This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.
Abstract
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Proper analysis of phenotypic data is essential for reliable genomic prediction (GP) and sustained genetic gain in breeding programs. In this study, we used phenotypic and genotypic data generated from the winter wheat breeding program of Deutsche Saatveredelung AG (DSV), Lippstadt, Germany, comprising 1,941 genotypes and 6,335 SNP markers across three traits: grain yield, plant height, and heading date. We evaluated the impact of different two-stage analysis strategies: one using the environment (year × location combination) as the analysis unit (TS-S1) and the other using the breeding stage as the analysis unit (TS-S2), each with and without accounting for breeding-stage effects (-YesBS and -NoBS), on the computation of best linear unbiased estimates (BLUEs) and genome-wide prediction ability (PA), defined as the correlation between BLUEs and predicted values. The performance of these 4 different second stage models (TS-S1-NoBS, TS-S1-YesBS, TS-S2-NoBS, and TS-S2-YesBS) were evaluated using the extended genomic best linear prediction (EGBLUP) model under 5-fold cross validation (5-fold CV), leave one year out cross validation (LOY-CV) and leave one breeding stage out cross validation (LOBS-CV) scenarios. In the presence of the strong breeding stage effect ignoring the breeding stage in the model resulted in biased BLUEs and overestimated prediction abilities in the 5-fold CV, and underestimated prediction abilities in the LOY-CV and LOBS-CV. These unstable prediction abilities are driven by confounded environmental effects in the BLUEs due to omission of the breeding-stage effect. In contrast, models that accounted for the breeding stage produced more reliable BLUEs and more stable prediction results across all cross-validation scenarios, with TS-S1-YesBS performing best overall. Overall, our findings demonstrate that breeding stage effects mainly arise from differences in growing conditions and must be adequately considered. Therefore, including breeding stage in phenotypic models is critical to obtaining unbiased BLUEs and ensuring accurate genomic prediction and selection decisions.
Ravindra Reddy Gundala, Georg Witte, Jost Doernte et al.· Frontiers in Plant Science· 0 citations
Genomic selection (GS) has become an important tool for accelerating genetic gain in wheat breeding by enabling the prediction of target traits using genome-wide molecular markers. However, the large-scale implementation of GS in public breeding programs remains constrained by the cost of high-density genotyping platforms. Medium-density targeted genotyping approaches provide a cost-effective alternative while maintaining prediction accuracy. In this study, we evaluated the performance of a public wheat mid-density genotyping platform (Wheat DArTag 3.9K EIB 2.0) for GS by comparing it with a previously deployed higher-density genotyping-by-sequencing (GBS) platform. The analyses were conducted using five consecutive years of CIMMYT Elite Yield Trials comprising more than 5000 elite spring wheat lines evaluated across multiple irrigated, drought, and heat-stressed environments. Trait predictability was assessed for agronomic, phenological, and disease resistance traits using the genomic best linear unbiased prediction (GBLUP) model under several cross-validation scenarios, including within-year and across-year predictions. After quality filtering, the DArTag platform retained approximately 1600–1800 SNPs, whereas the GBS platform retained approximately 6500–9600 SNPs. Across traits and years, no consistent superiority of GBS over DArTag was observed, and correlations between genomic estimated breeding values (GEBVs) obtained from both platforms were high, indicating that both genotyping systems would lead to highly similar selection decisions. The results suggest that, in elite wheat germplasm characterized by long-range linkage disequilibrium and strong realized genomic relationships, medium-density targeted genotyping platforms can retain most of the predictability achieved by higher-density systems. Overall, the public Wheat DArTag 3.9K EIB 2.0 platform represents a scalable and cost-effective solution for implementing GS in operational wheat breeding programs.
Rice (Oryza sativa L.) is highly vulnerable to drought during the reproductive phase, with yield losses exceeding 50% due to spikelet sterility, pollen abortion, and impaired grain filling. Progress through conventional breeding has been constrained by the polygenic nature of drought tolerance and by strong genotype × environment (G × E) interactions. This review proposes a systems breeding strategy integrating five complementary approaches rice pangenomics, genome-wide association studies (GWAS), genomic selection (GS), high-throughput phenomics, and precision genome editing to strengthen drought resilience at the reproductive stage. Structural variants identified through pangenome analyses across diverse Oryza accessions have been implicated in abscisic acid (ABA) signalling, osmolyte biosynthesis, antioxidant defence, and root system architecture pathways central to reproductive-stage drought adaptation. Multi-omics-informed GWAS, combined with co-localisation of eQTLs and protein QTLs in drought-stressed reproductive tissues, highlights high-confidence candidate genes including OsNAC14, OsbZIP23, and DRO1 that help explain the physiological basis of water-deficit adaptation. Incorporating envirotyping data into GS models has been shown to improve predictive accuracy across diverse rainfed environments. Alongside marker-assisted selection, base editing and prime editing enable targeted allelic refinement with minimal off-target effects. We present a proposed tiered candidate prioritisation pipeline that advances loci supported by convergent genomic, transcriptomic, proteomic, and field-level evidence toward practical breeding deployment. Translating these discoveries into climate-resilient varieties will require FAIR data sharing, coordinated phenotyping networks, and multi-environment validation platforms linking genomic discovery to scalable breeding pipelines for drought-resistant, high-yielding rice in rainfed systems.
Generation time is the principal bottleneck constraining genetic gain in plant breeding. Speed breeding (SB) addresses this by simultaneously managing the photoperiod, light spectrum and intensity, temperature, CO2 concentration, mineral nutrition, growing substrate volume, and post-harvest seed dormancy, enabling four to seven generations per year in long-day cereals and legumes, and four to five generations in optimised short-day systems. This review evaluates SB against the conventional pedigree framework; synthesises validated environmental parameters and crop-specific protocols; and examines principal SB applications in hybridisation, genomic selection, disease-resistance screening, and allele introgression. A structured comparison of major review and protocol papers identifies broad consensus on core parameters alongside genuine divergence on far-red light supplementation. Critical evaluation addresses genotype-by-environment interaction, incomplete trait coverage, infrastructure costs, and unresolved questions on the biological integrity of rapidly advanced generations. The review further discusses new genomic techniques (NGTs), particularly CRISPR/Cas9-based gene editing, in the context of the EU’s June 2026 NGT regulation, under which Category 1 plants carrying only targeted endogenous modifications are exempt from GMO authorisation. When combined with SB, this regulatory shift can compress the interval from gene-editing event to registered variety from decades to a few years. Speed breeding, NGTs, and conventional field-based selection are most productively treated as complementary elements of a unified pipeline.
V. Mladenov, Rada Šućur, B. Banjac et al.· Plants· 0 citations
Genomic selection has become an important strategy in cassava breeding, enabling faster selection cycles and sustained genetic progress. Despite its widespread adoption, long-term evaluations integrating predictive performance, realized genetic gain, and genetic diversity remain scarce, particularly in clonally propagated crops. We present a comprehensive assessment of genomic selection outcomes in the Brazilian cassava breeding program across four recurrent selection cycles (C0 to C3) implemented between 2011 and 2024, using historical phenotypic and genomic data from 210 multi-environment trials. Predictive ability of genomic best linear unbiased prediction models ranged from low to moderate, depending on the trait’s genetic architecture and heritability. Prediction accuracies were highest in early cycles (C0 and C1) and showed modest declines in later cycles (C2 and C3). Root yield, shoot yield, plant height, starch content, and dry matter content exhibited stable predictive performance across cycles, with a gradual reduction in RMSE, indicating improved model calibration as training populations expanded. Regression analyses of genomic estimated breeding values revealed significant realized genetic gains for most yield-related traits. In contrast, dry matter content and starch content exhibited small, non-significant negative trends, consistent with known unfavorable genetic correlations with yield. Targeted reductions in plant architecture scores reflected deliberate selection for ideotypes suited to mechanized production systems. At the same time, analyses of genetic diversity revealed a slight decrease in observed heterozygosity, with higher values in the most advanced selection cycle. These results provide an integrated framework for monitoring predictive performance, realized genetic gain, and population genetic dynamics under long-term genomic selection. Collectively, they offer valuable insights into balancing short-term genetic improvement with long-term sustainability and support the development of strategies to optimize selection decisions, breeding planning, and population management in Brazilian cassava breeding programs.
G. M. de Freitas, Diana C. Solarte Certuche, J. Jannink et al.· bioRxiv· 0 citations
By combining genomic data with precision breeding techniques, researchers are developing crops that are better adapted to a growing population and a changing climate, positioning the integration of molecular breeding and bioinformatics as a central pillar of future global food security.
Muhammad Shahid Iqbal, Z. Sarfraz, Muhammad Mujahid et al.· Frontiers in Plant Science· 0 citations
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