Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.

Journal: Human genomics

PMID: 33036646

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.

Authors

Christopher Toh

Department of Biomedical Engineering, University of California, Irvine, USA.
James P Brody

Department of Biomedical Engineering, University of California, Irvine, USA. jpbrody@uci.edu.

Keywords

Algorithms Betacoronavirus Case-Control Studies Chromosome Aberrations Chromosomes, Human Coronavirus Infections COVID-19 Datasets as Topic Humans Machine Learning Pandemics Pneumonia, Viral Risk Factors SARS-CoV-2 Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (33036646)

Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.

Abstract

Authors

Keywords

External Resources

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