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J. Crossa

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Open access Aug 2026

A Public Mid-Density Genotyping Platform for Genomic Selection in Wheat

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.

Susanna Dreisigacker, Pacome Judon, Leonardo Abdiel Crespo Herrera et al. · 0 citations
Review Open access Aug 2026

Advances in predictive breeding for wheat: concepts, methods, and applications

Wheat is one of the world’s main crops. Its improvement is pivotal given the threat of climate change and the growing population. However, enhancing breeding efficiency and improving wheat are challenging due to strong genotype-by-environment (G×E) interactions and the biological complexity underlying the wheat genome and key agronomic traits. In this context, predictive frameworks and data-driven approaches can offer new strategies to address these challenges. This article provides a comprehensive review of the latest developments in wheat breeding, highlighting emerging predictive frameworks and their contributions to modern breeding pipelines. First, we report on genomic selection (GS) applications, emphasizing GS’s ability to improve complex traits by shortening the breeding cycle and increasing selection accuracy. We then describe the applications of phenomics in wheat breeding, including both ground- and unmanned aerial vehicle (UAVs)-based systems. We also discuss the potential for implementing multi-omics strategies to improve complex wheat traits. We debate how predictive breeding frameworks can assist in identifying the best parents and crosses in wheat breeding. Finally, we presented the latest panorama of software for predictive breeding and its integration with other technologies. This review reports recent advances demonstrating how predictive frameworks are reshaping wheat breeding methods, highlighting current progress and outlining future opportunities to accelerate genetic gain in wheat improvement.

P. Vitale, Karim Ammar, Flávio Breseghello et al. · 0 citations