Stefania Mantovani

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 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 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.