ArticleBriefings in bioinformatics2024
GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Research progress on genomic selection breeding technology for crops.Protoplasma · 2026Review
- Integrating molecular networks and physiological adaptation for heat-resilient crops.Journal of integrative plant biology · 2026Review
- Integrating multi-model GWAS prior information enhances genomic prediction of cold tolerance traits in Populus simonii.BMC plant biology · 2026Article
- Enhancing genomic prediction accuracy in Huaxi cattle through integration of transcriptomic data and a self-attention-based SNP selection strategy.BMC bioinformatics · 2026Article
- Genome-wide association study and genomic prediction of bolting trait in spinach (Frontiers in plant science · 2026Article
- Physiological determinants of heterosis in hybrid rice (Frontiers in plant science · 2026Article
- Article
- 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
- Enhancing Across-Population Genomic Prediction for Maize Hybrids.Plants (Basel, Switzerland) · 2024Article
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Authors and funding
13 authors.
Funding
Abstract
Genomic selection (GS) has emerged as an effective technology to accelerate crop hybrid breeding by enabling early selection prior to phenotype collection. Genomic best linear unbiased prediction (GBLUP) is a robust method that has been routinely used in GS breeding programs. However, GBLUP assumes that markers contribute equally to the total genetic variance, which may not be the case. In this study, we developed a novel GS method called GA-GBLUP that leverages the genetic algorithm (GA) to select markers related to the target trait. We defined four fitness functions for optimization, including AIC, BIC, R2, and HAT, to improve the predictability and bin adjacent markers based on the principle of linkage disequilibrium to reduce model dimension. The results demonstrate that the GA-GBLUP model, equipped with R2 and HAT fitness function, produces much higher predictability than GBLUP for most traits in rice and maize datasets, particularly for traits with low heritability. Moreover, we have developed a user-friendly R package, GAGBLUP, for GS, and the package is freely available on CRAN (https://CRAN.R-project.org/package=GAGBLUP).
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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.