the southern US rice genepool.The plant genome · 2026Article
- Back to the future 2: the implications of germplasm structure on the balance between short- and long-term genetic gain in a changing target population of environments.G3 (Bethesda, Md.) · 2026Article
- Critical evaluation of the theory and practice of feed-forward neural networks for genomic prediction.G3 (Bethesda, Md.) · 2026Article
- Transformer model to determine spatio-temporal relationships of variables, and interpretability for soybean seed yield, oil, and protein prediction.Frontiers in artificial intelligence · 2026Article
- Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction.The plant genome · 2025Article
- Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Toward a general framework for AI-enabled prediction in crop improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Generative AI for predictive breeding: hopes and caveats.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Penalized factorial regression as a flexible and computationally attractive reaction norm model for prediction in the presence of GxE.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Sparse testing designs for optimizing resource allocation in multi-environment cassava breeding trials.The plant genome · 2025Article
- Genomic and phenomic prediction for soybean seed yield, protein, and oil.The plant genome · 2025Article
- Article
34 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.