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

Leveraging molecular tools for cereal breeding: Progress, limitations, and prospects

Rice, wheat, and maize cereals are the major foundation of global food security. However, climate change makes it more challenging to achieve high crop yield, the challenge occurs due to improper management of cereal diseases and pests, and limitations of traditional breeding processes. This study aimed to update the process, limitations, and prospects of molecular tools for cereal breeding, and to explore the significance of marker-assisted selection, marker-assisted backcrossing, gene pyramiding, genomic selection, and modern breeding for improving yield, stress tolerance, and grain quality of cereals. Based on recent studies, we have explored the advances and applications of high-throughput genotyping platforms like the single nucleotide polymorphism (SNP) array and genotyping by sequencing technology in cereals. In this study, we found several limitations, such as a low number of studies with large amounts of data, genotype-environment interactions, lack of study findings at the field level, cost implications, and integration of complex multi-omics data. This study further reveals that many crucial agronomic traits are polygenic in their mode of inheritance, and the hidden genetic links make selection weak and uncertain. However, the application of molecular tools such as CRISPR/Cas genome editing, speed breeding, pan-genomics, artificial intelligence, and high-throughput phenomics provides sustainable solutions to these challenges in cereal improvement. The application of these modern breeding tools, combined with microbiome-assisted breeding and agricultural technologies in precision cereal breeding, opens new opportunities for enhancing yield and climate-smart, sustainable cereal production for global food and nutrition security.

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

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.

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

AI-integrated digital breeding for crop improvement

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

Bioinformatics in crop research: using genomic data for crop improvement

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.

Enhancing genetic gains with high-throughput phenotyping and genomic selection: approaches in wheat and maize breeding

This work is composed by four chapters: Chapter 1 consists in a pioneer work where we attempted to provide the scientific community with relevant insights on multi- spectral high-throughput phenotyping in tropical wheat breeding aiming at designing indirect selection strategies. The main goals were to determine the best stages to acquire aerial images to extract vegetation indices, investigate key genetic parameter (such as heritability and accuracy) in different stages, understand the relationship between traits and evaluate the efficiency of indirect selection using aerial-based high-throughput phenotyping. Chapter 2 is an extension of Chapter 1. While in Chapter 1 we focused on single-time-point analysis, in Chapter 2 we leveraged linear mixed models to analyze high-throughput phenotyping data considering its longitudinal aspect. We successfully predicted single genotypic values from time- series-derived data and found relevant insights on the optimal number of flights required to acquire high-quality multi-spectral aerial information. We also decomposed the coefficients of correlations between spectral and agronomic data into direct and indirect effects, and used that information to design an indirect selection strategy. In Chapter 3, instead of using the multi-spectral variables to design selection strategies, we focused on implementing a machine learning pipeline to predict untested wheat genotypes. In short, we used spectral bands and vegetation indices as inputs to predict agronomic variables using machine learning models considering a stage-wise approach. Finally, Chapter 4 shows the application of digital phenotyping and genomic selection to predict temperate inbred lines. We integrated environmental information, novel and traditional phenotyping methods and whole-genome resequencing information to predict the performance of a subset of maize diversity panel for stalk lodging resistance. We demonstrate the usefulness of novel phenotyping platforms to access lodging resistance and show how to leverage information between correlated environments to boost predictive-ability. This thesis is composed by several works, where we attempted to integrate the state of art of plant breeding and fundamental concepts of quantitative genetics and statistics to solve real problems often faced by plant breeders. We hope this work serve as a guide for the implementation of future pipelines aiming to improve genetic gains in plant breeding programs of economically important crops. Keywords: Artificial intelligence; Digital phenotyping; Genomic prediction; Genotype- by-environment interaction; Linear mixed models; Triticum aestivum L.; Zea mays.