Optimizing phenotypic data analysis strategies to enhance genomic prediction in winter wheat breeding

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.

Integrating genomic selection into potato breeding: A comparison of genotyping platforms and cross‐environmental predictions

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.

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice (Oryza sativa) Improvement

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.

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.

Assessment of genomic prediction and genetic gain in multi‑population half-sib families in the perennial grass crop intermediate wheatgrass

The University of Minnesota has been domesticating the perennial forage intermediate wheatgrass (IWG) since 2011 using a combination of conventional methods and modern breeding tools such as genomic selection. Globally, most IWG selection nurseries are spaced-planted individuals of several hundred genotypes whereas commercial fields established for grain production are row-planted panmictic populations. This study evaluated genomic prediction models and estimated genetic gain in yield and agronomic performance of row-planted IWG half-sib families assessed over 3 years and 2 locations, Lamberton and St. Paul, MN, USA. The strongest trait correlation was negative (r = −0.49) between 2023 St. Paul height and 2022 St. Paul seed size. The three St. Paul environments were more similar for plant height and seed size and so were the Lamberton environments yet no specific trend was observed for grain yield. Evaluation of different univariate and multivariate genomic prediction models showed that multivariate models outperformed the best univariate models by 23 percentage points, yet no single multivariate model was the best predictor of all traits. Cross-environment predictions were the best among St. Paul environments and no single environment was the best predictor of the remaining environments. Genetic gain estimates indicated a 20 kg ha-1 increase in grain yield and 3 cm reduction in plant height per breeding cycle. While no single model predicted all traits with high accuracy, results obtained in this study suggest that evaluating IWG sibs in row plots followed by genomic trait predictions could lead to desired breeding progress for desired traits.