ArticleBMC genomics2024
Enhancing winter wheat prediction with genomics, phenomics and environmental data.
Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Antagonistic pleiotropy at the stem solidness 1 locus balances wheat stem architecture and yield components under terminal drought.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- AI Agents and Agentic AI in plant breeding: new frontiers, opportunities and challenges.Frontiers in genetics · 2026Article
- Near-Infrared Spectroscopy-Based Phenomics Data Can Improve Genomic Prediction of Agronomic and Grain Quality Traits Across Multi-Environment Sorghum Hybrid Trials.Plants (Basel, Switzerland) · 2025Article
- Leveraging genetic resources and genomic prediction to enhance flavonol content in cranberry fruit.The plant genome · 2025Article
- Historic insights and future potential in wheat elaborated using a diverse cultivars collection and extended phenotyping.Scientific reports · 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
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Enhancing prediction accuracy of key biomass partitioning traits in wheat using multi-kernel genomic prediction models integrating secondary traits and environmental covariates.The plant genome · 2025Article
- GBLUP Outperforms Quantile Mapping and Outlier Detection for Enhanced Genomic Prediction.International journal of molecular sciences · 2025Article
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
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Abstract
In the realm of multi-environment prediction, when the goal is to predict a complete environment using the others as a training set, the efficiency of genomic selection (GS) falls short of expectations. Genotype by environment interaction poses a challenge in achieving high prediction accuracies. Consequently, current efforts are focused on enhancing efficiency by integrating various types of inputs, such as phenomics data, environmental information, and other omics data. In this study, we sought to evaluate the impact of incorporating environmental information into the modeling process, in addition to genomic and phenomics information. Our evaluation encompassed five data sets of soft white winter wheat, and the results revealed a significant improvement in prediction accuracy, as measured by the normalized root mean square error (NRMSE), through the integration of environmental information. Notably, there was an average gain in prediction accuracy of 49.19% in terms of NRMSE across the data sets. Moreover, the observed prediction accuracy ranged from 5.68% (data set 3) to 60.36% (data set 4), underscoring the substantial effect of integrating environmental information. By including genomic, phenomic, and environmental data in prediction models, plant breeding programs can improve selection efficiency across locations.
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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.