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

ABSTRACT: The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution

SUBMITTER: Fallerini C 

PROVIDER: S-EPMC8661833 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature