ArticleHuman genomics2020
Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.
Article in Human genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Identifying direct risk factors in UK Biobank via simultaneous Bayesian-frequentist model-averaged hypothesis testing using Doublethink.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A compact encoding of the genome suitable for machine learning prediction of traits and genetic risk scores.BioData mining · 2025Article
- Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer.Human genomics · 2023Article
- Can machines learn the mutation signatures of SARS-CoV-2 and enable viral-genotype guided predictive prognosis?Journal of molecular biology · 2022Article
- Review
- The Need for a Human Pangenome Reference Sequence.Annual review of genomics and human genetics · 2021Review
- The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.Frontiers in artificial intelligence · 2021Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsWe 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.
resultsWe found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10
conclusionGenetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.
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Registered trials
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