Sara Amitrano

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

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.

Shorter androgen receptor polyQ alleles protect against life-threatening COVID-19 disease in males.

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.

Employing a Systematic Approach to Biobanking and Analyzing Genetic and Clinical Data for Advancing COVID-19 Research

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.

Post-Mendelian genetic model in COVID-19

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.

Clinical and molecular characterization of COVID-19 hospitalized patients

The paper reports Whole Exome Sequencing analysis of 35 hospitalized COVID-19 patients, identifying pathogenic mutations linked to virus infection susceptibility and disease progression.

ACE2 gene variants may underlie interindividual variability and susceptibility to COVID-19 in the Italian population

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