CBGP · Universidad Politécnica de Madrid

Crop Genomics &
Breeding Methods

Led by Julio Isidro y Sánchez, Associate Professor & Group Leader

Integrating quantitative genomics, phenomics, and AI to build prediction models and decision tools that accelerate crop improvement across diverse environments.

Our Research Join the Lab

Crops we work with

Small Grains

Small Grains

Maize

Maize

Sunflower

Sunflower

Olives

Olives

Table Grapes

Table Grapes

Berries

Berries

45+ Publications
6 Open-source tools
15+ Research grants
20+ Countries reached
About the Lab

Bridging Genomics and Field Breeding

The Rocinante Lab develops and applies quantitative genomics to accelerate crop improvement. We integrate genomics, phenomics, statistics, and field experimentation to deliver robust prediction models and decision tools that increase genetic gain, improve resilience, and support breeding programs across diverse environments.

We envision breeding pipelines where data-driven prediction and optimized mating translate biological understanding into measurable, sustainable progress — enabling the rapid development of high-performing crop varieties while safeguarding genetic diversity.

Open Positions

We welcome applications from motivated PhD students and postdocs. Get in touch to discuss opportunities.

What we do

Genomic Prediction & Selection

Models and experimental designs that maximize predictive accuracy for complex quantitative traits.

Association Mapping

Identifying loci, haplotypes, and genomic regions that explain trait variation across populations.

AI / ML for Breeding

Improving selection, mate allocation, and resource allocation under real breeding program constraints.

G×E & Stability

Quantitative methods to model performance across environments and target optimal adaptation.

Open-source Software

Reproducible R tools that make advanced statistical methods usable in practical breeding workflows.

Training & Education

International courses and workshops advancing genomic selection knowledge worldwide.

Open Source

Software Tools

R packages for genomic selection, mating optimization, and training set design.

GitHub Org

MateR — Genomic Mating Package

Javier Fernández González · Seif Maetwally · Julio Isidro Sánchez

Optimize mating plans using genomic information to maximize genetic gain, within-family variance, and genetic diversity. Supports polyploids, multi-trait optimization, additive and dominance effects, directional dominance, and testers.

R 2025
GitHub Paper

GEmetrics — Genotype×Environment Metrics

Simon Rio · Humberto Fanelli · Julio Isidro Sánchez

Calculate BLUP of genotype-by-environment metrics: ecovalence, environmental variance, Finlay–Wilkinson regression, and Lin & Binns superiority measure.

R CRAN 2024
CRAN GitHub

TrainSel — Training Population Selection

Deniz Akdemir · Julio Isidro y Sánchez

Optimization of training set selection via metaheuristics (simulated annealing, genetic algorithms) to find the best subset that maximizes predictive ability in high-dimensional problems.

R 2021
GitHub Paper

CovCombR — Combine Covariance Matrices

Deniz Akdemir · Mohamed Somo · Julio Isidro Sánchez

Combines partial covariance matrices using a Wishart-EM algorithm for independent trials, multi-view relationship data from genomic experiments, and Gaussian graphs.

R 2020
Docs Paper

GenomicMating — Efficient Breeding

Deniz Akdemir · Julio Isidro Sánchez · Hanna Haikka · Itaraju Baracuhy Brum

Implements the mate selection approach for efficient breeding by genomic mating, as published in Frontiers in Genetics (2016).

R 2016
Docs Paper

STPGA — Selection by Genetic Algorithm

Deniz Akdemir

Selection of Training Populations by Genetic Algorithm. Combines predictive analytics and experimental design to optimize results in high-dimensional prediction problems.

R CRAN 2015
Docs Paper
Research Output

Selected Publications

2026

MateR: a novel genomic mating framework

Javier Fernández-González, Seifelden M Metwally, Julio Isidro Sánchez. Genetics.

Phenotypic and genetic resistance to Septoria blotch disease in European wheat varieties

Conor Copeland, Julio Isidro y Sánchez, Humberto Fanelli, Fiona M. Doohan. The Plant Genome.

2025

Integrating host–pathogen interaction genomics to boost wheat defense against septoria tritici blotch

Alejandro Domínguez-Rondón et al. BMC Genomics.

A pangenome and pantranscriptome of hexaploid oat

Avni R et al. Nature.

Mapping novel yellow and leaf rust loci and predicting resistance in cross derived Canadian durum wheat

Menor de Gaspar, J. et al. The Plant Genome.

Maximizing the accuracy of genetic variance estimation

Fernández-González, J., Isidro y Sánchez, J. Theoretical and Applied Genetics.

Phenotypic plasticity of bread wheat contributes to yield reliability under heat and drought stress

Jatinder Singh Sangha et al. PLOS ONE.

2024

Optimizing fully-efficient two-stage models for genomic selection using open-source software

Fernández-González, J., Isidro y Sánchez, J. Plant Methods.

Maximizing Efficiency in Sunflower Breeding through Historical Data Optimization

Fernández-Gónzalez Javier et al. Plant Methods.

Global genomic population structure of wild and cultivated oat reveals signatures of chromosome rearrangements

Bekele, WA et al. Nature Communications.

Dissecting the Complex Genetic Basis of Pre and Post-harvest Traits in Vitis vinifera L. using GWAS

García-Abadillo, J. et al. Horticulture Research.

Genomic Selection in Plant Breeding: Key Factors Shaping Two Decades of Progress

Admas Alemu Abebe et al. Molecular Plant.

2023–2020

Expectation Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices

Akdemir D, Somo M, Isidro y Sánchez J. Axioms (2023).

A comparison of methods for training population optimization in genomic selection

Javier Fernández-González, Deniz Akdemir, Isidro y Sánchez J. TAG (2022).

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials

Rio S, Akdemir D, Carvalho T, Isidro y Sánchez J. TAG (2021).

Efficient Breeding by Genomic Mating

Akdemir D, Isidro y Sánchez J. Frontiers in Genetics (2016).

Training set optimization under population structure in genomic selection

Isidro y Sánchez J et al. TAG (2015).

Full Publication List on Google Scholar
Our People

Meet the Team

Julio Isidro y Sánchez

Julio Isidro y Sánchez

Group Leader · Associate Professor

Associate Professor at Universidad Politécnica de Madrid (UPM) and Group Leader at CBGP. His research focuses on quantitative genetics, genomic selection, and the development of computational tools for crop improvement. He has led projects funded by the EU Horizon 2020, and AEI and with industry partners across Europe, North America and Asia.

ORCID Google Scholar GitHub CBGP Profile

Current Members

Juan Martin del Menor

Juan Martin del Menor

PhD Student

Alejandro Dominguez

Alejandro Dominguez

PhD Student

Seifelden Metwally

Seifelden Metwally

PhD Student

Juan Manuel Gallego Rabadan

Juan Manuel Gallego Rabadan

Master Student

Inés Vegas Lorenzo

Inés Vegas Lorenzo

Technician

Naomi López Angulo

Naomi López Angulo

Technician

Angela Cardozo

Angela Cardozo

Technician

Alumni

Javier Fernández González

Javier Fernández González

Researcher

Julián García-Abadillo

Julián García-Abadillo

University of Florida

Mohsen Yoosefzadeh

Mohsen Yoosefzadeh

University of Guelph

Achille Nyouma

Achille Nyouma

University of Yaoundé

Humberto Fanelli

Humberto Fanelli

Bayer

Stacy Rousse

Stacy Rousse

PhD at INRA

Deniz Akdemir

Deniz Akdemir

Be The Match

Simon Rio

Simon Rio

CIRAD, Montpellier

Bleck Tita

Bleck Tita

Data Scientist

Pablo Atienza Lopez

Pablo Atienza Lopez

Bioinformatics

Kane D'Arcy Cusack

Kane D'Arcy Cusack

Agronomy Manager

Education

Teaching & Training

Advancing genomic selection knowledge through formal courses, international workshops, and open resources.

Courses

Quantitative Genetics and Plant Breeding

Graduate-level course on statistical methods in plant breeding, genomic selection theory, and practical applications in R. UPM, recurring.

Workshops

Genomic Selection Training Workshops

Hands-on training in genomic prediction methods, GBLUP, and training population optimization using our open-source tools. Delivered internationally.

Supervision

PhD & MSc Student Supervision

Mentoring graduate students in quantitative genetics, bioinformatics, and computational breeding at UPM and international partner institutions.

Teaching Resources

Presentations Presentations Slides & PPT files Quantitative Genetics Quantitative Genetics Course materials WorkBooks WorkBooks Exercises & PDFs
Funded Research

Research Projects

Green bar = active project

European & International Grants (2015–Present)

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

DADR-2024-029390 2025–2028 PI

InnoVar: Next Generation Variety Testing for Improved Cropping

H2020 Grant No. 818144 2019–2024 Deputy WP2 Leader

HealthyOats: Innovation in Oat Product Development

INTERREG Ireland–Wales 2020–2023 Project Coordinator

WheatSustain: Knowledge-driven Genomic Predictions for Sustainable Disease Resistance

SusCrop ERA-NET 2019–2022 PI & WP1 Lead

Effect of Soil Water Content on Seedling Emergence in Small-grain Cereals

EPPN 2018–2020 PI

Developing Multi-use Barley to Improve the Organic Irish Market

Irish Research Council 2019–2022 PI

CONSUS: Crop Optimization through Sensing, Understanding & Visualization

SFI Ireland 2017–2022 Funded Investigator

Oats for the Future: Host Resistance and RNAi to Minimize Mycotoxin Contamination

SFI/BBSRC 2017–2022 Co-PI

CTAG: Canadian Triticum Applied Genomics

Canadian Grant 2015–2019 Scientific Advisor

Spanish Grants (2015–Present)

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

CNS2024-154812 2025–2027 PI

Genomic-Assisted Breeding for Sustainable Agriculture: A Reference Approach

PID2021-123718OB-I00 2022–2025 PI

Machine Learning Approaches Applied to Genomic-Assisted Breeding

UPM-PhD Plan Propio 2022–2026 PI

Genomic-Assisted Breeding Applied to Sunflower Improvement (Syngenta)

Industrial Doctorate 2022 PI

WheatRes: Identification of New Sources of Horizontal Resistance to Septoria and Rust

PLEC2021-007930 2021–2024 PI

Breeding Tools to Harness Yield Productivity by Applying Genomic Selection

R&D Madrid 2021–2023 PI

Oat PanGenome Consortium

International Consortium 2020–Present Member

Svevo Platinum Genome Consortium

International Consortium 2020–Present Member

TRENDING_Wheat: Improving Accuracy and Efficiency of Selection for Complex Traits

PID2019-109089RB-C32 2019–2023 GS Implementation
Acknowledgments

Funding Sources

We are grateful for support from leading research funding organizations.

Agencia Estatal de Investigación (AEI) CDTI Syngenta Spanish Ministry of Science and Innovation European Union Horizon 2020 Severo Ochoa Program Universidad Politécnica de Madrid
Get in Touch

Contact Us

Interested in scientific collaboration, industry partnerships, or joining the lab as a PhD student or postdoc? We welcome enquiries.

For prospective graduate students: please review our open projects and include a brief research statement with your enquiry.

The Rocinante Lab
Centre for Plant Biotechnology and Genomics (CBGP)
Universidad Politécnica de Madrid
Campus Montegancedo, Pozuelo de Alarcón
28223 Madrid, Spain
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