Author

Martin Tournaire

Centre National de la Recherche Scientifique

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Genetic & Genomic Medicine· 3

Signature Assays, Organisms & Methods

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Assay:GWAS summary statistics· 3 Approach:statistical model· 3 Research Focus:benchmarking· 2 Organism:human· 2 Research Focus:database / resource· 1 Research Focus:fine-mapping· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Marie VerbanckiD· 3 shared Asma NouiraiD· 2 shared Mario Favre Moiron· 2 shared Carla Márquez‐LunaiD· 1 shared Ghislain RocheleauiD· 1 shared Ron DoiD· 1 shared Yves RozenholciD· 1 shared

Activity by Year

2024-2026 · peak 2/yr

Co-Author Collaboration Network

7 top collaborators · 12 mutual co-author links

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3 shared papersGenetic & Genomic Medicine

Marie VerbanckORCID

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3 papers

Recent·Most cited

The omnicausal model reveals the highly polyfactorial nature of complex diseases

Carla Márquez‐Luna, Martin Tournaire, Ghislain Rocheleau, Ron Do, Marie Verbanck

The paper introduces the omnicausal model to assess how phenome-wide risk factors collectively explain causality in complex diseases, showing they explain more variance than known risk factors.

Genetic Epidemiology · Aug 1, 2026Peer-reviewedGenetic & Genomic Medicine

Deriving LD-adjusted GWAS summary statistics through linkage disequilibrium deconvolution

Asma Nouira, Martin Tournaire, Mario Favre Moiron, Marie Verbanck

This paper introduces LDeconv, a method to adjust GWAS summary statistics for linkage disequilibrium, reducing false positives while preserving true associations.

medRxiv · Apr 11, 2026 · PreprintGenetic & Genomic Medicine

Inferring genetic variant networks by leveraging pleiotropy shows trait relationships drive massive pleiotropy in GWAS

Martin Tournaire, Asma Nouira, Mario Favre Moiron, Yves Rozenholc, Marie Verbanck

The paper introduces PRISM, a method using pleiotropy to infer variant-trait networks, finding direct effects account for 11% of significant effects but are highly heritable.

medRxiv · Jun 1, 2024 · PreprintGenetic & Genomic Medicine