Elisa Frullanti

This study combines common and rare variant analyses with single-cell multiomics to identify cell-type-specific genetic risk factors for severe COVID-19, revealing molecular mechanisms and potential therapeutic targets.

The paper shows that the L412F polymorphism in TLR3 is associated with severe COVID-19 in males by inhibiting autophagy and reducing TNF production.

The paper develops a machine learning model to predict COVID-19 severity using exonic variants from whole exome sequencing data, finding that variants across all frequency categories contribute to severity.

The paper identifies a genetic variant in the SELP gene associated with increased thrombosis risk in male COVID-19 patients, suggesting potential for anti-P-selectin antibody therapy.

The paper identifies shorter androgen receptor polyQ alleles as protective against severe COVID-19 in males using machine learning on whole-exome sequencing data from the GEN-COVID cohort.

The paper describes a biobanking initiative collecting genetic and clinical data from over 1,000 SARS-CoV-2-positive individuals to study multi-organ involvement and genetic factors in COVID-19 severity.

The paper proposes a post-Mendelian genetic model integrating common and rare variants to predict severe vs. mild COVID-19 outcomes in an Italian cohort.

The paper identifies rare TLR7 variants associated with severe COVID-19 in young males, suggesting a genetic basis for disease susceptibility.

The paper identifies ACE2 gene variants associated with interindividual variability in COVID-19 susceptibility in the Italian population using whole-exome-sequencing data.