GENOMIC ASSISTED BREEDING
Lab Web Page: https://therocinante-lab.github.io
- Akdemir, Deniz - Postdoctoral Fellow
- Fernández González, Javier - PhD Student
- García Abadillo Velasco, Julián - PhD Student
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.
Current research projects
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.
A diversity panel of 190 spring oat varieties from the i) European Avena Database (EADB), ii) Nordic Genetic Resource Centre and iii) Irish heritable germplasm from the Virtual Irish Centre in Crop improvement (VICCI) will be used for this project. The panel represents cultivars from important oat producers in Europe.
INNOVAR- 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.
WHEATSUSTAIN- 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.
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
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