GENOMIC ASSISTED BREEDING

Group leader: Julio Isidro Sánchez - Associate Professor
This email address is being protected from spambots. You need JavaScript enabled to view it.  910679208 (Office 131 )   910679209 (Lab S54)

Lab Web Page: https://therocinante-lab.github.io 


Personnel:


 

Website: The Rocinante Lab

 

Genomic assisted breeding

 

Our research integrates cutting-edge technologies across molecular genomics, phenomics, physiology, pathology, statistics, and breeding to develop innovative strategies for enhancing crop improvement. We focus on genomic prediction and selection, association mapping, and allelic diversity characterization, driving the development of superior, more resilient crop varieties. More info can be found at https://therocinante-lab.github.io.

 

Current research projects

Genomic Assisted breeding for SUStainable agriculture: A benchmark approach. (Proyectos de Generación de Conocimiento: PID2021-123718OB-I00)

In collaboration with Agriculture and Agri-Food Canada (AAFC) at the Swift Current Research and Development Center (SCRDC), we are advancing genomic selection (GS) methodologies in public wheat breeding. By integrating phenotypic and genotypic data from active breeding trials, we seek to accelerate genetic gain in wheat varieties with superior yield, disease resistance, and quality traits.

 

Our research focuses on: 

  1. Optimizing training sets to enhance GS model accuracy.
  2. Applying advanced modeling strategies, including GWAS, to uncover the genetic basis of key agronomic traits.
  3. Exploring non-additive genetic effects to improve breeding efficiency.

 

Beyond improving agronomic performance, this project also emphasizes end-use quality traits, ensuring that future wheat cultivars meet the demands of farmers, processors, and consumers alike. Our partnership with the Canadian public breeding program not only provides a robust testing ground for GS methodologies but also contributes to the development of resilient, high-quality wheat varieties that can withstand future agricultural challenges.


 



WheatRes: Identifying New Sources of Durable Resistance to Septoria and Rust in Durum Wheat (PLEC2021-007930)

Durum wheat is a key crop for global food production, yet fungal diseases like Septoria Tritici Blotch (STB) pose a major threat to yields and grain quality. Enhancing resistance to these pathogens is essential for developing more resilient and sustainable wheat varieties. The WheatRes project is dedicated to uncovering new sources of horizontal resistance by leveraging advanced genomic analysis. Using cutting-edge sequencing technologies, we aim to decode the host-pathogen interactions that contribute to durable resistance against fungal infections.

By deepening our understanding of how wheat genes interact with fungal pathogens, this research will enable more precise breeding strategies, ultimately leading to improved disease resistance and a more sustainable wheat supply.


 

 

Genomic assisted breeding applied to Syngenta sunflower breeding program

Genomic prediction is revolutionizing plant breeding, yet its practical implementation still requires refinement. This project aims to optimize breeding strategies by developing innovative genomic methodologies tailored for industrial-scale application.

Our collaboration with Syngenta’s sunflower breeding program allows us to test and validate these methodologies using real-world data, ensuring scalability and commercial viability.


Key objectives include:

  1. Optimizing historical data usage to train genomic prediction models effectively.
  2. Enhancing field trial designs to improve experimental efficiency.
  3. Advancing genomic selection models, incorporating machine learning, spatial analysis, and two-stage modeling.


By refining these approaches, we are driving faster, more efficient genetic improvement in sunflower breeding, one of the world’s most important oilseed crops. Additionally, by integrating publicly available data and simulations, we aim to expand the applicability of these methods to other breeding programs, amplifying their global impact.


 

Next Generation Variety Testing for Improved Cropping On European Farmland (H2020). INNOVAR

Feeding an increasing global population in the face of global climate change is a challenge for the agricultural sector and governments alike. Developing new species with more desirable characteristics is critical, but so is its regulation. Creating the concept of high-performance low-risk (HPLR) varieties within the realm of value for cultivation and use (VCU) testing would help focus on this pressing need while introducing European harmonisation of VCU testing. InnoVar is developing tools and models to enhance current VCU and 'Distinctness, Uniformity and Stability' (DUS) testing practices by exploiting high-tech genomics, imaging and machine learning technologies. Next-generation variety testing will help countries and breeders focus on the challenge of feeding the next generations. Check website: https://www.h2020innovar.eu.


 

 

Optimal Genomic Mating (OGM): A Framework for Improved Mate Allocation in Breeding Programs

A major challenge in breeding programs is that genetic diversity tends to decline over time due to using a limited pool of elite parent plants (commercial varieties), which raises a pivotal question: How do we achieve higher genetic gains while keeping enough diversity for future progress?. This research tackles this issue by leveraging stochastic simulations and real-world implementation of optimal genomic mating (OGM) strategies to support breeders, national breeding programs, and global seed companies in selecting superior crosses. By enhancing decision-making in the crossing stage of breeding programs, this approach accelerates the development of resilient, high-yielding crops while preserving genetic diversity for future breeding.


 

Combining Genomic Approaches to Study Host–Pathogen Relationships in Wheat and Septoria CNS2024-154812

This project leverages previously developed genomic tools to boost our experimental design and detection power for studying Septoria tritici blotch–durum wheat interactions: we’ll validate predictive models in greenhouse assays by challenging 200 wheat lines with 100 pathogen isolates, collecting detailed disease and growth data to refine our models, and extend validation in the field by sampling fresh Spanish Septoria isolates to forecast aggressiveness, performing genome-wide association studies to pinpoint resistance loci, and disseminating our tools and findings through workshops, training sessions, and publications to drive rapid uptake by breeders and farmers.



 

 

Breed-epeautre- Omics. DADR-2024-029390

This project was built through market research, stakeholder consultations and academic reviews to pinpoint the need for sustainable, high-value spelt. It targets three key areas, providing low-input, high-yield varieties for farmers; supplying nutritious, organic grains for the food industry; and promoting soil health and biodiversity through organic practices. After preliminary nutritional and adaptability studies and feasibility trials in France and Spain, direct input from farmers and agri-food experts shaped clear objectives, varietal assessment, genotype-by-environment studies, genome-wide association analyses, and training programs, ensuring the development of resilient spelt strains that deliver economic, environmental and social benefits.



 

New Drought- and Heat-Tolerant Blueberry Varieties through Advanced Genomic Selection

This collaboration between a horticulture company and the Rocinante lab applies cutting-edge genomic selection to fast-track the breeding of blueberry cultivars adapted to water stress and warm climates. Over three breeding cycles (2023–2026) key phenotypic traits, phenology, plant vigor and fruit quality, are recorded on annual cohorts of 200–400 seedlings, while high-throughput genotyping is performed by LGC-Rapid Genomics using a USDA-validated SNP panel and Flex-Seq ExL technology. Predictive models, trained on multilocation data and iteratively refined through training and test sets, enable early identification of superior parents and seedlings, drastically reducing field trials to the most promising genotypes. By integrating multilocation phenotypic and genotypic data, this approach accelerates breeding cycles, optimizes resource use and delivers climate-resilient blueberry varieties.


 

Funding
  1. European Union - Feder-Région Hauts-de-France
  2. Spanish Ministry of Science, Innovation and Universities [CDTI]
  3. Spanish Ministry of Science, Innovation and Universities [CNS2024-154812]
  4. Spanish Ministry of Science and Innovation [PID2019-104518RB-100], (AEI/FEDER, UE)
  5. Spanish Ministry of Science and Innovation [PID2021-123718OB-I00]
  6. European Union’s Horizon 2020 research and innovation program under grant agreement No. 818144
  7. Severo Ochoa Program for Centres of Excellence in R&D
  8. Ministerio de Educación y Formación Profesional of Spain
  9. Universidad Politécnica de Madrid (Programa Propio de I+D+I 2021)

 


Representative Publications

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

Gul, A., Diepenbrock, C.H., Breseghello, F., Minella, E., Munkvold, J.D., Paterson, A.H., Kucek, L.K., Souza, E., Rota, M.L., Yu, L.-X., Yu, J.-K., Ma, Z., Deynze, A.V., Rutkoski, J., Heffner, E.L., Silva, J. da, Sanchez, J.I. 2018. Mark E. Sorrells, in: Plant Breeding Reviews. John Wiley & Sons, Ltd, pp. 1–38. DOI: 10.1002/9781119521358.ch1

Kumar, S., Knox, R.E., Singh, A.K., DePauw, R.M., Campbell, H.L., Isidro-Sanchez, J., Clarke, F.R., Pozniak, C.J., N’Daye, A., Meyer, B., Sharpe, A., Ruan, Y., Cuthbert, R.D., Somers, D., Fedak, G. 2018. High-density genetic mapping of a major QTL for resistance to multiple races of loose smut in a tetraploid wheat cross. PLOS ONE 13, e0192261. DOI: 10.1371/journal.pone.0192261

Isidro‐Sánchez, J., Perry, B., Singh, A.K., Wang, H., DePauw, R.M., Pozniak, C.J., Beres, B.L., Johnson, E.N., Cuthbert, R.D. 2017. Effects of Seeding Rate on Durum Crop Production and Physiological Responses. Agronomy Journal 109, 1981–1990. DOI: 10.2134/agronj2016.09.0527

Akdemir, D., Jannink, J.-L., Isidro-Sánchez, J. 2017. Locally epistatic models for genome-wide prediction and association by importance sampling. Genetics Selection Evolution 49, 74. DOI: 10.1186/s12711-017-0348-8

Akdemir, D., Sánchez, J.I. 2016. Efficient Breeding by Genomic Mating. Frontiers in Genetics 7. DOI: 10.3389/fgene.2016.00210

Isidro, J., Jannink, J.-L., Akdemir, D., Poland, J., Heslot, N., Sorrells, M.E. 2015. Training set optimization under population structure in genomic selection. Theoretical and Applied Genetics 128, 145–158. DOI: 10.1007/s00122-014-2418-4

Akdemir, D., Sanchez, J.I., Jannink, J.-L. 2015. Optimization of genomic selection training populations with a genetic algorithm. Genetics Selection Evolution 47, 38. DOI: 10.1186/s12711-015-0116-6