Renaud Rincent

Centre National de la Recherche Scientifique

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About

Renaud Rincent is a leading figure in quantitative genetics and plant breeding, with a focus on integrating high-throughput phenotyping and genomic prediction to accelerate crop improvement. His work centers on developing statistical models that leverage metabolomics and other omics data to enhance selection accuracy, particularly in complex traits like yield and stress tolerance. His most-cited paper, "Plant metabolomics and breeding" (2020, 14 citations), provides a foundational framework for using metabolic profiles as predictive markers, bridging the gap between genotype and phenotype. Beyond this, Rincent has contributed to methods for optimizing genomic selection in diverse breeding populations, addressing key challenges like genotype-by-environment interactions. His research has practical implications for sustainable agriculture, enabling faster development of resilient crop varieties. With a growing citation impact, Rincent’s work is shaping the next generation of data-driven breeding strategies, making him a key collaborator for researchers seeking to harness big data in plant science.