The Rocinante Lab | Crop Genomics & Breeding Methods

Bridging Genomics and Field Breeding

Mission

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

Vision

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 for long-term improvement.

What we do

Genomic prediction & selection

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

Association mapping

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

AI/ML for breeding

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

G×E and stability

Quantitative methods to model performance across environments and target adaptation.

Open-source software

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

Training & education

International courses and workshops advancing genomic selection knowledge worldwide.

Open Source

Software Tools for Genomic Selection

2025

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.

GitHub Paper

2024

GEmetrics: Genotype-by-Environment Metrics

Simon Rio, Humberto Fanelli, Julio Isidro Sánchez

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

CRAN GitHub

2021

TrainSel: Training Population Selection

Deniz Akdemir, Julio Isidro y Sánchez

R package for optimization tasks in training set selection using metaheuristics like simulated annealing and genetic algorithms to find the best subset within the whole training set.

GitHub Paper

2020

CovCombR: Combine Covariance Matrices

Deniz Akdemir, Mohamed Somo, Julio Isidro Sánchez

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

Docs Paper

2016

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.

Docs Paper

2015

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.

Docs Paper

Research Output

Selected Publications

2026

MateR: a novel genomic mating framework Javier Fernández-González, Seifelden M Metwally, Julio Isidro Sánchez. Genetics.

2025

Integrating host–pathogen interaction genomics to boost wheat defense against septoria tritici blotch Alejandro Domínguez-Rondón et al. BMC Genomics.

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.

A pangenome and pantranscriptome of hexaploid oat Avni R et al. Nature.

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.

Assessing Genotype-by-Environment Interaction in Desi Chickpea via Bayesian Additive Main Effects Din, A. et al. Agriculture.

Genomic Selection in Plant Breeding: Key Factors Shaping Two Decades of Progress Admas Alemu Abebe et al. Molecular Plant.

2023

Expectation Maximization Algorithm for Combining a Sample of Partially Overlapping Covariance Matrices Akdemir Deniz, Somo M, Isidro y Sánchez J. Axioms.

2022

Hands on Training Optimization in Genomic Selection Julio Isidro Sánchez, Simon Rio, Deniz Akdemir. Genomic Selection Book.

A comparison of methods for training population optimization in genomic selection Javier Fernández-González, Deniz Akdemir, Isidro y Sánchez J. Theoretical and Applied Genetics.

GWAS and genomic prediction of resistance to stripe rust in Central European winter wheat Fahimeh Shahinnia et al, Isidro y Sánchez J. Theoretical and Applied Genetics.

2021

Assessment of genomic prediction reliability and optimization of experimental designs in multi-environment trials Rio S, Akdemir D, Carvalho T, Isidro y Sánchez J. Theoretical and Applied Genetics.

Training set optimization for sparse phenotyping in genomic selection Isidro y Sánchez J, Deniz Akdemir. Frontiers in Plant Science.

Genomic prediction and training set optimization in a structured Mediterranean oat population Rio S et al, Isidro y Sánchez J. Theoretical and Applied Genetics.

TrainSel: an R package for selection of training populations Akdemir D, Rio S, Isidro y Sánchez J. Frontiers in Genetics.

2020

Genome‐wide association mapping of Fusarium langsethiae infection in oat Isidro y Sánchez J et al. The Plant Genome.

Combining Partially Overlapping MultiOmics Data in Databases Using Relationship Matrices Akdemir D, Knox R, Isidro-Sánchez J. Frontiers in Plant Science.

Earlier

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. Theoretical and Applied Genetics (2015).

Changes duration of developmental phases of durum wheat caused by breeding in Spain and Italy Isidro y Sánchez J et al. Annals of Botany (2011).

Check the Full Publication List on Google Scholar

Our People

Meet the Team

Current Members

Julio Isidro y Sánchez

Group Leader

Juan Martin del Menor

PhD Student

Alejandro Dominguez

PhD Student

Seifelden Metwally

PhD Student

Inés Vegas Lorenzo

Technician

Naomi López Angulo

Technician

Angela Cardozo

Technician

Alumni

Javier Fernández González

Researcher

Julián García-Abadillo

University of Florida

Mohsen Yoosefzadeh

University of Guelph

Achille Nyouma

University of Yaoundé

Humberto Fanelli

Bayer

Stacy Roose

PhD in INRA

Deniz Akdemir

Be The Match

Simon Rio

CIRAD, Montpellier

Bleck Tita

Data Scientist

Pablo Atienza Lopez

Bioinformatics

Kane D'Arcy Cusack

Agronomy Manager

Education

Teaching & Training

Recurring

Quantitative Genetics and Plant Breeding

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

Workshops

Genomic Selection Training Workshops

Hands-on training in genomic prediction methods, GBLUP, and training population optimization using our software tools.

Supervision

PhD & MSc Student Supervision

Mentoring graduate students in quantitative genetics, bioinformatics, and computational breeding.

Teaching Resources

Presentations Slides & PPT files

Quantitative Genetics Course materials

WorkBooks Exercises & PDFs

Funded Research

Research Projects

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

European Plant Phenotyping Network (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: Deciphering Host Resistance and RNAi to Minimize Mycotoxin Contamination

SFI/BBSRC • 2017–2022 • Co-PI

PICS: Physiology Infrastructure for Crop Stress

SFI Ireland • 2016 • 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

Consolidation 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 in Durum Wheat

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

Previous Projects

Improving Wheat Productivity under Conditions of Abiotic Stress

NRC Wheat Flagship, Canada • 2012–2017 • Postdoctoral Project

Identification and Selection of Traits for Biomass Production and Conversion Efficiency

European Grant AGRNEX2008N0475 • 2009–2011 • Postdoctoral Project

IDuWUE: Improving Durum Wheat for Water-Use Efficiency and Yield Stability

European Grant ICA3NCTN2002N10085 • 2002–2006 • Team Member

New Approaches for Improving Durum Wheat Adaptation to Mediterranean Environments

Spanish Grant AGL2006-09226 • 2006–2009 • Team Member

Tritordeum Physiology in Mediterranean Conditions

Spanish Grant AGL2005-07257 • 2005–2008 • Team Member

Multidisciplinary Approach to Durum Wheat Improvement: Integration of Ecophysiological and Molecular Techniques

Spanish Grant AGL2002-04285 • 2003–2008 • PhD Project

Acknowledgments

Funding Sources

We are grateful to receive 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 collaboration or joining the lab?

GitHub

CBGP Profile