ArticleGenetica2015
A novel genomic selection method combining GBLUP and LASSO.
Article in Genetica, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Evolutionary structure constrains genomic prediction accuracy more than model complexity in mango (Mangifera indica L.).G3 (Bethesda, Md.) · 2026Article
- Enhancing genomic prediction ability of blast resistance using genome-wide association study-derived marker weights in two rice (Oryza sativa L.) populations.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Complex traits and candidate genes: estimation of genetic variance components across multiple genetic architectures.G3 (Bethesda, Md.) · 2023Article
- Genomic Prediction of Columnaris Disease Resistance in Catfish.Marine biotechnology (New York, N.Y.) · 2020Article
- On the usefulness of parental lines GWAS for predicting low heritability traits in tropical maize hybrids.PloS one · 2020Article
- Genomic prediction using an iterative conditional expectation algorithm for a fast BayesC-like model.Genetica · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic prediction of quantitative traits is a critical task in plant and animal breeding. Genomic selection is an accurate and efficient method of estimating genetic merits by using high-density genome-wide single nucleotide polymorphisms (SNP). In the framework of linear mixed models, we extended genomic best linear unbiased prediction (GBLUP) by including additional quantitative trait locus (QTL) information that was extracted from high-throughput SNPs by using least absolute shrinkage selection operator (LASSO). GBLUP was combined with three LASSO methods-standard LASSO (SLGBLUP), adaptive LASSO (ALGBLUP), and elastic net (ENGBLUP)-that were used for detecting QTLs, and these QTLs were fitted as fixed effects; the remaining SNPs were fitted using a realized genetic relationship matrix. Simulations performed under distinct scenarios revealed that (1) the prediction accuracy of SLGBLUP was the lowest; (2) the prediction accuracies of ALGBLUP and ENGBLUP were equivalent to or higher than that of GBLUP, except under scenarios in which the number of QTLs was large; and (3) the persistence of prediction accuracy over generations was strongest in the case of ENGBLUP. Building on the favorable computational characteristics of GBLUP, ENGBLUP enables robust modeling and efficient computation to be performed for genomic selection.
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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.