Post-mendelian genetic model in COVID-19

Host genetics is an emerging theme in COVID-19. A handful of common polymorphisms and some rare variants have been identified, either through GWAS or candidate gene approach, respectively. However, an organic model is still missing. Here, we propose a model that takes into account both common and rare coding variants. This model has been piloted in a cohort of 1,318 Italian SARS-CoV-2 positive individuals. Ordered logistic regression of clinical WHO grading on age, stratified by sex, was used to obtain a binary phenotypic classification useful for subsequent data mining. Genetic variability from WES was synthesized in several Boolean representations differentiated according to allele frequencies and genotype effect. LASSO logistic regression was used for extracting relevant features, part of them being sex specific. We defined a set of common coding polymorphisms relevant for COVID-19 severity. Another set of rare coding variants were found to contribute either to mildness or severity of COVID-19, which in some cases simulate a Mendelian inheritance. The combined effect of common and rare variants can be described as an Integrated PolyGenic Score (IPGS). This score can be computed using the following formula: (nmildness-nseverity) +F (mmildness-mseverity). Here n is the number of common driver genes, m is the number of driver rare variants, and F is a factor for an appropriate weighing of the more powerful rare variants. We named this model: “post-Mendelian”. The model has been tested with multiple train-test splits and the polygenic score accurately separated patients with severe COVID-19 from those having a mild form. Furthermore, segregation of IPGS can be observed in familial cases in which more than one infected individual is present.