Vincenzo Forgetta

The paper identifies nephronectin as a mediator of obesity's effect on COVID-19 severity using proteome-wide Mendelian randomization and validates its expression in lung cells.

This study uses whole-exome sequencing to directly call HLA alleles in the UK Biobank, identifying 129 novel associations between HLA alleles and 11 autoimmune diseases.

The paper develops a latent factor model to capture additional genetic risk from family history, improving polygenic risk prediction for height and 11 complex diseases.

The paper introduces the Effector Index, an algorithm to predict causal genes at GWAS loci using genomic features, achieving 85% AUC in type 2 diabetes validation.

The paper investigates the association of rare loss-of-function variants in type I IFN immunity genes with severe COVID-19 and finds no significant association.

This study identifies OAS1 as a protective factor against COVID-19 severity and susceptibility using Mendelian randomization and case-control analyses.

This paper identifies 518 genetic loci associated with bone mineral density and fracture risk, validates causal genes in mice, and provides insights for osteoporosis drug development.

This paper uses machine learning to predict osteoporotic fracture risk from genotypes, finding that genomic predictions outperform clinical risk factors in identifying fracture risk.