ArticleThe plant genome2019
Evaluation of RR-BLUP Genomic Selection Models that Incorporate Peak Genome-Wide Association Study Signals in Maize and Sorghum.
Article in The plant genome, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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62 citing papers in PubMed.
- Genetic insights into bacterial wilt resistance using genomic prediction and association mapping in blueberry.The plant genome · 2026Article
- Genomic prediction of wild-derived powdery mildew resistance for strawberry (Fragaria × ananassa) pre-breeding.Heredity · 2026Article
- GAPIT version 4: integration of GWAS into genomic prediction.Molecular biology and evolution · 2026Article
- Beyond QTL and GWAS: how deep learning, graph models, and multi-omics are reshaping plant genomic prediction analysis.Frontiers in genetics · 2026Review
- AI Agents and Agentic AI in plant breeding: new frontiers, opportunities and challenges.Frontiers in genetics · 2026Article
- Enhancing the prediction accuracy of groundnut yield by integrating significant markers and modeling genotype × environment interaction.The plant genome · 2025Article
- Multifactor Analysis of a Genome-Wide Selection System inPlants (Basel, Switzerland) · 2025Article
- Environmental genomic selection to leverage polygenic local adaptation in barley landraces.Communications biology · 2025Article
- Exploiting historical agronomic data to develop genomic prediction strategies for early clonal selection in the Louisiana sugarcane variety development program.The plant genome · 2025Article
- Genetic control of sweetness and acidity in blackberry.Frontiers in plant science · 2025Article
- Multi-trait ridge regression BLUP withFrontiers in plant science · 2025Article
- Genetic architecture and genomic prediction of vase life in carnation.Frontiers in plant science · 2025Article
- mmGEBLUP: an advanced genomic prediction scheme for genetic improvement of complex traits in crops through integrative analysis of major genes, polygenes, and genotype-environment interactions.Briefings in bioinformatics · 2024Article
- Decoding the genetic blueprint: regulation of key agricultural traits in sorghum.Advanced biotechnology · 2024Review
- GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.Briefings in bioinformatics · 2024Article
- Natural variation of domestication-related genes contributed to latitudinal expansion and adaptation in soybean.BMC plant biology · 2024Article
- Exploring the genetic basis of resistance to Neopestalotiopsis species in strawberry.The plant genome · 2024Article
- Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel.G3 (Bethesda, Md.) · 2024Article
- Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection.The plant genome · 2024Article
- Genomic prediction for agronomic traits in a diverse Flax (Linum usitatissimum L.) germplasm collection.Scientific reports · 2024Article
2 more citing papers are in PubMed but not listed here.
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Abstract
Certain agronomic crop traits are complex and thus governed by many small-effect loci. Statistical models typically used in a genome-wide association study (GWAS) and genomic selection (GS) quantify these signals by assessing genomic marker contributions in linkage disequilibrium (LD) with these loci to trait variation. These models have been used in separate quantitative genetics contexts until recently, when, in published studies, the predictive ability of GS models that include peak associated markers from a GWAS as fixed-effect covariates was assessed. Previous work suggests that such models could be useful for predicting traits controlled by several large-effect and many small-effect genes. We expand this work by evaluating simulated traits from diversity panels in maize ( L.) and sorghum [ (L.) Moench] using ridge-regression best linear unbiased prediction (RR-BLUP) models that include fixed-effect covariates tagging peak GWAS signals. The ability of such covariates to increase GS prediction accuracy in the RR-BLUP model under a wide variety of genetic architectures and genomic backgrounds was quantified. Of the 216 genetic architectures that we simulated, we identified 60 where the addition of fixed-effect covariates boosted prediction accuracy. However, for the majority of the simulated data, no increase or a decrease in prediction accuracy was observed. We also noted several instances where the inclusion of fixed-effect covariates increased both the variability of prediction accuracies and the bias of the genomic estimated breeding values. We therefore recommend that the performance of such a GS model be explored on a trait-by-trait basis prior to its implementation into a breeding program.
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