AgreenSkills session, year: 2nd session, 2015
Receiving laboratory: UR group Mathematical and Statistical Methods (Biometris),Wageningen University
Country of origin : France
Deciphering genotype x environment interactions by coupling genetics and ecophysiological modelling
The development of genotyping technologies has permitted to generate new tools for plant breeders. Molecular markers can indeed be used to detect genomic regions involved in the determinism of traits (QTL detection) or to predict the performance of selection candidates (genomic selection, GS). These two approaches were successfully implemented in some cases, but their efficiency is limited by the fact that varieties interact with their environments. The effect of the detected QTL on integrative traits such as yield is indeed often different from one environment to another. And in GS, predictions between environments often have reduced accuracy because of these genotype x environment interactions (GEI), which limit genetic progress. A few approaches were recently proposed to improve the efficiency of QTL detection and GS thanks to the integration of environmental covariates, but the gain in detection power and accuracy was limited. My research proposal is to combine ecophysiological and genetic approaches to better model GEI. Crop model can indeed be used to predict key developmental traits such as flowering date, and to estimate stress indexes at key stages for collection of varieties. We propose to use these environmental covariates to characterize the environments specifically for each variety. The sensitivity of different wheat varieties to these covariates will be estimated, and used for QTL detection and GS. Flexible mixed models will be developed to let the covariates capture various proportions of the variance, depending on their importance. This should make possible the detection of stress tolerance QTL, and improve prediction accuracy of new varieties in new environments. This approach will be evaluated on a wheat panel of 220 varieties phenotyped in 40 environments for phenological and yield traits, and genotyped with a 420k SNP array within the PIA-ANR project BreedWheat.
I studied agronomy in AgroParisTech and AgroCampus Ouest with a master degree in 2009. I did a PhD in INRA Le Moulon on the optimization of genomic selection and association mapping under the supervision of Alain Charcosset and together with Limagrain, KWS and Biogemma. I obtained a permanent position in 2014 in INRA Clermont-Ferrand about the adaptation of wheat to climate change. I try to combine crop growth modelling with genetic modelling to better understand the genotype x environment interactions and develop tools to increase genetic progress in the context of climate change. I currently focus my research to drought stress using different kind of experiments (multi-environment trials, high-throughput phenotyping platforms…) on various traits (phenology, roots, yield components…).
–Rincent R., Kuhn E, Monod H., Oury F.-X., Rousset M., Allrad V., Le Gouis J. (2017) Optimization of multi-environment trials for genomic selection based on crop models. Theor. App. Genet.
– Revilla P., V.M. Rodríguez, A. Ordás, R. Rincent, A. Charcosset, C. Giauffret, A.E. Melchinger, C.C. Schön, E. Bauer, T. Altmann, D. Brunel, J. Moreno-González, L. Campo, M. Ouzunova, Á. Álvarez, J.I. Ruíz de Galarreta, J. Laborde, R.A. Malvar. 2016 Association mapping for cold tolerance in two large maize inbred panels. BMC Plant Biology;
– Rincent R., S. Nicolas, S. Bouchet, T. Altmann, D. Brunel, P. Revilla, R. A. Malvar, J. Moreno-Gonzalez, L. Campo, A. E. Melchinger, W. Schipprack, E. Bauer, C-C. Schoen, N. Meyer, M. Ouzunova, P. Dubreuil, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, P. Flament, L. Moreau, A. Charcosset 2014 Dent and Flint maize diversity panels reveal important genetic potential for increasing biomass production. Theor. App. Genet.127: 2313-2331;
– Rincent R., L. Moreau, H. Monod, E. Kuhn, A.E. Melchinger, R. A. Malvar, J. Moreno-Gonzalez, S. Nicolas, D. Madur, V. Combes, F. Dumas, T. Altmann, D. Brunel, M. Ouzunova, P. Flament, P. Dubreuil, A. Charcosset, T. Mary-Huard 2014 Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics 197:375-387;
– Revilla P., V. M. Rodríguez, R. Rincent, A. Charcosset, C. Giauffret, A. E. Melchinger, C.-C. Schön, E. Bauer, T. Altmann, D. Brunel, F. Tardieu, J. Moreno-González, L. Campo, M. Ouzunova, J. Laborde, A. A. Rodríguez, J. I. Ruíz de Galarreta, R. A. Malvar 2014 Variability for cold tolerance in two large maize inbred panels adapted to European climates. Crop Sci. 54: 1981-1991;
– Bauer E., M. Falque, H. Walter, C. Bauland, C. Camisan, L. Campo, N. Meyer, N. Ranc, R. Rincent, W. Schipprack, T. Altmann, P. Flament, A. E. Melchinger, M. Menz, J. Moreno-González, M. Ouzunova, P. Revilla, A. Charcosset, O. C. Martin, C.-C. Schön 2013 Intraspecific variation of recombination rate in maize. Genome Biol. 14: R103;
– Rincent R., D. Laloe, S. Nicolas, T. Altmann, D. Brunel, P. Revilla, V.M. Rodríguez, J. Moreno-Gonzalez, A. Melchinger, E. Bauer,C-C. Schoen,N. Meyer, C. Giauffret, C. Bauland, P. Jamin, J. Laborde, H. Monod, P. Flament, A. Charcosset, L. Moreau 2012 Maximizing the reliability of genomic selection by optimizing the calibration set of reference individuals: comparison of methods in two diverse groups of maize inbreds (Zea mays L.). Genetics 192: 715–728.