期刊
HUMAN GENETICS
卷 141, 期 1, 页码 147-173出版社
SPRINGER
DOI: 10.1007/s00439-021-02397-7
关键词
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资金
- Intesa San Paolo [B/2020/0119]
- Italian Ministry of University and Research
- Istituto Buddista Italiano Soka Gakkai [2020-2016_ RIC_3]
- Sepsis Research (the Fiona Elizabeth Agnew Trust)
- Intensive Care Society
- Wellcome-Beit Prize award [103258/Z/13/A]
- BBSRC [BBS/E/D/20002172, BBS/E/D/10002070, BBS/E/D/30002275]
- Canadian Institutes of Health Research [CIHR: 365825, 409511, 100558]
- McGill Interdisciplinary Initiative in Infection and Immunity (MI4)
- Lady Davis Institute of the Jewish General Hospital
- Jewish General Hospital Foundation
- Canadian Foundation for Innovation
- NIH Foundation
- Cancer Research UK [C18281/A29019]
- Genome Quebec
- Public Health Agency of Canada
- McGill University
- Fonds de Recherche Quebec Sante (FRQS)
- FRQS Merite Clinical Research Scholarship
- Calcul Quebec
- Compute Canada
- Welcome Trust
- Medical Research Council
- European Union
- National Institute for Health Research (NIHR)
- Eli Lilly
- GlaxoSmithKline
- Biogen
- European Union's Horizon 2020 research and innovation program [824110]
- SciLifeLab/KAW national Covid-19 research program project [KAW 2020.0182, KAW 2020.0241]
- Swedish Research Council [2014-02569, 2014-07606]
- Knut and Alice Wallenberg Foundation
- King's College London
- Wellcome Trust [103258/Z/13/A] Funding Source: Wellcome Trust
- Biotechnology and Biological Sciences Research Council [BBS/E/D/10002070, BBS/E/D/30002275, BBS/E/D/20002172] Funding Source: researchfish
The study developed a machine-learning model using common and rare exonic variants to predict COVID-19 severity. By selecting key Boolean features and combining them into an Integrated PolyGenic Score, the model offers insights into the genetic contribution to disease severity. A quarter of the selected genes are sex-specific, and pathway analysis highlighted the multi-organ nature of COVID-19 severity. This model could potentially aid in diagnostics, therapeutics, and disease management at the bedside.
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 of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.
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