Antoine Auvergne

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 study evaluates trait selection strategies in multi-trait GWAS, finding that diverse trait sets improve variant detection compared to univariate screening and specific methods like JASS outperform others.

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.