Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation
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PRIMARY RESEARCH
Open Access
Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation Christopher Toh and James P. Brody*
Abstract Introduction: The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection. Methods: We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification. Results: We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10−11) as measured against a randomized control and (p = 3 · 10−14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test. Conclusion: Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity. Keywords: COVID-19, Genetic risk score, UK biobank, Machine learning
Introduction The course of COVID-19 varies from asymptomatic to severe (acute respiratory distress, cytokine storms, and death) in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response to infection. Human genetic variation can affect susceptibility and resistance to viral infections [1]. For instance, variants in the gene IFITM3 affect the severity of seasonal influenza
[2]. Patients hospitalized from seasonal influenza had a particular allele of the gene IFITM3 at a higher rate than expected from the general population. Laboratory work determined that this particular allele can alter the course of the influenza virus infection. We have previously shown that chromosomal-scale length variation is a powerful tool to analyze genomewide associations [3]. This method is particularly appealing for genetic risk scores because it includes epistatic effects that might be missed with conventional genomewide association studies. Others have used machine
* Correspondence: [email protected] Department of Biomedical Engineering, University of California, Irvine, USA © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 Internation
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