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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 the French university AgroParisTech, and specialized in plant breeding in the university AgroCampus Ouest in Rennes. After a first experience in research during my Master I decided to go on with a PhD in quantitative genetics in Le Moulon under the supervision of A. Charcosset in collaboration with Limagrain, Biogemma and KWS. My PhD was about the optimization of genomic selection and association mapping. Genomic selection is a method to predict the performance of unobserved individuals using genotypic data, and association mapping is an approach to detect the regions of the genome involved in the variability of a trait. After my PhD I obtained a permanent position at INRA Clermont-Ferrand about the adaptation of Wheat to climate change. I am interested in traits related to drought and heat stress, and try to use jointly ecophysiological modelling and quantitative genetics to increase the efficiency of plant breeding tools in this context of changing climate.
Ly, D., Huet, S., Gauffreteau, A., Rincent, R., Touzy, G., Mini, A., Jannink, JL, Cormier, F., Paux, E., Lafarge, S., Le Gouis, J., Charmet, G., 2018. Whole-genome prediction of reaction norms to environmental stress in bread wheat (Triticum aestivum L.) by genomic random regression. Field Crops Research, 216, 32-41. Doi: 10.1016/j. fcr.2017.08.020.
Rincent R., A. Charcosset, L. Moreau, 2017. Predicting genomic selection efficiency to optimize calibration set and to assess prediction accuracy in highly structured populations. Theoretical and Applied Genetics. 30(11):2231-2247. Doi: 10.1007/s00122-017-2956-7.
Rincent R., E. Kuhn, H. Monod, F.-X. Oury, M. Rousset, V. Allard, J. Le Gouis, 2017. Optimization of multienvironment trials for genomic selection based on crop models. Theoretical and Applied Genetics, 130(8):17351752. Doi: 10.1007/s00122-017-2922-4.
Rincent R., L. Moreau, H. Monod, et al., 2014. Recovering power in association mapping panels with variable levels of linkage disequilibrium. Genetics 197:375-387.
Rincent R., D. Laloe, S. Nicolas, T et al., 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.