Correlation between host’s genetic profile and COVID-19 infection



196.000 €


3 years


Interdisciplinary skills and high innovation for personalized patient care in the event of future Coronavirus pandemics

Interdisciplinary skills and high innovation for personalized patient care in the event of future Coronavirus pandemics

In the context of the current COVID-19 pandemic, the Medical Genetics Unit has launched a research project on the study of the genetic profile of the host in order to identify variants of susceptibility to COVID-19 infection and at the same time also protective variants, related both to the risk of contagion and to the probability of developing more or less severe symptoms.

Through similar studies, other international groups have recently identified genetic variants of susceptibility to COVID-19, precisely in some genes and in the locus determining the blood group.

The Medical Genetics Research Unit of the Campus Bio-Medico University of Rome intends to devote itself to the continuation of this project: genotype-phenotype correlation studies will be carried out to evaluate the variability of clinical manifestations at an intra-family level. This will be achieved by identifying families with symptom variability with the same exposure and subsequent genotyping.

The study is expected to reconfirm known genetic profiles but also to identify new genetic factors of susceptibility or protection from coronavirus infection. Furthermore, given the current availability of different vaccines, our study may be useful for understanding and predicting the individual response to COVID vaccination. It is in fact expected that 10% of the population will not be responsive to vaccination and remain at high risk of contagion and of developing severe symptoms. In addition, knowledge of the genetic profile of the host may be important not only to acquire knowledge on the pathogenesis of the infection to implement a disease modeling on which to test compounds for therapeutic purposes, but also to
develop personalized risk and prevention predictions.
The use of artificial intelligence allows us to integrate different variables (both genetic and clinical) and to elaborate machine learning algorithms.

The project is extremely innovative – i.e. by virtue of being an application and principle of the modern concept of precision medicine and because it integrates knowledge and IT methodology that are increasingly entering clinical and laboratory diagnosis –unique and interdisciplinary, thanks to the engineering skills, physiological, genetic, clinical and pharmacological.