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.

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

The paper identifies a new class of non-coding variants, dsbSNPs, that modulate DNA double-strand break frequency and are enriched in GWAS associations for complex diseases.

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.

This study uses Mendelian randomization to identify causal effects of plasma triglycerides on 2,600 disease traits, finding 19 significant associations including cardiovascular and non-atherosclerotic diseases.

The paper constructs a tissue-level phenome-wide network map linking genes and phenotypes using colocalized signals from the UK Biobank and Genotype-Tissue Expression project.

The paper uses GTEx data to analyze GWAS loci, showing dose-dependent RNA effects and proposing target genes for 47% of loci using combined methods.

The paper quantifies horizontal pleiotropy across 372 phenotypes using UK Biobank data and finds it is widespread, especially in polygenic traits and regulatory regions.

The paper conducts a GWAS in non-European populations, identifies new genetic loci, and highlights the importance of diverse cohorts for understanding complex traits.

MR-PRESSO detects pleiotropic outlier instruments in summary-level Mendelian randomization; across 82 traits it found pleiotropy in 41% of significant causal estimates, with distortions from −168% to 189%.