Crop Genomics and Breeding Methods lab

Our research utilizes cutting edge technologies encompassing molecular genomics, phenomics, physiology, pathology, statistics and breeding to research strategies that contribute to the development of superior crop varieties. Our focus involves genomic prediction and selection, association mapping and characterization of allelic diversity


We develop software tools for genomic selection tasks such as TRS optimizacion and imputation of missing data.

TrainSel: an R package for selection of training populations

Deniz Akdemir and Julio Isidro y Sánchez

TrainSel is an R package developed for optimization task in the training set (TRS) while performing statistical analysis or machine learning approaches. It solves combinatorial problems using metaheuristics like simulated annealing (SA) and Genetic algorithms (GA) to find the best subset within the whole TRS.

Code   Paper  

CovCombR: Combine Partial Covariance / Relationship Matrices

Deniz Akdemir, Mohamed Somo, Julio Isidro Sanchez

Combine partial covariance matrices using a Wishart-EM algorithm. It can be used to combine partially overlapping covariance matrices from independent trials, partially overlapping multi-view relationship data from genomic experiments, partially overlapping Gaussian graphs described by their covariance structures.

Docs   Paper  

STPGA: Selection of Training Populations by Genetic Algorithm

Deniz Akdemir

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals.

Docs   Paper  




Genomic prediction and training set optimization in a structured Mediterranean oat population

Simon Rio, Luis Gallego-Sánchez, Gracia Montilla-Bascón, Francisco J. Canales, Isidro y Sánchez, J and Elena Prats


Genome‐wide association mapping of Fusarium langsethiae infection and mycotoxin accumulation in oat (Avena sativa L.)

Isidro y Sánchez, J., D’Arcy Cusack, K., Verheecke‐Vaessen, C., Kahla, A., Bekele, W., Doohan, F., Magan, N. and Medina, A.

Genomic approaches for climate resilience breeding in Oats
Book Chapter

Isidro y Sánchez, J., Elena Prats, Catherine Howarth, Tim Langdon, Gracia Montilla-Bascón


Multiobjective optimized genomic breeding strategies for sustainable food improvement

Deniz Akdemir, William Beavis, Roberto Fritsche-Neto, Asheesh K.Singh Isidro y Sánchez, J.



Effects of Seeding Rate on Durum Crop Production and Physiological Responses

Isidro y Sánchez, J., Ben Perry, Asheesh K. Singh, Hong Wang, Ronald M. DePauw et al.


Genomic Selection
Book Chapter

Isidro-Sánchez J, Akdemir D, Burke J.
The World Wheat Book: A History of Wheat Breeding, Vol. 3, Chapter 32, eds A. William, B. Alain, and V. G. Maarten (Paris: Lavoisier), 1001–1023.

Chromatographic Methods to Evaluate Nutritional Quality in Oat
Book Chapter

Montilla-Bascón, Gracia, Corey D. Broeckling, O. Hoekenga, E. Prats, M. Sorrells and Isidro-Sánchez J.



Brassinosteroid leaf unrolling QTL mapping in durum wheat

Isidro y Sánchez, J., Knox R, Singh A.K, Clarke F.R, Krishna P, DePauw R.M, Clarke, J.M, Somers D

Quantitative genetic analysis and mapping of leaf angle in durum wheat

Isidro y Sánchez, J., Knox R, Singh A.K, Clarke F.R, DePauw R.M, Clarke, J.M, Somers D

    2007 - 2011

The Lab

Julio Isidro

Group Leader


Humberto Fanelli Carvalho

Postdoctoral Fellow

a a a

Javier Fernández

PhD Student


Julián García-Abadillo Velasco

PhD Student

Achille Nyouma

Postdoctoral Fellow

Previous members

Deniz Akdemir

Clinical Data Scientist at Be The Match

Simon Rio

Researcher in polyploid genomics/genetics Cirad, Montpellier

Pablo Atienza Lopez

MSc. Bioinformatics at University of Copenhagen


Learning resources made by lab members will be soon here!


Current Projects


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

There have been many scientific reports evaluating models and accuracy of GAB in plant breeding programs. However, the information on how, when, and why to apply GAB tools in real plant breeding programs is scarce. The main reason for this lack of information is that most plant breeding programs are private, and also GAB applications are in relatively new tools. Here, we aim to apply knowledge driven genomic prediction tools from a empirical public breeding program that will help to deliver general guideline applications of this technology.


Identificación de nuevas fuentes de resistencia horizontal a septoria y roya en trigo duro (PLEC2021-007930)

This study aims to characterize Zymoseptoria tritici infections and perform genome predictions and genome-wide association analysis on wheat and also on the pathogen. In this project we seek to perform genome analysis to gain a better understanding of the pathosystem and to predict the evolution and incidence of pathogens to anticipate future pathogen attacks to crops by studying host-pathogen interactions.

Machine learning approaches applied to genomic assisted breeding

Programa Propio de I+D+I 20201 de la Universidad Politécnica de Madrid.
Convocatoria de ayudas para contratos predoctorales.

Wheat (the second most important crop worldwide) yields are not currently increasing at comparable rates to those achieved in previous decades. Exploitation of the full range of available genetic resources (pre-breeding) could help develop new varies that will be needed in the future. The emergent technologies based on Artificial Intelligence and Machine learning are interesting tools to handle the huge volumes of genotypic and phenotypic data that are generated from agronomic and biological experiments. The aim of this PhD project is to develop statistical and programming tools to exploit and decode the hidden patterns underlying data and apply this knowledge on real breeding problems in both academical and industrial fields.

Genomic assisted breeding applied to Syngenta sunflower breeding program.

Programa UPM-Syngenta.
Convocatorias FPU

This project aims to provide a benchmark guideline on genomic assisted breeding for Syngenta sunflowerbreeding program by building tools and models that augment current practices capitalizing on advances in genomics, phenomics, imaging technologies, and machine learning.


Next Generation Variety Testing For Improved Cropping On European Farmland (H2020)

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.


Knowledge-driven genomic predictions for sustainable disease resistance in wheat (Suscrop)

WheatSustain will establish a close collaboration among world leading experts on genomic prediction modelling in plants and animals, bioinformatics, wheat genomics and leaders in the field of plant pathology and host-pathogen relationships for stripe rust and FHB resistance in wheat. An interdisciplinary research team is established involving cutting-edge research groups from Norway, Ireland, Germany, Austria, Mexico, USA and Canada. Plant breeders from public and private breeding programs will take active part in the research by providing germplasm with phenotypic and genotypic data, take part in disease evaluations and test out the developed breeding methodologies in their breeding programs.

Oats for the future

Deciphering potential of host resistance and RNAi to minimise mycotoxin contamination under present and future climate scenarios

This study aims to perform an association mapping analysis of hexaploid oat (Avena sativa L.) cultivars for resistance to mycotoxins produced by Fusarium langsethiae, by detecting genetic variants involved in the resistance using Genome-Wide Association (GWA) analysis. In addition, a screening of a wide range of heritage Irish oat genotypes for distinct gene expression profiles relevant to differential mycotoxin contamination profiles will be performed. Finding regions of the genome associated with resistance to F. langsethiae will highlight chromosome locations of the oat genome that could be used as hotspots for further studies.

Previous Projects

Irish Grants

Developing multi-use barley to improve the organic Irish market (2019-2022)

Granted-96K. PI. Julio Isidro-Sánchez.

PICS: Physiology Infrastructure for Crop Stress (2016)

295K. PI. Julio Isidro-Sánchez.

Canadian Grants

CTAG: Canadian Triticum Applied Genomics (2015-2019)

Scientific advisor of WPs.

Improving Wheat Productivity under Conditions of Abiotic Stress (2012-2017)

A proposed project as part of the National Research Council Wheat Flagship Program and the Canadian Wheat Improvement Consortium. 14 million. Postdoctoral Project.

European Grants

Identification and selection of traits that maximize biomass production as well as enhance the efficiency of biomass conversion by novel processes are critical for the viability of biorefineries (2009-2011)

AGRNEX2008N0475. Postdoctoral Project.

IDuWUE: Improving durum wheat for water use efficiency and yield stability through physiological and molecular approaches (2002-2006)

ICFPN502A3PR03 (ICA3NCTN2002N10085), 963K. PhD. Project.

Spanish Grants

Aproximación multidisciplinary al incremento de la eficacia en la mejora del trigo duro: Integración de tácnicas ecofisiológicas y moleculares (2003-2006)

AGL2002-04285-C03-02. 65K. Member of the Research team of this project.

Nuevas vias para mejorar la adaptacion del trigo duro, Triticum turgidum L.Var.Durum a ambientes mediterraneos (2006-2009)

AGL2006-09226-C02-02- 02/AGR. 45K. Member of the Research team of this project.

Fisiología del tritórdeo en condiciones mediterráneas: enfoque multidisciplinary para una colaboración transmediterránea azahar (2005-2008)

AGL2005-07257- C04-04. 28K. Member of the Research team of this project.


We are very grateful to receive funding for our research from: