Integration of crop modeling and sensing into molecular breeding for nutritional quality and stress tolerance - Jonathan Berlingeri; Abelina Fuentes; Earl Ranario; Heesup Yun; Ellen Y. Rim; Oscar Garrett; Alexander Howard; Mary-Francis LaPorte; Sassoum Lo; Duke Pauli; Jenna Hershberger; Mason Earles; et al. (Theoretical and Applied Genetics, 2025)
Integrating innovative technologies into plant breeding is critical to bolster food and nutritional security under biotic and abiotic stresses in changing climates. While breeding efforts have focused primarily on yield and stress tolerance, emerging evidence highlights the need to also prioritize nutritional quality. Advanced molecular breeding approaches have enhanced our ability to develop improved crop varieties and could be substantially informed by the routine integration of crop modeling and remote sensing technologies. This review article discusses the potential of combining crop modeling and sensing with molecular breeding to address the dual challenge of nutritional quality and stress tolerance. We provide overviews of stress response strategies, challenges in breeding for quality traits, and the use of environmental data in genomic prediction. We also describe the status of crop modeling and sensing technologies in grain legumes, rice, and leafy greens, alongside the status of -omics tools in these crops and the use of AI with directed evolution to identify novel resistance genes. We describe the pairwise and three-way integration of AI-enabled sensing and biophysically and empirically constrained crop modeling into breeding to enable prediction of phenotypic and breeding values and dissection of genotype-by-environment-by-management interactions with increasing fidelity, efficiency, and temporal/spatial resolution to inform selection decisions. This article highlights current initiatives and future trends that focus on leveraging these advancements to develop more climate-resilient and nutritionally dense crops, ultimately enhancing the effectiveness of molecular breeding.

Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software - José Crossa; Johannes W.R. Martini; Paolo Vitale; Paulino Pérez-Rodríguez; Germano Costa-Neto; Roberto Fritsche-Neto; Daniel Runcie; Jaime Cuevas; Fernando Toledo; H. Li; Pasquale De Vita; Guillermo Gerard; et al. (Trends in Plant Science, 2025)

Machine learning algorithms translate big data into predictive breeding accuracy - José Crossa; Osval A. Montesinos-Lopez; Germano Costa-Neto; Paolo Vitale; Johannes W.R. Martini; Daniel Runcie; Roberto Fritsche-Neto; Abelardo Montesinos-Lopez; Paulino Pérez-Rodríguez; Guillermo Gerard; Susanna Dreisigacker; Leonardo Crespo-Herrera; et al. (Trends in Plant Science, 2025)

High-dimensional multi-omics measured in controlled conditions are useful for maize platform and field trait predictions - Baber Ali; Bertrand Huguenin-Bizot; Maxime Laurent; François Chaumont; Laurie C. Maistriaux; Stéphane Nicolas; Hervé Duborjal; Claude Welcker; François Tardieu; Tristan Mary-Huard; Laurence Moreau; Alain Charcosset; et al. (Theoretical and Applied Genetics, 2024)

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations - Haixiao Hu; Malachy T. Campbell; Trevor H. Yeats; Xuying Zheng; Daniel E. Runcie; Giovanny Covarrubias-Pazaran; Corey Broeckling; Linxing Yao; Melanie Caffe-Treml; Lucı́a Gutiérrez; Kevin P. Smith; James Tanaka; et al. (Theoretical and Applied Genetics, 2021)
Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Genetics of gene expression responses to temperature stress in a sea urchin gene network - DANIEL E. RUNCIE; DAVID A. GARFIELD; COURTNEY C. BABBITT; JENNIFER A. WYGODA; SAYAN MUKHERJEE; GREGORY A. WRAY (Molecular Ecology, 2012)
Stress responses play an important role in shaping species distributions and robustness to climate change. We investigated how stress responses alter the contribution of additive genetic variation to gene expression during development of the purple sea urchin, Strongylocentrotus purpuratus , under increased temperatures that model realistic climate change scenarios. We first measured gene expression responses in the embryos by RNA‐seq to characterize molecular signatures of mild, chronic temperature stress in an unbiased manner. We found that an increase from 12 to 18 °C caused widespread alterations in gene expression including in genes involved in protein folding, RNA processing and development. To understand the quantitative genetic architecture of this response, we then focused on a well‐characterized gene network involved in endomesoderm and ectoderm specification. Using a breeding design with wild‐caught individuals, we measured genetic and gene–environment interaction effects on 72 genes within this network. We found genetic or maternal effects in 33 of these genes and that the genetic effects were correlated in the network. Fourteen network genes also responded to higher temperatures, but we found no significant genotype–environment interactions in any of the genes. This absence may be owing to an effective buffering of the temperature perturbations within the network. In support of this hypothesis, perturbations to regulatory genes did not affect the expression of the genes that they regulate. Together, these results provide novel insights into the relationship between environmental change and developmental evolution and suggest that climate change may not expose large amounts of cryptic genetic variation to selection in this species.

Sequence signatures of a recent chromosomal rearrangement in Drosophila mojavensis - Daniel E. Runcie; Mohamed A. F. Noor (Genetica, 2009)