Léo Henches

The paper identifies and functionally characterizes regulatory variants in DPP9 associated with severe COVID-19 by analyzing genetic risk factors and their impact on gene expression and immune cell function.

The paper develops a taxonomy-free GWAS method using k-mers to analyze gut microbiome associations with host phenotypes, identifying significant links to seven traits in a cohort of healthy participants.

This study assesses the progress and limitations of polygenic risk score prediction accuracy for common diseases, showing diminishing returns from recent GWAS and highlighting the need for broader variant coverage.

This study evaluates multitrait GWAS for brain MRI phenotypes, showing predictable gains over univariate GWAS and proposing data-driven strategies for phenotype selection.

This paper applies a multi-trait GWAS approach to non-European ancestries, identifying new associations and demonstrating increased replication of European loci.

The paper uses multitrait GWAS to identify genetic links between MRI brain volume phenotypes and psychiatric disorders, finding significant associations with ADHD and schizophrenia.

The paper introduces MANOCCA, a method to test the effect of predictors on the covariance matrix of multivariate outcomes, showing it outperforms existing methods in simulations and identifies significant associations in real data.

The paper develops ChoruMM, a multi-components mixed model for bacterial GWAS, showing that standard human genetics models are inadequate for highly structured bacterial genomes.