Deep learning identified genetic variants associated with COVID-19 related mortality
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
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. 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 (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.
Article activity feed
-
SciScore for 10.1101/2022.05.05.22274731: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Demographic variables: Age and sex (Field ID: 31, 34) are used as confounding variables in regression analyses to eliminate potential bias when testing the significance of association between super variants and the phenotype (Fig 4 B&C). Field IDsuggested: NoneStatistical analyses were conducted with Python 3.9.0. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the …SciScore for 10.1101/2022.05.05.22274731: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Demographic variables: Age and sex (Field ID: 31, 34) are used as confounding variables in regression analyses to eliminate potential bias when testing the significance of association between super variants and the phenotype (Fig 4 B&C). Field IDsuggested: NoneStatistical analyses were conducted with Python 3.9.0. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
