ERC Advanced Grant Ikerbasque Research Professor
0000-0002-6606-6884Joseph Schacherer obtained his BSc in Cell and Molecular Biology from the Louis Pasteur University (Strasbourg, France) and continued his scientific pursuits by completing his PhD in Genetics and Genomics at the same university, under the supervision of Pr. Jacky de Montigny.
In 2007, Prof. Schacherer joined the University of Strasbourg (France) as Assistant Professor of Genetics and Genomics after completing his postdoctoral training at Princeton University (USA). During this period, he established an independent research program focused on understanding the genetic basis of phenotypic diversity through the integration of population genomics, quantitative genetics, and functional genomics. He further broadened his expertise through visiting appointments at the Department of Genome Sciences, University of Washington (USA, 2014), and the Chemical Genomics Research Group at RIKEN (Japan, 2018), where he strengthened international collaborations and incorporated cutting-edge genomic technologies into his research.
In 2017, he was promoted to Full Professor of Genetics and Genomics at the University of Strasbourg and, from 2023 to 2025, served as Deputy Director of the Genetics, Genomics and Microbiology Laboratory (University of Strasbourg–CNRS), contributing to the scientific leadership and strategic development of one of Europe's leading genomics research centers. Prof. Schacherer has established an internationally recognized research program supported by highly competitive funding, including an ERC Consolidator Grant, an NIH R01 grant, an ERC Advanced Grant, and multiple projects funded by the French National Research Agency (ANR).
In 2026, Prof. Joseph Schacherer joined CIC bioGUNE as an Ikerbasque Research Professor, where he leads the Quantitative and Predictive Genomics Lab. His research focuses on understanding how genetic variation generates phenotypic diversity by integrating population genomics, functional genomics, and systems biology, primarily using Saccharomyces cerevisiae as a model. The group aims to uncover the molecular and evolutionary mechanisms linking genotype to phenotype and to develop predictive frameworks for complex traits and genome evolution.
Awards & recognitions
GSA Medal (2026). Laureate of the Ourisson prize, Cercle Gutenberg (2016). Laureate of “Les espoirs de l’Université de Strasbourg” prize (2014). ESF-EMBO prize, Symposium on Comparative Genomics (2007). Prize of the Biology Society of Strasbourg for the PhD thesis (2005). PhD Fellowship from the Association de la Recherche contre le Cancer (2004-2005). PhD Fellowship from the French ministry of Research and Education (2001-2004).
Personal and group social networks
Latest Publications
The main research interest of the Quantitative and Predictive Genomics Lab is to understand how genetic variation shapes phenotypic diversity and adaptation across eukaryotic organisms. We investigate the molecular and evolutionary mechanisms linking genome variation to complex traits by integrating population genomics, quantitative genetics, functional genomics, and systems biology. Leveraging advances in long-read sequencing, pangenomics, and multi-omics technologies, together with experimental model systems such as Saccharomyces cerevisiae, we combine experimental and computational approaches to uncover the functional consequences of genetic variation. Our long-term vision is to develop predictive models that connect genotype to phenotype, providing fundamental insights into genome evolution, biological function, and the genetic basis of complex traits.
Research line 1: Population genomics and genome evolution
We investigate the origin, distribution, and evolutionary dynamics of genetic variation in natural populations. By integrating large-scale genome sequencing with evolutionary and population genetic analyses, we seek to understand how mutation, recombination, natural selection, demographic history, and domestication shape genome diversity and adaptation. We are particularly interested in the evolutionary processes that generate functional genomic variation and determine the trajectories of genome evolution across populations and species.
Research line 2: Deciphering genotype–phenotype relationships
A central goal of our research is to identify the genetic basis of complex phenotypes and understand how genetic variation translates into phenotypic diversity. We combine population genomics, quantitative genetics, genome-wide association studies, experimental genetics, and functional genomics to identify causal variants and characterize their molecular effects. Using yeast as a powerful model system, we dissect the genetic architecture of complex traits and develop approaches that improve our ability to predict phenotypes from genotypes.
Research line 3: Integrative and predictive genomics
Understanding biological systems requires integrating information across multiple molecular layers. We develop computational and experimental frameworks that combine genomics, transcriptomics, proteomics, metabolomics, and systems biology to characterize the functional consequences of genetic variation. By integrating multi-omics data with predictive modeling, we aim to construct comprehensive models that explain cellular functions, complex phenotypes, and evolutionary processes. Ultimately, our objective is to establish general principles that enable accurate prediction of biological function from genomic information.
Collaborations
Dmitri Petrov (Stanford University), Markus Ralser (Charité Berlin), Maitreya Dunham (University of Washington), David Gresham (New York University).
Links
• https://www.haploteam.org/
• https://www.nature.com/articles/s41586-025-09637-0
• https://www.nature.com/articles/s41586-018-0030-5
• https://www.nature.com/articles/nature07670
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Salomé Bertone
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Victor Loegler
Members
POSTDOCTORAL RESEARCHERLatest Publications
The genetics of the many forms of diversity
Schacherer, J;
GENETICS
2026-06-24
From one genome to thousands, and beyond
Brach, G; Schacherer, J;
FEMS YEAST RESEARCH
2026-01-01