A Chinese host genetic study discovered IFNs and causality of laboratory traits on COVID-19 severity
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SciScore for 10.1101/2021.06.04.21258335: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Written informed consent was obtained from all participants, as approved by the Medical Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Phenotype: There are two types of phenotypes: laboratory and clinical measurements.
IRB: Written informed consent was obtained from all participants, as approved by the Medical Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Phenotype: There are two types of phenotypes: laboratory and clinical measurements.Sex as a biological variable not detected. Randomization Given the potential genetic correlation between these features and the … SciScore for 10.1101/2021.06.04.21258335: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Written informed consent was obtained from all participants, as approved by the Medical Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Phenotype: There are two types of phenotypes: laboratory and clinical measurements.
IRB: Written informed consent was obtained from all participants, as approved by the Medical Ethics Committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology. Phenotype: There are two types of phenotypes: laboratory and clinical measurements.Sex as a biological variable not detected. Randomization Given the potential genetic correlation between these features and the COVID-19 susceptibility and severity, we performed two-sample Mendelian randomization analyses to examine causal effects between them and uncover genetic variants that determined disease status by acting on the laboratory traits. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources Based on the VCF files after VQSR with biallelic variants, imputation was performed with Beagle v4.0 51 taking GL as input in east Asian (EAS) population of 1000 Genomes Project as reference panel. Beaglesuggested: (BEAGLE, RRID:SCR_001789)1000 Genomes Projectsuggested: (1000 Genomes Project and AWS, RRID:SCR_008801)Genome-wide association studies: We used PLINK v2.0 52 to perform single-variant GWAS analyses using a linear regression model for the quantitative laboratory features under the assumption of additive allelic effects of the SNP dosage. PLINKsuggested: (PLINK, RRID:SCR_001757)We used R (version 4.0.2) with the TwoSampleMR package 55,56 and set the significance threshold at the level of 0.05. TwoSampleMRsuggested: (TwoSampleMR, RRID:SCR_019010)We used six existing gene set databases, including GO (gene ontology) molecular function 58, GO cellular component 58, GO biological process 58, KEGG (Kyoto encyclopedia of genes and genomes) 59, Reactome 60, and WikiPathways 61. KEGGsuggested: (KEGG, RRID:SCR_012773)WikiPathwayssuggested: (WikiPathways, RRID:SCR_002134)Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Despite the many compelling discoveries of our work, there are still a few limitations. First, the single-variant GWAS analysis of severity status did not identify any genome-wide signals due to the current sample size (N = 466) and thus small genetic effect sizes. We believe that large-scale case-control studies have potentials to uncover genome-wide significant variants. Second, even though our GWAS analysis of laboratory features produced concrete and powerful signals, there are still many more traits without being detected associated variants that reach the study-wide or the genome-wide significance threshold. For the identified associations, after SNPs clumping and pruning, there is merely one independent strong variant, while the tested traits were often known as polygenic. This is still due to small sample size restriction. Third, despite the fact that our MR findings are supported by solid biological mechanisms and also potentially replicated by many populations, the causal significance could be different among different HGI phenotypes. For example, when testing the causal effects of LDL-C based on BBJ database, with the outcome of covid vs. population in all population without UKBB, the p-value is 0.01; while for the outcome of hospitalized covid vs. population in all population without UKBB, the p-value is 0.98. We consider this phenomenon directly relating to the corresponding HGI GWAS summary results and further investigations are needed to explain the intrinsic b...
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.
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