Aug 2026· Crop Breeding, Genetics and Genomics· 0 citations· 30 references
TL;DR
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.
Abstract
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.
Abstract Genomic selection (GS) is a powerful tool for accelerating genetic gain in potato (Solanum tuberosum L.) breeding, particularly for complex traits. In this study, three practical aspects of GS implementation in a potato breeding program were examined. First, the predictive ability of GS models was evaluated for three key traits (total yield, marketable yield, and specific gravity) using two elite potato populations with shared ancestry, tested across seven location‐year environments. Two cross‐validation strategies were used to reflect practical breeding scenarios: predicting unphenotyped lines in known environments and predicting clonal performance in unknown environments. Four models were evaluated, two of which included genotype‐by‐environment interactions. Tuber specific gravity showed higher and more consistent prediction accuracy across environments, supporting the evidence that it is a more stable trait. Second, the impact of genotyping platforms and marker density on GS performance were examined, as the two populations were genotyped using two different targeted sequencing platforms: Flex‐seq (22K loci) and DArTag (4K loci), sharing ∼4K common loci, that allowed direct comparison. Prediction accuracies were comparable across platforms, indicating that both are suitable for GS implementation, with the choice depending on breeding goals, cost, and throughput considerations. Finally, the long‐term impact of GS on genetic gain was assessed through stochastic simulation of a 30‐year breeding pipeline, comparing conventional phenotypic selection with GS‐assisted selection scenarios. GS scenarios achieved higher long‐term genetic gains, though practical deployment should consider both cost and breeding objectives. Our findings for the three aspects of this study support the integration of GS into potato breeding programs, while highlighting key considerations for its effective implementation.
R. Dhakal, M. A. Peixoto, Leo Hoffmann et al.· The Plant Genome· 0 citations
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
The 10K SNP chip offers a cost-effective alternative to higher-density arrays, enabling its integration into genomic selection, marker-assisted breeding, and diversity monitoring, ultimately supporting accelerated genetic gain and the delivery of improved varieties to farmers.
Hyeonah Shim, Hai-Lin Zhang, Thomas Groß et al.· Frontiers in Plant Science· 0 citations
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.
Ha Duc Chu, T. Q. Nguyen, Loc Van Nguyen et al.· Genes· 1 citation
Goats (Capra hircus) are among the world’s most important livestock, providing milk, meat, and fiber across diverse agro-ecological zones. Traditional breeding relying on pedigree-based estimated breeding values (EBVs) has driven steady genetic progress but is constrained by long generation intervals and limited accuracy for sex-limited or difficult-to-measure traits. High-throughput single nucleotide polymorphism (SNP) chips and genomic selection (GS) have transformed goat breeding by enabling early, accurate selection independent of phenotypic records. This review synthesizes the development of goat SNP chip platforms from the foundational 52 K GoatSNP50 BeadChip through high-density solid-phase arrays and low-cost liquid-phase capture panels, with emphasis on their relative performance, cost-effectiveness, imputation potential, and suitability for different breeding systems. In addition to genomic selection (GS), genome-wide association studies (GWAS), and genetic diversity assessment, we also discuss candidate-gene selection and marker-assisted selection (MAS) as practical intermediate approaches that remain relevant in many goat breeding programs. GS has achieved genomic estimated breeding value (GEBV) prediction accuracies of 0.35–0.79 for key production traits across multiple countries and breeds. GWAS has identified candidate genes for milk composition (DGAT1, CSN1S1), growth (PLAG1, HMGA2), reproduction (BMPR1B, GDF9), and fiber quality (KRT, KRTAP families). We compare GS with traditional BLUP-based approaches, assess economic benefits, and discuss key challenges including reference population construction, genotype imputation, inbreeding management via Optimum Contribution Selection (OCS), and multi-omics integration. Future directions include customized chip design, AI-assisted genomic prediction, climate adaptation breeding, and CRISPR/Cas9 gene editing for precision improvement.
Ting-Chieh Kang, Hisn-Hung Lin, Kai-Fei Tseng et al.· Frontiers in Veterinary Scie...· 0 citations
In this study, a dataset derived from a natural rice population was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework, which provided valuable insights into modeling genotype-by-environment interactions.
Jinhan Zhang, Wei-Jie Tang, Hong-Wei Ma et al.· Theoretical and Applied Gene...· 0 citations
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