ArticleGenetic epidemiology2023
Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.
Article in Genetic epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification and validation of supervariants reveal novel loci associated with human white matter microstructure.Genome research · 2024Pooled it
- Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.Communications medicine · 2025Article
- Peripheral Blood Omics and Other Multiplex-based Systems in Pulmonary and Critical Care Medicine.American journal of respiratory cell and molecular biology · 2023Article
- Pros and cons of Mendelian randomization.Fertility and sterility · 2023Review
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Authors and funding
5 authors.
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Abstract
Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10
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