Are psychiatric disorders risk factors for COVID-19 susceptibility and severity? a two-sample, bidirectional, univariable, and multivariable Mendelian Randomization study
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Abstract
Observational studies have suggested bidirectional associations between psychiatric disorders and COVID-19 phenotypes, but results of such studies are inconsistent. Mendelian Randomization (MR) may overcome the limitations of observational studies, e.g., unmeasured confounding and uncertainties about cause and effect. We aimed to elucidate associations between neuropsychiatric disorders and COVID-19 susceptibility and severity. To that end, we applied a two-sample, bidirectional, univariable, and multivariable MR design to genetic data from genome-wide association studies (GWASs) of neuropsychiatric disorders and COVID-19 phenotypes (released in January 2021). In single-variable Generalized Summary MR analysis, the most significant and only Bonferroni-corrected significant result was found for genetic liability to BIP-SCZ (a combined GWAS of bipolar disorder and schizophrenia as cases vs. controls) increasing risk of COVID-19 (OR = 1.17, 95% CI, 1.06–1.28). However, we found a significant, positive genetic correlation between BIP-SCZ and COVID-19 of 0.295 and could not confirm causal or horizontally pleiotropic effects using another method. No genetic liabilities to COVID-19 phenotypes increased the risk of (neuro)psychiatric disorders. In multivariable MR using both neuropsychiatric and a range of other phenotypes, only genetic instruments of BMI remained causally associated with COVID-19. All sensitivity analyses confirmed the results. In conclusion, while genetic liability to bipolar disorder and schizophrenia combined slightly increased COVID-19 susceptibility in one univariable analysis, other MR and multivariable analyses could only confirm genetic underpinnings of BMI to be causally implicated in COVID-19 susceptibility. Thus, using MR we found no consistent proof of genetic liabilities to (neuro)psychiatric disorders contributing to COVID-19 liability or vice versa, which is in line with at least two observational studies. Previously reported positive associations between psychiatric disorders and COVID-19 by others may have resulted from statistical models incompletely capturing BMI as a continuous covariate.
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SciScore for 10.1101/2020.11.29.20240481: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: The GWAS summary statistics we used were drawn from studies that had obtained written informed consent from participants and received ethical approval from institutional review boards. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources For anxiety and ADRS, we performed meta-analysis in METAL 17 excluding UKBB participants: of anxiety using the iPSYCH (4,584 cases and 19,225 controls) and ANGST cohorts (7,016 cases and 14,745 controls) and of ASRD also using the iPSYCH cohort (4,584 cases and 19,225 controls) and ANGST cohorts … SciScore for 10.1101/2020.11.29.20240481: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement Consent: The GWAS summary statistics we used were drawn from studies that had obtained written informed consent from participants and received ethical approval from institutional review boards. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources For anxiety and ADRS, we performed meta-analysis in METAL 17 excluding UKBB participants: of anxiety using the iPSYCH (4,584 cases and 19,225 controls) and ANGST cohorts (7,016 cases and 14,745 controls) and of ASRD also using the iPSYCH cohort (4,584 cases and 19,225 controls) and ANGST cohorts (12,665 cases and 14,745 controls). METALsuggested: (METAL, RRID:SCR_002013)At last, we conducted multivariable MR(MVMR) analyses 25 using the MendelianRandomization R package to examine which phenotypes remained risk factors taking into account pleiotropic effects among exposures. MendelianRandomizationsuggested: NoneResults from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:However, our results should also be interpreted in light of several limitations. First, a general concern in MR studies is risk of sample overlap. We minimized chances of sample overlap between exposure datasets and outcome datasets by excluding UKBB populations from (neuro)psychiatric GWASs and by excluding 23andme cohorts from COVID-19 datasets. Nonetheless, cryptic relatedness and potential sample overlap between exposure and outcome GWASs may result in some degree of instrument bias. However, the F-statistics we found were all above 36, allaying major concerns about weak instrument bias. Another limitation directly follows from the availability of GWAS data. For some phenotypes, such as obsessive-compulsive disorder and anorexia nervosa, no large datasets excluding the UKBB were available at the time of analysis or writing. For MDD and SCZ, summary statistics of larger GWASs may become available in the coming year. Similarly, as COVID-19 GWAS sample sizes ramp up, statistical power in MR analysis may increase. We encourage researchers to repeat MR analyses on other phenotypes and to use such larger GWAS datasets once they become available. To that end, we have uploaded our code to Github (see data availability section above). In conclusion, we provide converging evidence for slightly increased susceptibility to and severity of COVID-19 in those with genetic liability to bipolar disorder. Odds ratios and direction of effect for genetic liability to schizophrenia were simil...
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.
- Thank you for including a protocol registration statement.
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SciScore for 10.1101/2020.11.29.20240481: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement The GWAS summary statistics we used were drawn from studies that had obtained written informed consent from participants and received ethical approval from institutional review boards. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources ● ● ● 0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.00 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● −0.10 D1 (b zy ) ● ● ● IVW weighted median MR−Egger GSMR PRESSO −0.15 −0.10 −0.05 0.00 0.05 0.10 … SciScore for 10.1101/2020.11.29.20240481: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement The GWAS summary statistics we used were drawn from studies that had obtained written informed consent from participants and received ethical approval from institutional review boards. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Experimental Models: Organisms/Strains Sentences Resources ● ● ● 0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.00 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● −0.10 D1 (b zy ) ● ● ● IVW weighted median MR−Egger GSMR PRESSO −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 BIPSCZ (b zx ) Figure 2 A. ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● −0.10 D1suggested: NoneSoftware and Algorithms Sentences Resources For anxiety and ADRS, we performed meta-analysis in METAL 17 excluding UKBB participants: of anxiety using the iPSYCH (4,584 cases and 19,225 controls) and ANGST cohorts (7,016 cases and 14,745 controls) and of ASRD also using the iPSYCH cohort (4,584 cases and 19,225 controls) and ANGST cohorts (12,665 cases and 14,745 controls). METALsuggested: (METAL, RRID:SCR_002013)At last, we conducted multivariable MR(MVMR) analyses 25 using the MendelianRandomization R package to examine which phenotypes remained risk factors taking into account pleiotropic effects among exposures. MendelianRandomizationsuggested: NoneResults from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
However, our results should also be interpreted in light of several limitations. First, a general concern in MR studies is risk of sample overlap. We minimized chances of sample overlap between exposure datasets and outcome datasets by excluding UKBB populations from (neuro)psychiatric GWASs and by excluding 23andme cohorts from COVID-19 datasets. Nonetheless, cryptic relatedness and potential sample overlap between exposure and outcome GWASs may result in some degree of instrument bias. However, the F-statistics we found were all above 36, allaying major concerns about weak instrument bias. Another limitation directly follows from the availability of GWAS data. For some phenotypes, such as obsessive-compulsive disorder and anorexia nervosa, no large datasets excluding the UKBB were available at the time of analysis or writing. For MDD and SCZ, summary statistics of larger GWASs may become available in the coming year. Similarly, as COVID-19 GWAS sample sizes ramp up, statistical power in MR analysis may increase. We encourage researchers to repeat MR analyses on other phenotypes and to use such larger GWAS datasets once they become available. To that end, we have uploaded our code to Github (see data availability section above). In conclusion, we provide converging evidence for slightly increased susceptibility to and severity of COVID-19 in those with genetic liability to bipolar disorder. Odds ratios and direction of effect for genetic liability to schizophrenia were simil...
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.
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.
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