The GEN-COVID Multicenter Study was established to collect and systematize biological samples and clinical data from multiple Italian hospitals to make them globally available for COVID-19 research. The study leverages the GEN-COVID Biobank (GCB), GEN-COVID Patient Registry (GCPR), and GEN-COVID Genetic Data Repository (GCGDR) to achieve this goal. Data are managed according to FAIR principles for optimal accessibility and interoperability. The COVID-19 pandemic posed a significant challenge to healthcare systems, highlighting the urgent need for efficient data collection and sharing. This study specifically focuses on collecting data that reveals the multisystemic nature of COVID-19, including genetic determinants of virus-host interaction and disease severity. The aim is to use this data to identify multi-organ involvement in COVID-19, define genetic factors influencing infection susceptibility and severity, and ultimately contribute to improved diagnostics, prognostics, and personalized treatments.

The introduction cites several sources to establish the context of COVID-19's impact and heterogeneity of clinical presentation. It emphasizes the importance of rapid data collection and the utility of a well-organized biobank for accelerating research. Specific mention is made of the need to investigate the ACE2 gene's role in the Italian population.

The GEN-COVID Multicenter Study involves 22 Italian hospitals and aims to sequence DNA from 2000 patients (WES by University of Siena), perform genotyping on 2000 patients (GWAS by Institute for Molecular Medicine of Finland), and associate genetic data with disease severity. Data are shared through the GEN-COVID consortium and the Network of Italian Genome (NIG). The study includes PCR-positive SARS-CoV-2 patients aged 18+ with informed consent. Data collection includes socio-demographic information, family history, pre-existing conditions, symptoms, and more than 150 clinical variables. Biological samples (whole blood, plasma, serum, leukocytes) are collected and processed according to standard protocols. Genomic DNA is extracted, and WES and genotyping using Illumina platforms are performed. Data analysis includes descriptive statistics, Chi-square tests, and linear regression to evaluate associations between clinical severity and various factors. The study also includes collection of laboratory data such as PaO2/FiO2 ratio, cardiac markers, pancreatic enzymes, kidney function tests, immune cell counts, inflammatory markers, and D-dimer levels.

As of July 16, 2020, data from 1033 individuals were collected, representing a range of disease severity. Clustering analysis identified five main clinical categories: severe multisystemic failure (with thromboembolic or pancreatic variants), cytokine storm (with or without liver involvement), moderate heart failure (with or without liver damage), moderate multisystemic involvement (with or without liver damage), and mild illness (with or without hypoxia). More than 150 clinical variables were collected and analyzed. Statistical tests were used to assess the association between disease severity and various clinical and demographic variables.

The findings highlight the multi-systemic nature of COVID-19 and the variability in clinical presentation. The comprehensive data collected provide valuable resources for identifying genetic and clinical factors associated with disease susceptibility and severity. The systematic approach to data collection and management, adhering to FAIR principles, facilitates data sharing and collaboration among researchers worldwide. This is crucial for accelerating research and developing improved diagnostic, prognostic, and therapeutic strategies for COVID-19.

The GEN-COVID Multicenter Study provides a valuable resource for COVID-19 research by systematically collecting and analyzing clinical and genetic data from a large cohort of Italian patients. The identification of distinct clinical categories and the availability of comprehensive data through the Network for Italian Genomes will facilitate further research into the genetic and clinical determinants of COVID-19 severity and will aid in the development of personalized treatment strategies. Future research could focus on expanding the cohort size, performing functional studies to validate identified genetic associations, and integrating data from other omics platforms.

The study is limited by its focus on the Italian population, which may not be fully generalizable to other populations. While the data collection is extensive, there may be unmeasured confounding factors that influence the observed associations. The study's findings are based on an interim analysis and may change as more data become available.