Genomic Selection in Rice Using Targeted Sequencing: Model Evaluation and Training Population Optimization for Breeding Applications.
- 1Fujian Key Laboratory of Agricultural Genetic Engineering, Institute of Biotechnology, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China.
Abstract
Background/objectives
Genomic selection (GS) can accelerate crop improvement, but its application in breeding programs is often constrained by the cost of high-density genotyping. This study evaluated genomic prediction using 4647 targeted sequencing-derived SNPs in 829 rice accessions and investigated the effects of prediction model selection, population structure, training population optimization, and GWAS-assisted marker selection on predictive performance.Methods
Seventeen GS models were evaluated for eight agronomic traits using repeated five-fold cross-validation, cross-population prediction, STPGA-based training population optimization, and GWAS-derived marker subsets. Resaults: Predictive performance varied substantially among traits and models. For grain length (GL), EnsembleGS achieved the highest predictive performance among the 17 models (r = 0.838), while RKHS showed the highest performance for grain number per panicle (r = 0.698), RFR for heading date (r = 0.854), and EnsembleGS for length-to-width ratio (r = 0.844). Cross-population prediction generally resulted in lower predictive performance than within-population cross-validation, with substantial variation among transfer directions. STPGA retained substantial predictive performance with reduced training population sizes; for GL, the highest performance reached r = 0.800 with a 400-individual STPGA-selected training population. GWAS-guided marker prioritization also retained substantial predictive information after marker reduction. For GL, the highest performance reached r = 0.789 and 0.802 using the GWAS-Top500 and GWAS-Top1000 subsets, respectively, compared with 0.751 and 0.775 for the corresponding Random500 and Random1000 subsets. For length-to-width ratio, the corresponding values were r = 0.764 and 0.800 for GWAS-Top500 and GWAS-Top1000, compared with 0.727 and 0.783 for the random subsets.Conclusions
Integrating targeted sequencing with trait- and model-aware prediction, optimized training population design, and GWAS-guided marker prioritization can retain substantial genomic prediction performance while reducing marker and training population requirements, providing a potentially cost-effective framework for genomic selection in rice breeding.
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BibTeX
@article{chen2026genomic,
title = {{Genomic Selection in Rice Using Targeted Sequencing: Model Evaluation and Training Population Optimization for Breeding Applications.}},
author = {Zaijie Chen and Leibo Pan and Yue Wang and Min Lin and Yan Lin and Mingji Wu and Huaqing Liu and Taijiao Hu and Shaohua Yang and Yana Song},
journal = {Genes},
year = {2026},
month = oct,
doi = {10.3390/genes17101240},
url = {https://europepmc.org/article/MED/42938356},
note = {CC BY},
}
RIS
TY - JOUR
TI - Genomic Selection in Rice Using Targeted Sequencing: Model Evaluation and Training Population Optimization for Breeding Applications.
AU - Zaijie Chen
AU - Leibo Pan
AU - Yue Wang
AU - Min Lin
AU - Yan Lin
AU - Mingji Wu
AU - Huaqing Liu
AU - Taijiao Hu
AU - Shaohua Yang
AU - Yana Song
PY - 2026
DA - 2026/10/08
JO - Genes
DO - 10.3390/genes17101240
UR - https://europepmc.org/article/MED/42938356
AB - Background/objectives Genomic selection (GS) can accelerate crop improvement, but its application in breeding programs is often constrained by the cost of high-density genotyping. This study evaluated genomic prediction using 4647 targeted sequencing-derived SNPs in 829 rice accessions and investigated the effects of prediction model selection, population structure, training population optimization, and GWAS-assisted marker selection on predictive performance.Methods Seventeen GS models were evaluated for eight agronomic traits using repeated five-fold cross-validation, cross-population prediction, STPGA-based training population optimization, and GWAS-derived marker subsets. Resaults: Predictive performance varied substantially among traits and models. For grain length (GL), EnsembleGS achieved the highest predictive performance among the 17 models (r = 0.838), while RKHS showed the highest performance for grain number per panicle (r = 0.698), RFR for heading date (r = 0.854), and EnsembleGS for length-to-width ratio (r = 0.844). Cross-population prediction generally resulted in lower predictive performance than within-population cross-validation, with substantial variation among transfer directions. STPGA retained substantial predictive performance with reduced training population sizes; for GL, the highest performance reached r = 0.800 with a 400-individual STPGA-selected training population. GWAS-guided marker prioritization also retained substantial predictive information after marker reduction. For GL, the highest performance reached r = 0.789 and 0.802 using the GWAS-Top500 and GWAS-Top1000 subsets, respectively, compared with 0.751 and 0.775 for the corresponding Random500 and Random1000 subsets. For length-to-width ratio, the corresponding values were r = 0.764 and 0.800 for GWAS-Top500 and GWAS-Top1000, compared with 0.727 and 0.783 for the random subsets.Conclusions Integrating targeted sequencing with trait- and model-aware prediction, optimized training population design, and GWAS-guided marker prioritization can retain substantial genomic prediction performance while reducing marker and training population requirements, providing a potentially cost-effective framework for genomic selection in rice breeding.
SN - 2073-4425
ER -