ArticleFrontiers in plant science2021
Response to Early Generation Genomic Selection for Yield in Wheat.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Evaluating reaction norm and fixed effect models for genomic selection of end-use quality in wheat.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction in dolichos bean (Lablab purpureus L. Sweet) breeding: choice of the type of training population and selection generation.BMC plant biology · 2026Article
- A breeding strategy for high-oleic acid content and high-yield peanut using Tag SNPs and genomic selection.BMC plant biology · 2026Article
- GWAS and genomic prediction for resistance to soybean mosaic virus strain SC7.BMC plant biology · 2026Article
- Joint validation of yield and inter-annual consistency and elite lines selection in 52 advanced wheat lines based on a PCA-membership function-TOPSIS collaborative system.Frontiers in plant science · 2026Article
- Genome analyses and breeding of polyploid crops.Nature plants · 2025Review
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI.Scientific reports · 2025Article
- Drought-tolerant wheat for enhancing global food security.Functional & integrative genomics · 2024Review
- Genomic prediction of the performance of tropical doubled haploid maize lines under artificial Striga hermonthica (Del.) Benth. infestation.G3 (Bethesda, Md.) · 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
- Article
- Multimodal deep learning methods enhance genomic prediction of wheat breeding.G3 (Bethesda, Md.) · 2023Article
- Results from rapid-cycle recurrent genomic selection in spring bread wheat.G3 (Bethesda, Md.) · 2023Article
- Accuracy of Selection in Early Generations of Field Pea Breeding Increases by Exploiting the Information Contained in Correlated Traits.Plants (Basel, Switzerland) · 2023Article
- Optimal Contribution Selection Improves the Rate of Genetic Gain in Grain Yield and Yield Stability in Spring Canola in Australia and Canada.Plants (Basel, Switzerland) · 2023Article
- Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize (Frontiers in genetics · 2023Article
- Enhancing genetic gain through the application of genomic selection in developing irrigated rice for the favorable ecosystem in Bangladesh.Frontiers in genetics · 2023Article
- Separation of the effects of two reduced height (Rht) genes and genomic background to select for less Fusarium head blight of short-strawed winter wheat (Triticum aestivum L.) varieties.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated increasing genetic gain for grain yield using early generation genomic selection (GS). A training set of 1,334 elite wheat breeding lines tested over three field seasons was used to generate Genomic Estimated Breeding Values (GEBVs) for grain yield under irrigated conditions applying markers and three different prediction methods: (1) Genomic Best Linear Unbiased Predictor (GBLUP), (2) GBLUP with the imputation of missing genotypic data by Ridge Regression BLUP (rrGBLUP_imp), and (3) Reproducing Kernel Hilbert Space (RKHS) a.k.a. Gaussian Kernel (GK). F2 GEBVs were generated for 1,924 individuals from 38 biparental cross populations between 21 parents selected from the training set. Results showed that F2 GEBVs from the different methods were not correlated. Experiment 1 consisted of selecting F2s with the highest average GEBVs and advancing them to form genomically selected bulks and make intercross populations aiming to combine favorable alleles for yield. F4:6 lines were derived from genomically selected bulks, intercrosses, and conventional breeding methods with similar numbers from each. Results of field-testing for Experiment 1 did not find any difference in yield with genomic compared to conventional selection. Experiment 2 compared the predictive ability of the different GEBV calculation methods in F2 using a set of single plant-derived F2:4 lines from randomly selected F2 plants. Grain yield results from Experiment 2 showed a significant positive correlation between observed yields of F2:4 lines and predicted yield GEBVs of F2 single plants from GK (the predictive ability of 0.248,
Indexed as
Identifiers
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.