Peer-Reviewed Publication
Nat Commun2026May 7, 2026Journal Article
A versatile multi-components mixed model for bacterial-Genome Wide association studies.
Arthur Frouin1, Fabien Laporte2, Lukas Hafner3, Mylène Maury3,4, Zachary R McCaw5, Hanna Julienne2, Léo Henches2, Alexandre Leclercq3,4, Rayan Chikhi2, Marc Lecuit3,4,6, Hugues Aschard7,8
1Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France. arthurs.frouin@gmail.com.
2Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France.
3Institut Pasteur, Université Paris Cité, Inserm U1117, Biology of Infection Unit, Paris, France.
4Institut Pasteur, Listeria French National Reference Center and WHO Collaborating Center, Paris, France.
5Insitro, South San Francisco, California, USA.
6Necker-Enfants Malades University Hospital, Department of Infectious Diseases and Tropical Medicine, Institut Imagine, AP-HP, Paris, France.
7Department of Computational Biology, USR 3756 CNRS, Institut Pasteur, Paris, France. hugues.aschard@pasteur.fr.
8Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. hugues.aschard@pasteur.fr.
Abstract
Genome-wide Association Studies (GWAS) have played a crucial role in uncovering the genetics underlying complex human traits. Recently, there has been considerable interest in adapting GWAS-like methodologies to investigate pathogenic bacteria. Despite the variety of methods proposed, there remains a lack of clarity on how to effectively model the intricate population structures found in bacterial…
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