| Title: | Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank |
| Journal: | Genetic Epidemiology |
| Published: | 24 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36691909/ |
| DOI: | https://doi.org/10.1002/gepi.22515 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006374 |
| Citations: | 3 (3 in last 2 years) as of 8 Aug 2024 |
Publication 10998
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.</p>
5 Authors
- Zihuan Liu
- Wei Dai
- Shiying Wang
- Yisha Yao
- Heping Zhang
1 Application
| Application ID | Title |
|---|---|
| 42009 | Genetic study of complex phenotypes |
Enabling scientific discoveries that improve human health