An Explainable Host Genetic Severity Predictor Model for COVID-19 Patients

Abstract

Understanding the COVID-19 severity and why it differs significantly among patients is a thing of concern to the scientific community. The major contribution of this study arises from the use of a voting ensemble host genetic severity predictor (HGSP) model we developed by combining several state-of-the-art machine learning algorithms (decision tree-based models: Random Forest and XGBoost classifiers). These models were trained using a genetic Whole Exome Sequencing (WES) dataset and clinical covariates (age and gender) formulated from a 5-fold stratified cross-validation computational strategy to randomly split the dataset to overcome model instability. Our study validated the HGSP model based on the 18 features (i.e., 16 identified candidate genetic variants and 2 covariates). We provided post-hoc model explanations through the ExplainerDashboard - an open-source python library framework, allowing for deeper insight into the prediction results. We applied the Enrichr and OpenTarget genetics bioinformatic interactive tools to associate the genetic variants for plausible biological insights, and domain interpretations such as pathways, ontologies, and disease/drugs. Through an unsupervised clustering of the SHAP feature importance values, we visualized the complex genetic mechanisms. Our findings show that while age and gender mainly influence COVID-19 severity, a specific group of patients experiences severity due to complex genetic interactions.

Competing Interest Statement

The authors have declared no competing interest.

Clinical Protocols

https://github.com/raimondilab/COVID-19-severity-host-genetic-predictor-model-explanation

Funding Statement

Intesa San Paolo for the 2020 charity fund dedicated to the project N B/2020/0119 Identificazione delle basi genetiche determinanti la variabilita clinica della risposta a COVID-19 nella popolazione italiana. The EU project H2020-SC1-FA-DTS-2018-2020, entitled International consortium for integrative genomics prediction (INTERVENE) - Grant Agreement No. 101016775.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The dataset used for this study was part of the GEN-COVID Multicenter Study, https://sites.google.com/dbm.unisi.it/gen-COVID. The Italian multicenter study aimed at identifying the COVID-19 host genetic bases. Specimens were provided by the COVID-19 Biobank of Siena, which is part of the Genetic Biobank of Siena, a member of BBMRI-IT, of Telethon Network of GeneticBiobanks (project no. GTB18001), of EuroBioBank, and RD-Connect. Further information on the cleansed dataset and codes are available on our Githhub group page at: https://github.com/raimondilab/COVID-19-severity-host-genetic-predictor-model-explanation

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