ArticleGenetics2021
Optimal breeding-value prediction using a sparse selection index.
Article in Genetics, 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.
- Derivation of prediction error variance for non-genotyped individuals in genomic selection.Frontiers in genetics · 2026Article
- Optimization of Genomic Breeding Value Estimation Model for Abdominal Fat Traits Based on Machine Learning.Animals : an open access journal from MDPI · 2025Article
- Breaking down data silos across companies to train genome-wide predictions: A feasibility study in wheat.Plant biotechnology journal · 2025Article
- Genomic selection: Essence, applications, and prospects.The plant genome · 2025Review
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- Leveraging historical trials to predict Fusarium head blight resistance in spring wheat breeding programs.The plant genome · 2025Article
- Breeding values and index creation for health and behavior traits in Labrador Retriever guide dogs.Frontiers in veterinary science · 2025Article
- Use of multi-trait principal component selection index to identify fall armyworm (Frontiers in plant science · 2025Article
- Genome-wide association and genomic prediction for iron and zinc concentration and iron bioavailability in a collection of yellow dry beans.Frontiers in genetics · 2024Article
- Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).The plant genome · 2023Article
- Sparse multi-trait genomic prediction under balanced incomplete block design.The plant genome · 2023Article
- Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize.BMC plant biology · 2023Article
- The trend of breeding value research in animal science: bibliometric analysis.Archives animal breeding · 2023Article
- Genomic selection for morphological and yield-related traits using genome-wide SNPs in oil palm.Molecular breeding : new strategies in plant improvement · 2022Article
- Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data.The plant genome · 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
- Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview.Frontiers in plant science · 2021Article
- Training Set Construction for Genomic Prediction in Auto-Tetraploids: An Example in Potato.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP.
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.