ArticleGenetics2022
Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package.
Article in Genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fast analysis of biobank-size data and meta-analysis using the BGLR R-package.G3 (Bethesda, Md.) · 2025Pooled it
- Integrating Epistatic Interactions into Genomic Prediction of Growth and Fillet Fat Content in Common Carp.International journal of molecular sciences · 2026Article
- East-west divergence and metabolic differentiation in celery domestication.Nature communications · 2026Article
- Integrating GWAS-guided markers preselection with genomic selection enhances prediction of pulpwood-related traits in slash pine (Pinus elliottii Englem.).BMC plant biology · 2026Article
- Mining QTLs and candidate genes in bread wheat associated with kernel hardness through GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Nonlinear genomic selection index accelerates multi-trait crop improvement.Nature communications · 2026Article
- Integrative genomic approaches to unravel genomic regions and candidate genes associated with flag leaf photosynthesis at the reproductive stage in rice.Frontiers in plant science · 2026Article
- Multi-trait and multi-environment genomic prediction enhances yield components improvement in durum wheat.Frontiers in plant science · 2026Article
- Predictive breeding and marker-assisted selection for grain quality and freezing tolerance in durum wheat.Frontiers in plant science · 2026Article
- Leveraging GWAS-Identified Markers in Combination with Bayesian and Machine Learning Models to Improve Genomic Selection in Soybean.International journal of molecular sciences · 2025Article
- Deciphering the regulatory network of carbon isotope discrimination in bread wheat through genome-wide association studies and genomic prediction.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- A Framework Integrating GWAS and Genomic Selection to Enhance Prediction Accuracy of Economical Traits in Common Carp.International journal of molecular sciences · 2025Article
- Multi-trait/environment sparse genomic prediction using the SFSI R-package.The plant genome · 2025Article
- A major QTL region associated with powdery mildew resistance in leaves and fruits of the reconstructed garden strawberry.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- GWAS-assisted and multitrait genomic prediction for improvement of seed yield and canning quality traits in a black bean breeding panel.G3 (Bethesda, Md.) · 2025Article
- An Unexplored Diversity for Adaptation of Germination to High Temperatures inEvolutionary applications · 2025Article
- Integrating phenomic selection using single-kernel near-infrared spectroscopy and genomic selection for corn breeding improvement.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- Mining genomic regions associated with stomatal traits and their candidate genes in bread wheat through genome-wide association study (GWAS).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Enhancing wheat genomic prediction by a hybrid kernel approach.Frontiers in plant science · 2025Article
- Genomic prediction of male fertility in Brown Swiss cattle.JDS communications · 2024Article
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2 authors.
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Abstract
The BGLR-R package implements various types of single-trait shrinkage/variable selection Bayesian regressions. The package was first released in 2014, since then it has become a software very often used in genomic studies. We recently develop functionality for multitrait models. The implementation allows users to include an arbitrary number of random-effects terms. For each set of predictors, users can choose diffuse, Gaussian, and Gaussian-spike-slab multivariate priors. Unlike other software packages for multitrait genomic regressions, BGLR offers many specifications for (co)variance parameters (unstructured, diagonal, factor analytic, and recursive). Samples from the posterior distribution of the models implemented in the multitrait function are generated using a Gibbs sampler, which is implemented by combining code written in the R and C programming languages. In this article, we provide an overview of the models and methods implemented BGLR's multitrait function, present examples that illustrate the use of the package, and benchmark the performance of the software.
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