VISION

Website: The Rocinante Lab

We are pioneering the next generation of plant breeding by integrating cutting-edge genomics, advanced computational methods, and innovative breeding strategies. Our mission is to accelerate the development of climate-resilient, high-performing crop varieties that meet the challenges of feeding a growing global population while maintaining agricultural sustainability.

 

The Rocinante Lab operates at the intersection of molecular genomics, statistical modeling, machine learning, and practical breeding applications. We don't just predict the future of agriculture, we're actively building it through data-driven breeding strategies that maximize genetic gain while preserving diversity for future generations.

 

Research Philosophy

Modern plant breeding faces a critical challenge, achieving rapid genetic improvement while maintaining the diversity needed for long-term resilience. Our research addresses this through:

 

  • Predictive Breeding: Leveraging genomic selection and machine learning to identify superior varieties before field testing
  • Optimal Resource Allocation: Designing efficient breeding strategies that maximize impact with limited resources
  • Knowledge-Driven Approaches: Integrating host-pathogen genomics, environmental data, and historical breeding records
  • Open Science: Developing open-source tools and methodologies accessible to breeding programs worldwide

We believe the future of breeding lies in intelligent decision-making powered by comprehensive data integration, from genome sequences to field performance across environments. 

 

Current Research Projects

Combining Genomic Approaches to Study Host-Pathogen Relationships in Wheat and Septoria

 

Spanish Grant CNS2024-154812 (2025-2027) | €144K

 

Building on previously developed genomic tools, this project aims to revolutionize our understanding of wheat-Septoria tritici blotch interactions. We're validating predictive models through controlled greenhouse challenges of 200 wheat lines with 100 pathogen isolates, then extending to field conditions with fresh Spanish isolates. Through genome-wide association studies, we're pinpointing resistance loci and developing forecasting tools for pathogen aggressiveness. Our goal is rapid knowledge transfer to breeders and farmers through workshops, training sessions, and publications.

 

Impact: Enabling breeders to develop durable disease resistance by understanding the genetic architecture of both host and pathogen. 

 

 

Breed-E-Omics: Genomic Approach for Sustainable Spelt Agriculture

European Grant DADR-2024-029390 (2025-2028) | €96K

 

This project addresses the growing demand for sustainable, high-value spelt wheat. Through comprehensive stakeholder engagement with farmers and agri-food industry, we've identified three critical needs: low-input high-yield varieties for farmers, nutritious organic grains for food processors, and promotion of soil health and biodiversity. We're conducting varietal assessments, genotype-by-environment studies, and genome-wide association analyses to develop resilient spelt strains that deliver economic, environmental, and social benefits.

 

Vision: Spelt can represents the future of sustainable cereal production, combining ancient nutritional benefits with modern genomic breeding efficiency.

 

 

Genomic-Assisted Breeding for Sustainable Agriculture: A Benchmark Approach

Spanish Grant PID2021-123718OB-I00 (2022-2025) | €127K

In collaboration with Agriculture and Agri-Food Canada (AAFC) at the Swift Current Research and Development Center, we're advancing genomic selection methodologies in public wheat breeding. This partnership provides real-world validation of our approaches while contributing to resilient, high-quality wheat varieties for future agricultural challenges.

 
Key focus areas:
  • Training set optimization to enhance prediction accuracy
  • Advanced modeling strategies integrating GWAS insights
  • Non-additive genetic effects for improved breeding efficiency
  • End-use quality traits ensuring varieties meet diverse stakeholder needs

 

Innovation: Demonstrating how public breeding programs can leverage genomic selection for accelerated genetic gain.

 

 

Optimal Genomic Mating (OGM): Framework for Improved Mate Allocation (2025-2029)

A fundamental challenge in breeding is balancing genetic gain against diversity loss. How do we achieve maximum improvement while preserving variation for future progress? Our OGM research leverages stochastic simulations and real-world implementations to support breeders in selecting superior crosses. This approach accelerates development of resilient, high-yielding crops while maintaining long-term genetic diversity—essential for sustained breeding progress.

 

Paradigm shift: Moving from selecting individual plants to optimizing entire mating strategies for multi-generational impact.

 

 

Climate-Resilient Blueberry Varieties through Advanced Genomic Selection

Collaboration with Horticulture Company (2023-2026)

 

This innovative partnership applies cutting-edge genomic selection to fast-track blueberry cultivar development for water stress and warm climates. Over three breeding cycles, we're phenotyping 200-400 seedlings annually for key traits (phenology, vigor, fruit quality) while conducting high-throughput genotyping. Our iteratively refined predictive models enable early identification of superior genotypes, drastically reducing field trial requirements and accelerating breeding cycles.

 

Innovation: Demonstrating genomic selection's power beyond traditional field crops—expanding to high-value horticultural species.

 

 

Genomic Selection Applied to Industrial Sunflower Breeding  (2022-2026)

 

Partnering with Syngenta's sunflower breeding program, we're optimizing genomic methodologies for industrial-scale application. This project addresses practical challenges in implementing genomic selection:

 

  • Historical data optimization for training genomic prediction models
  • Enhanced field trial designs for improved efficiency
  • Advanced modeling incorporating machine learning, spatial analysis, and two-stage approaches

 

By integrating publicly available data and simulations, we're developing broadly applicable methods that benefit breeding programs globally, demonstrating how genomic selection can drive faster genetic improvement in one of the world's most important oilseed crops.

 

 

Previous Projects (last 5 years project)
WheatRes: Identifying New Sources of Durable Resistance
 
Spanish Grant PLEC2021-007930 (2021-2024) | €190K
Fungal diseases like Septoria Tritici Blotch pose major threats to durum wheat production. This project leverages cutting-edge sequencing technologies to decode host-pathogen interactions contributing to horizontal resistance. By understanding how wheat genes interact with fungal pathogens at the molecular level, we're enabling more precise breeding strategies for sustainable disease management.
 
Future direction: Moving beyond single-gene resistance to durable, multi-layered defense mechanisms.
 
 
Next Generation Variety Testing (InnoVar - H2020)
 
European Grant (2019-2024) | €319K | Deputy WP2 Leader
The InnoVar project is reimagining variety testing for European agriculture. We're developing the concept of high-performance low-risk (HPLR) varieties within Value for Cultivation and Use (VCU) testing frameworks. By exploiting high-throughput genomics, advanced imaging, and machine learning, we're creating tools that help countries and breeders focus on feeding future generations sustainably.
 
 
 
The Future of Breeding: Our Perspective
The next decade of plant breeding will be defined by:
 
  1. Integration of Multi-Omics Data: Combining genomics, transcriptomics, metabolomics, and phenomics for comprehensive understanding of trait architecture
  2. Predictive Breeding at Scale: Moving from genomic prediction to full breeding program optimization, including crossing decisions, resource allocation, and multi-trait selection
  3. Host-Pathogen Co-Evolution: Understanding pathogen populations alongside host genetics to develop truly durable resistance strategies
  4. Climate-Smart Varieties: Breeding specifically for future climate scenarios, not just current conditions
  5. Democratization of Genomic Tools: Making advanced breeding methodologies accessible to small breeding programs and developing regions through open-source software and collaborative research
  6. Sustainable Intensification: Achieving higher yields with lower environmental impact through precise genetic improvement
 
Our lab is actively contributing to each of these frontiers, developing both theoretical frameworks and practical tools that advance global breeding efforts.
 
 
Open-Source Contributions
We believe in making our research accessible. Our R packages include:
 
  • MateR (2025): Advanced mating optimization framework
  • GEmetrics (2024): Genotype-by-environment interaction metrics
  • TrainSel (2021): Training population selection for genomic prediction
  • CovCombR (2020): Combining partially overlapping multi-omics datasets
  • GenomicMating (2018): Implementation of optimal mating strategies
 
Join Our Team
We're always looking for motivated students and postdocs passionate about using genomics and data science to solve real agricultural challenges. If you're interested in combining cutting-edge computational methods with practical breeding applications, get in touch.
 

 

Domínguez Rondón, Alejandro - PhD Student

Isidro Sánchez, Julio - Researcher CSIC

Martín Menor de Gaspar, Juan - PhD Student

Metwally, Seifelden - PhD Student

Vegas Lorenzo, Inés - Technician

      • DADR-2024-029390. Approche génomique d'un projet Epeautre pour une agriculture durable face au réchauffement climatique. PEI Haute-de- France 2025-2028. European Union - Feder-Région Hauts-de-France, FEDER/EU.  PI: Julio Isidro Sánchez.

      • CNS2024-154812. Combining genomic approaches to study host-pathogen relationships in wheat and Septoria 2025 - 2027. Ministerio de Ciencia, Innovación y Universidades (MICIUI), Spain PI: Julio Isidro Sánchez.




      • IDI-20250085. Climate-Resilient Blueberry Varieties through Advanced Genomic Selection. Collaboration with Horticulture Company. 2023-2026. Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (MICIU/AEI), Spain and Centro para el Desarrollo Tecnológico y la Innovación (CDTI). PI: Julio Isidro Sánchez.

           

      • PID2019-104518RB-100. Genomic Assisted breeding for SUStainable agriculture: A benchmark approach. 2022-2026. Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (MICIU/AEI), Spain and FEDER/EU.   IP: Julio Isidro Sánchez.




      • PLEC2023-010225. Application of Genomic assisted breeding on Olive tree breeding (2025-2029). PROLIVE (PLEC2023-010225). Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación (MICIU/AEI), Spain. Workgroup: Julio Isidro Sánchez.




      • Grant agreement No. 818144. INNOVAR: Next Generation Variety Testing For Improved Cropping On European Farmland (H2020) (2020-2025). European Union’s Horizon 2020 research and innovation program (EU).  IP: Julio Isidro Sánchez.




      • PREDOC-21-GCCS6G-62-2B65BK. Programa propio de I+D+I de la UPM. Machine learning approaches applied to genomic assisted breeding. 2021-2024. Universidad Politécnica de Madrid (UPM), Spain. IP: Julio Isidro Sánchez.




      • HealthyOats. 2022-2024. European Regional Development Fund (EU). https://www.irelandwales.eu/projects/healthy-oats Coordinator: Julio Isidro Sánchez.



      • Knowledge-driven genomic predictions for sustainable disease resistance in wheat. 2022-2024. Cofund on Sustainable Crop Production . IP: Julio Isidro Sánchez.




      • PLEC2021-00793. WheatRes. Identificación de nuevas fuentes de resistencia horizontal a septoria y roya en trigo duro (2021-2024). IP: Julio Isidro Sánchez.




Private Funding

    • Programa UPM-Syngenta. Genomic assisted breeding applied to Syngenta sunflower breeding program. IP: Julio Isidro Sánchez.



Gaspar, J.M. de, Cuthbert, R.D., Yuefeng, R., Knox, R., Fu, B., Wang, K., Sangah, J.S., Berraies, S., Meyer, B., Bokore, F.E., Sánchez, J.I ✉. y 2026. Genetic control of wheat flour end-use quality and rheology by genome-wide association studies. The Plant Genome 19, e70236. DOI: 10.1002/tpg2.70236

Copeland, C., Isidro y Sánchez, J., Fanelli, H., Doohan, F.M. 2026. Phenotypic and genetic resistance to Septoria blotch disease in European wheat varieties. The Plant Genome 19, e70237. DOI: 10.1002/tpg2.70237

Fernández-González, J., Metwally, S.M., Isidro y Sánchez, J. 2026. MateR: a novel genomic mating framework. Genetics iyag013. DOI: 10.1093/genetics/iyag013

Avni, R., Kamal, N., Bitz, L., Jellen, E.N., Bekele, W.A., Angessa, T.T., Auvinen, P., Bitz, O., Boyle, B., Canales, F.J., Carlson, C.H., Chapman, B., Chawla, H.S., Chen, Y., Copetti, D., Correia de Lemos, S., Dang, V., Eichten, S.R., Klos, K.E., Fenn, A.M., Fiebig, A., Fu, Y.-B., Gundlach, H., Gupta, R., Haberer, G., He, T., Herrmann, M.H., Himmelbach, A., Howarth, C.J., Hu, H., Isidro y Sánchez, J., Itaya, A., Jannink, J.-L., Jia, Y., Kaur, R., Knauft, M., Langdon, T., Lux, T., Marmon, S., Marosi, V., Mayer, K.F.X., Michel, S., Nandety, R.S., Nilsen, K.T., Paczos-Grzęda, E., Pasha, A., Prats, E., Provart, N.J., Ravagnani, A., Reid, R.W., Schlueter, J.A., Schulman, A.H., Sen, T.Z., Singh, J., Singh, M., Sirijovski, N., Stein, N., Studer, B., Viitala, S., Vronces, S., Walkowiak, S., Wang, P., Waters, A.J., Wight, C.P., Yan, W., Yao, E., Zhang, X.-Q., Zhou, G., Zhou, Z., Tinker, N.A., Fiedler, J.D., Li, C., Maughan, P.J., Spannagl, M., Mascher, M. 2025. A pangenome and pantranscriptome of hexaploid oat. Nature 1–9. DOI: 10.1038/s41586-025-09676-7


Bekele, W.A., Avni, R., Birkett, C.L., Itaya, A., Wight, C.P., Bellavance, J., Brodführer, S., Canales, F.J., Carlson, C.H., Fiebig, A., Li, Y., Michel, S., Nandety, R.S., Waring, D.J., Arbelaez, J.D., Beattie, A.D., Caffe, M., del Blanco, I.A., Fiedler, J.D., Gupta, R., Gutierrez, L., Harris, J.C., Harrison, S.A., Herrmann, M.H., Huang, Y.-F., Isidro y Sanchez, J., McMullen, M.S., Mitchell Fetch, J.W., Nilsen, K.T., Parkin, I.A.P., Peng, Y., Smith, K.P., Sutton, T., Yan, W., Zwer, P., Diederichsen, A., Esvelt Klos, K., Fu, Y.-B., Howarth, C.J., Jannink, J.-L., Jellen, E.N., Langdon, T., Maughan, P.J., Paczos-Grzeda, E., Prats, E., Sen, T.Z., Mascher, M., Tinker, N.A. 2025. Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements. Nature Communications 16, 9486. DOI: 10.1038/s41467-025-57895-3

Menor de Gaspar, J.✉, Domínguez Rondón, A., García-Abadillo, J., Knox, R., Bokore, F.E., Boyle, K., Ammar, K., Huerta-Espino, J., Berraies, S., Meyer, B., Zhang, W., Cuthbert, R.D., Pierre, F., Ruan, Y.✉, Isidro y Sánchez, J.✉ 2025. Mapping novel yellow and leaf rust loci and predicting resistance in cross derived Canadian durum wheat. The Plant Genome 18, e70124. DOI: 10.1002/tpg2.70124

Fernández-González, J., Isidro y Sánchez, J. 2025. Maximizing the accuracy of genetic variance estimation and using a novel generalized effective sample size to improve simulations. Theoretical and Applied Genetics 138, 78. DOI: 10.1007/s00122-025-04861-8

Sangha, J.S., Wang, W., Knox, R., Ruan, Y., Cuthbert, R.D., Isidro-Sánchez, J., Li, L., He, Y., DePauw, R., Singh, A., Cutler, A., Wang, H., Selvaraj, G. 2025. Phenotypic plasticity of bread wheat contributes to yield reliability under heat and drought stress. PLOS ONE 20, e0312122. DOI: 10.1371/journal.pone.0312122

Fernández-González, J., Isidro y Sánchez, J. 2025. Optimizing fully-efficient two-stage models for genomic selection using open-source software. Plant Methods 21, 9. DOI: 10.1186/s13007-024-01318-9

Viviani, A., Haile, J.K., Fernando, W.G.D., Ceoloni, C., Kuzmanović, L., Lhamo, D., Gu, Y.-Q., Xu, S.S., Cai, X., Buerstmayr, H., Elias, E.M., Confortini, A., Bozzoli, M., Brar, G.S., Ruan, Y., Berraies, S., Hamada, W., Oufensou, S., Jayawardana, M., Walkowiak, S., Bourras, S., Dayarathne, M., Isidro y Sánchez, J., Doohan, F., Gadaleta, A., Marcotuli, I., He, X., Singh, P.K., Dreisigacker, S., Ammar, K., Klymiuk, V., Pozniak, C.J., Tuberosa, R., Maccaferri, M., Steiner, B., Mastrangelo, A.M., Cattivelli, L. 2025. Priority actions for Fusarium head blight resistance in durum wheat: Insights from the wheat initiative. The Plant Genome 18, e20539. DOI: 10.1002/tpg2.20539

Garcia-Abadillo, J., Adunola, P., Aguilar, F.S., Trujillo-Montenegro, J.H., Riascos, J.J., Persa, R., Isidro y Sanchez, J., Jarquín, D. 2024. Sparse testing designs for optimizing predictive ability in sugarcane populations. Frontiers in Plant Science 15. DOI: 10.3389/fpls.2024.1400000

Carvalho, H.F., Rio, S., García-Abadillo, J., Isidro y Sánchez, J. 2024. Revisiting superiority and stability metrics of cultivar performances using genomic data: derivations of new estimators. Plant Methods 20, 85. DOI: 10.1186/s13007-024-01207-1

López-Fernández, M., Chozas, A., Benavente, E., Alonso-Rueda, E., Isidro y Sánchez, J., Pascual, L., Giraldo, P. 2024. Genome wide association mapping of end-use gluten properties in bread wheat landraces (Triticum aestivum L.). Journal of Cereal Science 118, 103956. DOI: 10.1016/j.jcs.2024.103956

Fernández-González, J., Haquin, B., Combes, E., Bernard, K., Allard, A., Isidro y Sánchez, J. 2024. Maximizing efficiency in sunflower breeding through historical data optimization. Plant Methods 20, 42. DOI: 10.1186/s13007-024-01151-0

Alemu, A., Åstrand, J., Montesinos-López, O.A., Isidro y Sánchez, J., Fernández-Gónzalez, J., Tadesse, W., Vetukuri, R.R., Carlsson, A.S., Ceplitis, A., Crossa, J., Ortiz, R., Chawade, A. 2024. Genomic selection in plant breeding: key factors shaping two decades of progress. Molecular Plant. DOI: 10.1016/j.molp.2024.03.007

García-Abadillo, J., Barba, P., Carvalho, T., Sosa-Zuniga, V., Lozano, R., Carvalho, H.F., Garcia-Rojas, M., Salazar, E., y Sánchez, J.I. 2024. Dissecting the Complex Genetic Basis of Pre and Post-harvest Traits in Vitis vinifera L. using Genome-Wide Association Studies. Horticulture Research uhad283. DOI: 10.1093/hr/uhad283

Fernández-González, J., Akdemir, D., Isidro y Sánchez, J. 2023. A comparison of methods for training population optimization in genomic selection. Theoretical and Applied Genetics 136, 30. DOI: 10.1007/s00122-023-04265-6

Akdemir, D., Somo, M., Isidro-Sanchéz, J. 2023. An Expectation-Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices. Axioms 12, 161. DOI: 10.3390/axioms12020161

Garcia-Abadillo, J., Morales, L., Buerstmayr, H., Michel, S., Lillemo, M., Holzapfel, J., Hartl, L., Akdemir, D., Carvalho, H.F., Isidro-Sánchez, J. 2023. Alternative scoring methods of fusarium head blight resistance for genomic assisted breeding. Frontiers in Plant Science 13. DOI: 10.3389/fpls.2022.1057914

Shahinnia, F., Geyer, M., Schürmann, F., Rudolphi, S., Holzapfel, J., Kempf, H., Stadlmeier, M., Löschenberger, F., Morales, L., Buerstmayr, H., Isidro y Sánchez, J., Akdemir, D., Mohler, V., Lillemo, M., Hartl, L. 2022. Genome-wide association study and genomic prediction of resistance to stripe rust in current Central and Northern European winter wheat germplasm. Theoretical and Applied Genetics. DOI: 10.1007/s00122-022-04202-z

Rio, S., Akdemir, D., Carvalho, T., Isidro y Sánchez, J. 2021. Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials. Theoretical and Applied Genetics. DOI: 10.1007/s00122-021-03972-2

Isidro y Sánchez, J., Akdemir, D. 2021. Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview. Frontiers in Plant Science 12, 1889. DOI: 10.3389/fpls.2021.715910

Rio, S., Gallego-Sánchez, L., Montilla-Bascón, G., Canales, F.J., Isidro y Sánchez, J., Prats, E. 2021. Genomic prediction and training set optimization in a structured Mediterranean oat population. Theoretical and Applied Genetics. DOI: 10.1007/s00122-021-03916-w

Akdemir, D., Rio, S., Isidro y Sánchez, J. 2021. TrainSel: An R Package for Selection of Training Populations. Frontiers in Genetics 12. DOI: 10.3389/fgene.2021.655287

Smári Hilmarsson, H., Rio, S., Isidro y Sánchez, J. 2021. Genotype by Environment Interaction Analysis of Agronomic Spring Barley Traits in Iceland Using AMMI, Factorial Regression Model and Linear Mixed Model. Agronomy 11, 499. DOI: 10.3390/agronomy11030499

Akdemir, D., Knox, R., Isidro y Sánchez, J. 2020. Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices. Frontiers in Plant Science 11, 947. DOI: 10.3389/fpls.2020.00947

Isidro‐Sánchez, J., Cusack, K.D., Verheecke‐Vaessen, C., Kahla, A., Bekele, W., Doohan, F., Magan, N., Medina, A. 2020. Genome-wide association mapping of Fusarium langsethiae infection and mycotoxin accumulation in oat (Avena sativa L.). The Plant Genome e20023. DOI: 10.1002/tpg2.20023

Isidro-Sánchez, J., Prats, E., Howarth, C., Langdon, T., Montilla-Bascón, G. 2020. Genomic Approaches for Climate Resilience Breeding in Oats, in: Kole, C. (Ed.), Genomic Designing of Climate-Smart Cereal Crops. Springer International Publishing, Cham, pp. 133–169. DOI: 10.1007/978-3-319-93381-8_4

Akdemir, D., Isidro-Sánchez, J. 2019. Design of training populations for selective phenotyping in genomic prediction. Scientific Reports 9, 1446. DOI: 10.1038/s41598-018-38081-6

Akdemir, D., Beavis, W., Fritsche-Neto, R., Singh, A.K., Isidro-Sánchez, J. 2019. Multi-objective optimized genomic breeding strategies for sustainable food improvement. Heredity 122, 672–683. DOI: 10.1038/s41437-018-0147-1