ArticleBMC genetics2015
Ridge, Lasso and Bayesian additive-dominance genomic models.
Article in BMC genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Integrating Epistatic Interactions into Genomic Prediction of Growth and Fillet Fat Content in Common Carp.International journal of molecular sciences · 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
- Optimizing genomic selection models for wheat breeding under contrasting water regimes in a mediterranean environment.Plant methods · 2025Article
- Integrated multi-omics profiling reveals shared mechanistic pathways in asthma-allergic rhinitis comorbidity: A hybrid machine learning framework leveraging Mendelian randomization for precision diagnostics.The World Allergy Organization journal · 2025Article
- PAGER: A novel genotype encoding strategy for modeling deviations from additivity in complex trait association studies.BioData mining · 2024Article
- Using visual scores for genomic prediction of complex traits in breeding programs.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2023Article
- Genomic studies of the additive and dominant genetic control on production traits of Euterpe edulis fruits.Scientific reports · 2023Article
- Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones.Frontiers in plant science · 2022Article
- Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications.Frontiers in plant science · 2021Review
- A Review of Genomic Models for the Analysis of Livestock Crossbred Data.Frontiers in genetics · 2020Review
- Advancing crested wheatgrass [Agropyron cristatum (L.) Gaertn.] breeding through genotyping-by-sequencing and genomic selection.PloS one · 2020Article
- SNP and Haplotype-Based Genomic Selection of Quantitative Traits inPlants (Basel, Switzerland) · 2019Article
- Relationship between SNPs ofAnimals : an open access journal from MDPI · 2019Article
- Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize.Frontiers in plant science · 2019Article
- Cassava yield traits predicted by genomic selection methods.PloS one · 2019Article
- BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.G3 (Bethesda, Md.) · 2018Article
- Article
- Non-additive Effects in Genomic Selection.Frontiers in genetics · 2018Review
- Multienvironment genomic variance decomposition analysis of open-pollinated Interior spruce (Molecular breeding : new strategies in plant improvement · 2018Article
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7 authors.
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Abstract
backgroundA complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes).
resultsG-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close.
conclusionsAmongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (-2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
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