ArticleG3 (Bethesda, Md.)2016
Genomic Selection in Multi-environment Crop Trials.
Article in G3 (Bethesda, Md.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed, 89 citations in OpenAlex.
- Missing comparability: When genomic selection faces field variability. A case study in soybeans.The plant genome · 2026Article
- Optimizing training sets for genomic selection to identify superior genotypes across multiple environments.G3 (Bethesda, Md.) · 2026Article
- Precision-weighted mixed models identify high-yielding and temporally stable cowpea germplasm across three growing seasons.Frontiers in plant science · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Improved bivariate analysis of canola survivability against blackleg disease.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Agricultural landscape genomics to increase crop resilience.Plant communications · 2025Review
- Analyses of crop yield dynamics and the development of a multimodal neural network prediction model with G×E×M interactions.Frontiers in plant science · 2025Article
- Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties.Frontiers in plant science · 2024Article
- Genomic selection using random regressions on known and latent environmental covariates.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
- Article
- Open-source analytical pipeline for robust data analysis, visualizations and sharing in crop breeding.Plant methods · 2022Article
- Empirical comparison of time series models and tensor product penalised splines for modelling spatial dependence in plant breeding field trials.Frontiers in plant science · 2022Article
- Crop genomic selection with deep learning and environmental data: A survey.Frontiers in artificial intelligence · 2022Review
- Plant Genotype to Phenotype Prediction Using Machine Learning.Frontiers in genetics · 2022Review
- Overview of Major Computer Packages for Genomic Prediction of Complex Traits.Methods in molecular biology (Clifton, N.J.) · 2022Article
- OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security.Plants (Basel, Switzerland) · 2021Review
- Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Frontiers in plant science · 2021Article
- Evidence for the Application of Emerging Technologies to Accelerate Crop Improvement - A Collaborative Pipeline to Introgress Herbicide Tolerance Into Chickpea.Frontiers in plant science · 2021Article
- Genomic Selection in Sugarcane: Current Status and Future Prospects.Frontiers in plant science · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.
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What OpenQuestion holds
Registered trials
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.