ArticlePloS one2019
Enhancing genomic selection by fitting large-effect SNPs as fixed effects and a genotype-by-environment effect using a maize BC1F3:4 population.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Integrative genomics and zero-shot deep learning-based phenotyping reveal the genetic architecture of seed traits in mungbean (Vigna radiata L.).The plant genome · 2026Article
- Uncovering the genetic architecture of biomass yield and related traits in Southern US oat germplasm using genome-wide association study.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Genomic prediction using a cost-effective mid-density SNP platform in spring wheat.Frontiers in plant science · 2026Article
- Enhancing genomic predictive ability of yield and yield-related traits in spring wheat by integrating major plant adaptation genes as a fixed effect.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Increased genomic predictive ability in mango using GWAS-preselected variants and fixed-effect SNPs.Frontiers in plant science · 2025Article
- Effects of SNP marker density and training population size on prediction accuracy in alfalfa (Medicago sativa L.) genomic selection.The plant genome · 2024Article
- Genetic analysis of phenotypic plasticity identifiesFrontiers in plant science · 2023Article
- Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize.International journal of molecular sciences · 2022Article
- Identification and validation of a QTL for spikelet number on chromosome arm 6BL of common wheat (Molecular breeding : new strategies in plant improvement · 2022Article
- Experimental evaluation of genomic selection prediction for rust resistance in sugarcane.The plant genome · 2021Article
- Training set design in genomic prediction with multiple biparental families.The plant genome · 2021Article
- Effectiveness of genomic selection for improving provitamin A carotenoid content and associated traits in cassava.G3 (Bethesda, Md.) · 2021Article
- Genomic Prediction across Structured Hybrid Populations and Environments in Maize.Plants (Basel, Switzerland) · 2021Article
- QTL Mapping and Prediction of Haploid Male Fertility Traits in Maize (Plants (Basel, Switzerland) · 2020Article
- Multiple Small-Effect Alleles ofFrontiers in plant science · 2020Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The popularity of genomic selection (GS) has increased owing to its prospects in commercial breeding. It is necessary to enhance GS to increase its efficiency. In this study, a maize BC1F3:4 population, consisting of 481 families, was evaluated for days to anthesis in four environments, and genotyped with DNA chips including 55,000 single nucleotide polymorphisms (SNPs). This population was used to investigate whether GS could be enhanced by borrowing information from the genetic basis and genotype-by-environment (G × E) interaction. The results showed that: 1) fitting the top four large-effect SNPs as fixed effects could increase prediction accuracy, including three minor-effect SNPs explaining less than 10% phenotypic variance; 2) the increase of prediction accuracy when fitting large-effect SNPs as fixed effects was related to the decrease of genetic variance; 3) generally, the GS model fitting large-effect SNPs as fixed effects and G × E component enhanced GS. Therefore, we propose fitting large-effect markers as fixed effects and G × E effect for crop breeding projects in order to obtain accurately predicted phenotypic data and conduct efficient selection of desired plants.
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