Genetic Examination of Hematological Parameters in SARS-CoV-2 Infection and COVID-19
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Abstract
Background
People hospitalized with COVID-19 often exhibit hematological alterations, such as lower lymphocyte and platelet counts, which have been reported to associate with disease prognosis. It is unclear whether inter-individual variability in baseline hematological parameters prior to acute infection influences risk of SARS-CoV-2 infection and progression to severe COVID-19.
Methods
We assessed the association of blood cell counts and indices with incident SARS-CoV-2 infection and severe COVID-19 in UK Biobank and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD). Since genetically determined blood cell measures better represent cell abundance across the lifecourse, we used summary statistics from genome-wide association studies to assess the shared genetic architecture of baseline blood cell counts and indices on COVID-19 outcomes.
Results
We observed inconsistent associations between measured blood cell indices and both SARS-CoV-2 infection and COVID-19 hospitalization in UK Biobank and VUMC SD. In Mendelian randomization analyses using genetic summary statistics, no putative causal relationships were identified between COVID-19 related outcomes and hematological indices after adjusting for multiple testing. We observed overlapping genetic association signals between hematological parameters and COVID-19 traits. For example, we observed overlap between infection susceptibility-associated variants at PPP1R15A and red blood cell parameters, and between disease severity-associated variants at TYK2 and lymphocyte and platelet phenotypes.
Conclusions
We did not find convincing evidence of a relationship between baseline hematological parameters and susceptibility to SARS-CoV-2 infection or COVID-19 severity, though this relationship should be re-examined as larger and better-powered genetic analyses of SARS-CoV-2 infection and severe COVID-19 become available.
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SciScore for 10.1101/2022.02.28.22271562: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The QualityLab pipeline was used to extract and clean laboratory values for over 275 million observations across 1.5 million patients as previously described [63]. QualityLabsuggested: NoneAnalysis of Coincident Genetic Association Signals: We assessed evidence for coincident genetic association signals at COVID-19 loci which had been reported as associated with blood cell traits in a previous GWAS [23, 29]. Coincident Genetic Association Signalssuggested: NoneResults from OddPub: We did not detect open …
SciScore for 10.1101/2022.02.28.22271562: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources The QualityLab pipeline was used to extract and clean laboratory values for over 275 million observations across 1.5 million patients as previously described [63]. QualityLabsuggested: NoneAnalysis of Coincident Genetic Association Signals: We assessed evidence for coincident genetic association signals at COVID-19 loci which had been reported as associated with blood cell traits in a previous GWAS [23, 29]. Coincident Genetic Association Signalssuggested: NoneResults 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: We detected the following sentences addressing limitations in the study:[56] We would highlight several key limitations in this work. First, the time between SARS-CoV-2 infection/COVID-19 hospitalizations and blood cell measurements were variable among individuals and different in UKB. We have attempted to account for this, but also emphasize that external factors (diseases, diet changes, etc) may alter hematopoiesis and measured blood cell counts, in contrast to genetic factors associated with these traits, where alleles are assigned at birth. Second, the measures we examined here are those from readily obtained peripheral blood cell counts, but there are other interesting hematological measures which are less frequently assessed in large populations. For example, specific lymphocyte subsets (such as naïve or memory cells) may be relevant to COVID-19 but could not be assessed here. Third, the power of MR analysis is still limited for COVID-19 genetics, even with the coordinating efforts of the HGI meta-analysis. We conducted a MR power analysis at (α = 0.05/87) using the case and control counts from the HGI meta-analysis and the minimum and maximum estimated heritability from the LD Score Regression analysis for our blood cell traits (h2 range: 0.04 -0.3, Supplementary Figure 1). The limited heritability explained by the variants identified in the HGI meta-analyses causes challenges in using both genetic correlation and MR analysis methods. There is a more up to date version of the HGI meta-analysis which includes more samples (Release 6 [57]), ...
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.
- No funding statement was detected.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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