Clément Chatelain

This paper analyzes 100,526 GWAS to identify pleiotropic genes and their therapeutic implications, finding that highly pleiotropic genes are associated with safety liabilities and that combining PAV support with intermediate pleiotropy improves therapeutic success.

This study identifies genetic risk factors associated with severe psoriasis and shows that polygenic risk scores can predict severe disease outcomes better than traditional risk factors.

This study identifies 1,242 novel genetic variants and 31 novel genes associated with frailty through genome-wide association and proteomics integration.

This study identifies 440 genes/proteins associated with atopic dermatitis severity through multi-omics analyses and highlights four with significant cross-evidence, including CEP85, which may contribute to severity via impaired phagocytosis.

The paper identifies RNA-initiating sites at STRs and links them to gene expression, revealing regulatory roles and interactions with Alu repeats.

This study conducts a genome-wide meta-analysis across three biobanks to identify new genetic loci associated with lumbar disc herniation, expanding the known genetic landscape.

This study identifies genetic risk loci and candidate genes for inflammatory polyneuropathy and CIDP through GWAS and functional analyses.

This study conducts a GWAS to analyze acne severity, identifying two new loci and six novel genes associated with severity, while showing limited overlap with known acne risk loci.

This study identifies therapeutic targets by integrating GWAS with multi-omics data, finding enriched causal genes and novel associations like IL6ST in polymyalgia rheumatica.

This study identifies gene-gene interactions in multiple sclerosis using GWAS data, finding 4 missense variant pairs and 117 eQTL-mediated pairs, including known interactions like GLI-I and SUFU binding.

The paper identifies genome-wide significant epistasis interactions across 502 phenotypes using UK Biobank data and replicates some in Finngen consortium cohorts, revealing common variants' roles in complex diseases.

This paper introduces epiGWAS, a method for detecting epistasis in genome-wide association studies, showing improved identification of gene-gene interactions compared to existing approaches.

This paper introduces kernelPSI, a method for nonlinear post-selection inference in GWAS, showing it improves statistical power and interpretability compared to existing tools.