Vitamin D and COVID-19 susceptibility and severity in the COVID-19 Host Genetics Initiative: A Mendelian randomization study.

BACKGROUND: Increased vitamin D levels, as reflected by 25-hydroxy vitamin D (25OHD) measurements, have been proposed to protect against COVID-19 based on in vitro, observational, and ecological studies. However, vitamin D levels are associated with many confounding variables, and thus associations described to date may not be causal. Vitamin D Mendelian randomization (MR) studies have provided results that are concordant with large-scale vitamin D randomized trials. Here, we used 2-sample MR to assess evidence supporting a causal effect of circulating 25OHD levels on COVID-19 susceptibility and severity. METHODS AND
FINDINGS: Genetic variants strongly associated with 25OHD levels in a genome-wide association study (GWAS) of 443,734 participants of European ancestry (including 401,460 from the UK Biobank) were used as instrumental variables. GWASs of COVID-19 susceptibility, hospitalization, and severe disease from the COVID-19 Host Genetics Initiative were used as outcome GWASs. These included up to 14,134 individuals with COVID-19, and up to 1,284,876 without COVID-19, from up to 11 countries. SARS-CoV-2 positivity was determined by laboratory testing or medical chart review. Population controls without COVID-19 were also included in the control groups for all outcomes, including hospitalization and severe disease. Analyses were restricted to individuals of European descent when possible. Using inverse-weighted MR, genetically increased 25OHD levels by 1 standard deviation on the logarithmic scale had no significant association with COVID-19 susceptibility (odds ratio [OR] = 0.95; 95% CI 0.84, 1.08; p = 0.44), hospitalization (OR = 1.09; 95% CI: 0.89, 1.33; p = 0.41), and severe disease (OR = 0.97; 95% CI: 0.77, 1.22; p = 0.77). We used an additional 6 meta-analytic methods, as well as conducting sensitivity analyses after removal of variants at risk of horizontal pleiotropy, and obtained similar results. These results may be limited by weak instrument bias in some analyses. Further, our results do not apply to individuals with vitamin D deficiency.
CONCLUSIONS: In this 2-sample MR study, we did not observe evidence to support an association between 25OHD levels and COVID-19 susceptibility, severity, or hospitalization. Hence, vitamin D supplementation as a means of protecting against worsened COVID-19 outcomes is not supported by genetic evidence. Other therapeutic or preventative avenues should be given higher priority for COVID-19 randomized controlled trials.

Introduction

SARS-CoV-2 infection has killed millions of individuals and has led to the largest economic contraction since the Great Depression [1]. Therefore, therapies are required to treat severe COVID-19 and to prevent its complications. Therapeutic development, in turn, requires well-validated drug targets to lessen COVID-19 severity.

Recently, vitamin D status, as reflected by 25-hydroxy vitamin D (25OHD) level has been identified as a potentially actionable drug target in the prevention and treatment of COVID-19 [2]. As the pre-hormone to the biologically active form calcitriol, 25OHD has been epidemiologically linked to many health outcomes [3,4]. Given calcitriol’s recognized in vitro immunomodulatory role [5], as well as observational and ecological studies associating measured 25OHD blood levels with COVID-19 [6,7], the vitamin D pathway might be a biologically plausible target in COVID-19. This could be of public health importance, given that the prevalence of vitamin D insufficiency is high in most countries, and that more than 37% of elderly adults in the US take vitamin D supplements [8]. Further, 25OHD supplementation is inexpensive and reasonably safe—thus providing a potential avenue to lessen the burden of the SARS-CoV-2 pandemic.

However, observational studies on 25OHD are prone to confounding and reverse causation bias. Confounding happens when the relationship between the exposure (25OHD) and the outcome (COVID-19) is influenced by unobserved or improperly controlled common causes. Reverse causation happens when the outcome itself is a cause of the exposure. Likewise, conclusions drawn from in vitro may not be applicable in vivo. Accordingly, randomized controlled trials (RCTs) on 25OHD supplementation have been undertaken to test its effect on disease outcomes where observational studies have supported a role for 25OHD level. However, across endocrinology, respirology, cardiology, and other specialties, these trials have most often not demonstrated statistically significant benefits [9-11]. Some RCTs have even shown a detriment to 25OHD supplementation [12]. In the field of infectious diseases, an individual patient data meta-analysis of RCTs of 25OHD supplementation [13] showed some benefit to prevent respiratory tract infections (odds ratio [OR] = 0.80; 95% CI: 0.69, 0.93). However, this effect was driven by generally benign upper respiratory tract infections, was not observed in lower respiratory tract disease (OR = 0.96; 95% CI: 0.83, 1.10), and even showed numerically worse all-cause mortality (OR = 1.39; 95% CI: 0.85, 2.27). Likewise, a recent trial on sepsis obtained a numerically higher mortality rate in patients who received 25OHD supplementation [14]. At present, we are aware of 2 RCTs testing the role of vitamin D supplementation on COVID-19 outcomes, both using high-dose vitamin D given at time of hospital admission for COVID-19. The first RCT [15] was a small trial (n = 75) showing fewer intensive care unit admissions in the vitamin-D-treated arm. However, the follow-up time for mortality varied, and the open-label design put the study at high risk of bias. The second RCT [16] was a larger study (n = 240) using a double-blind design, and showed no effect on mortality, risk of mechanical ventilation, and length of stay. Nevertheless, questions remain on the use of pre-illness vitamin D supplementation and its effect on disease susceptibility. While RCTs can control for confounding and provide unbiased estimates of the effect of 25OHD supplementation in COVID-19, large well-designed RCTs require considerable resources and time.

Mendelian randomization (MR) is a genetic epidemiology method that uses genetic variants as instrumental variables to infer the causal effect of an exposure (in this case, 25OHD level) on an outcome (in this case, COVID-19 susceptibility and severity) [17]. MR overcomes confounding bias since genetic alleles are randomized to the individual at conception, thereby breaking associations with most confounders. Similarly, since genetic alleles are always assigned prior to disease onset, they are not influenced by reverse causation. MR has been used in conjunction with proteomics and metabolomics to prioritize drug development and repurposing, and support investment in RCTs that have a higher probability of success [18,19]. In the case of vitamin D, MR has provided causal effect estimates consistently in line with those obtained from RCTs [9,20-24], and supporting the use of vitamin D supplementation in preventing diseases in at-risk individuals (most notably for multiple sclerosis [25]). Hence, MR may support investments in 25OHD supplementation trials in COVID-19, if a benefit was shown. Further, since MR results can be generated rapidly, such evidence may provide interim findings while awaiting RCT results.

However, MR relies on several core assumptions [26]. First, genetic variants must be associated with the exposure of interest. Second, they should not affect the outcome except through effects on the exposure (i.e., they should exhibit a lack of horizontal pleiotropy). Specifically, MR also assumes that the relationship between the exposure and the outcome is linear. However, this assumption is robust to non-liear effects as it will still provide a valid test of the null hypothesis when studying population-level effects [27], as MR then measures the population-averaged effect on the outcome of a shift in the distribution of the exposure. Third, genetic variants should not associate with the confounders of the exposure–outcome relationship. Of these assumptions, the most problematic is the second assumption. Yet, in the case of 25OHD, many of its genetic determinants reside at loci that harbor genes whose roles in 25OHD production, metabolism, and transport are well known [25]. Leveraging this known physiology can help to prevent the incorporation of genetic variants that could lead to horizontal pleiotropy.

Here, we used genetic determinants of serum 25OHD from a recent genome-wide association study (GWAS) and meta-analysis of 443,734 participants of European ancestry [28] in an MR study to test the relationship between increased 25OHD level and COVID-19 susceptibility and severity.

Methods

We used a 2-sample MR approach to estimate the effect of 25OHD levels on COVID-19 susceptibility and severity. In 2-sample MR [29], the effect of genetic variants on 25OHD and on COVID-19 outcomes are estimated in separate GWASs from different populations. This allows for increased statistical power by increasing the sample size in both the exposure and outcome cohorts. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [30] (S1 STROBE Checklist).

Our study did not employ a prospective protocol. Analyses were first planned and performed in July 2020 and updated following peer-review in December 2020. Three major changes were made during the update. First, we used the most up-to-date COVID-19 Host Genetics Initiative (COVID-19 HGI) GWAS summary statistics. These were made available during the peer-review process. Second, to alleviate potential selection and collider bias, we modified the outcome phenotypes to include population controls. We also performed additional MR sensitivity analyses to check the robustness of our results. The latter 2 modifications were made at the request of peer-reviewers. Finally, minor changes to the results’ interpretations were made following further peer-review in February 2021.

Choice of 25OHD genetic instruments

To find genetic variants explaining 25OHD levels [28], we used a GWAS from our group, which is, to the best of our knowledge, the largest published GWAS of 25OHD levels. Importantly, this meta-analysis controlled for season of vitamin D measurement to obtain genetic variants significantly associated with 25OHD levels. From the list of conditionally independent variants provided, we further selected SNPs whose effect on 25OHD level was genome-wide significant (p < 5 × 10−8), whose minor allele frequency was more than 1%, and with linkage disequilibrium coefficients (r2) of less than 5% (using the LDlink [31] tool and the European 1000 Genomes dataset, excluding Finnish populations). For SNPs that were not available in the outcome GWAS or with palindromic alleles of intermediate frequency (between 42% and 58%), we again used the LDlink [31] tool to find genetic proxies in the European 1000 Genomes dataset (excluding Finnish populations) using a linkage disequilibrium r2 of 90% or more.

COVID-19 outcome definitions and GWASs

We used the COVID-19 HGI outcome definitions and GWAS summary statistics for COVID-19 susceptibility, hospitalization, and severe disease outcomes [32]. For all outcomes, a COVID-19 infection defined as a positive SARS-CoV-2 infection (e.g., RNA RT-PCR or serloogy test), electronic health record evidence of SARS-CoV-2 infection (using International Classification of Diseases or physician notes), or self-reported infections from the patients. The susceptibility phenotype compared COVID-19 cases with controls, which were defined as any individuals without a history of COVID-19. The hospitalized outcome compared cases, defined as hospitalized patients with COVID-19, and controls, defined as any individuals not experiencing a hospitalization for COVID-19, which includes those without COVID-19. The severe disease outcome cases were defined as hospitalized individuals with COVID-19 who died or required respiratory support. Respiratory support was defined as intubation, continuous positive airway pressure (CPAP), bilevel positive airway pressure (BiPAP), continuous external negative pressure, or high-flow nasal cannula. Controls for the severe COVID-19 outcome were defined as individuals without severe COVID-19 (including those without COVID-19). The inclusion of COVID-19-negative participants as controls in each outcome decreases the possibility of collider bias [33] and allows for better population-level comparisons. These 3 outcome phenotypes are referred to as C2, B2, and A2, respectively, in the COVID-19 HGI documentation.

For our study, we used the 20 October 2020 (v4) COVID-19 HGI fixed effect meta-analysis of GWASs from up to 22 cohorts, performed in up to 11 countries. Every participating cohort was asked to provide summary statistics from a GWAS on the above 3 outcomes, and including the non-genetic covariates age, sex, age × age, and age × sex; 20 genetic principal components; and any locally relevant covariates at the discretion of participating studies (e.g., hospital, genotype panel). Cohorts were asked to follow common sample and variant quality control, and performed analysis only if they enrolled 100 cases or more. Analyses were done separately for each major ancestry group to further control for population stratification. For the purposes of our study, we used the meta-analysis results from European ancestry cohorts, except for the severe COVID-19 outcome, for which this meta-analysis was not available. Further details on the 3 phenotypes and participating cohorts are found in Table 1 and S1 Data.

Table 1

Sources of data for the analysis.

Phenotype	Source of genetic variants
	Cohort	Participants
25OHD circulating levels	Manousaki et al. [28]	Meta-analysis of 2 25OHD GWASs:• 401,460 adult white British participants from the UKB• 42,274 from an international consortium of adult individuals of European ancestry
COVID-19 susceptibility	Susceptibility	Meta-analysis of 22 GWASs performed in individuals of European ancestry from 11 countries:• Cases: 14,134 individuals with COVID-19 by laboratory confirmation, chart review, or self-report• Controls: 1,284,876 individuals without confirmation or history of COVID-19
COVID-19 severity	Hospitalized	Meta-analysis of 13 GWASs performed in individuals of European ancestry from 11 countries:• Cases: 6,406 hospitalized individuals with COVID-19• Controls: 902,088 individuals without hospitalization with COVID-19
	Severe disease	Meta-analysis of 12 GWASs performed in individuals of European ancestry from 9 countries:• Cases: 4,336 SARS-CoV-2-infected hospitalized individuals who died or required respiratory support (intubation, CPAP, BiPAP, continuous external negative pressure, or high-flow nasal cannula)• Controls: 623,902 without severe COVID-19

COVID-19 susceptibility and severity outcomes are taken from the COVID-19 Host Genetics Initiative. See S1 Data for details on cohorts of COVID-19 susceptibility and severity phenotypes.

25OHD, 25-hydroxy vitamin D; BiPAP, bilevel positive airway pressure; CPAP, continuous positive airway pressure; GWAS, genome-wide association study; UKB, UK Biobank.

Primary MR analysis

The effect of 25OHD level on COVID-19 outcomes was obtained for each SNP by using the Wald ratio method. The effect of each SNP was given in standardized log-transformed 25OHD level. Each estimate was meta-analyzed using the inverse-variance weighted (IVW) method, and we performed variant heterogeneity tests to check the robustness of IVW results. Allele harmonization and computations were performed using the TwoSampleMR package [34].

Horizontal pleiotropy sensitivity analysis

We undertook multiple analyses to assess the risk of horizontal pleiotropy (a violation of the second MR assumption). First, we used the MR–Egger method, which allows for an additional intercept (alpha) term, which also provides an estimate of directional horizontal pleiotropy. This method relies upon the assumption that the size of the direct effect of a genetic variant on the outcome that does not operate through the exposure is independent of the variant’s effect on the exposure. Given possible instability in MR–Egger estimates [35], we also used the bootstrap MR–Egger method to meta-analyze the causal effect estimates from each SNP instrument. Further, we used 4 additional meta-analysis methods known to be more robust to the presence of horizontal pleiotropy (at the expense of statistical power): penalized weighted median, simple mode, weighted median, and weighted mode [36].

Second, we restricted our choices of SNPs to those whose closest gene is directly involved in the vitamin D pathway. These genes have an established role in vitamin D regulation through its synthesis (DHCR7/NADSYN1 and CYP2R1), transportation (GC), and degradation (CYP24A1) (S1 Fig). This decreases the risk of selecting a genetic variant that affects COVID-19 outcomes independent of its effect on 25OHD levels.

Third, we used the PhenoScanner tool [37,38] on the remaining SNPs to check for variants associated (at a genome-wide significant threshold of p < 5 × 10−8) with phenotypes at risk of affecting COVID-19 outcomes independent of 25OHD, making them at higher risk of horizontal or vertical pleiotropy. Note that vertical pleiotropy, which happens when the COVID-19 outcome is influenced by a phenotype directly in the causal pathway between 25OHD level and COVID-19 outcome, does not violate MR assumptions.

Research ethics

Each cohort included in this study received its respective institutional research ethics board’s approval to enroll patients. All information used for this study is publicly available as deidentified GWAS summary statistics.

Results

We obtained our 25OHD genetic instruments from our previously published GWAS on circulating 25OHD levels in 401,460 white British participants in the UK Biobank (UKB) [39], which was meta-analyzed with a GWAS on 25OHD levels of 42,274 participants of European ancestry [40]. Of the 138 reported conditionally independent SNPs (explaining 4.9% of the 25OHD variance), 100 had a minor allele frequency of more than 1%, of which 77 were directly available in the COVID-19 HGI GWAS summary statistic and had a linkage disequilibrium coefficient of less than 5%. Additionally, 3 more variants had good genetic proxies (r2 ≥ 90%) and were therefore added to our instrument lists, for a total of 80 variants. These explained 4.0% of the variance in 25OHD serum levels. The full list of SNPs used can be found in S2 Data.

Using the COVID-19 HGI results restricted to cohorts of European ancestry, we used a total of 14,134 cases and 1,284,876 controls to define COVID-19 susceptibility, 6,406 cases and 902,088 controls to define COVID-19 hospitalization, and 4,336 cases and 623,902 controls to define COVID-19 severe disease. Table 1 summarizes the definition and sample size of both the exposure and outcome GWASs. Since the UKB was used in the 2 phases of the MR study, some overlap between the exposure and the outcome GWASs was unavoidable (S1 Data).

We first used IVW meta-analysis to combine effect estimates from each genetic instrument. For a standard deviation increase in log-transformed 25OHD level, we observed no statistically significant effect upon odds of susceptibility (OR = 0.95; 95% CI: 0.84, 1.08; p = 0.44). Of note, in the UKB, the distribution of 25OHD levels has a mean of 48.6 nmol/L and a standard deviation of 21.1 nmol/L. This standard deviation is comparable to what can be achieved with vitamin D supplementation, especially over short therapeutic courses [41]. Similarly, we observed no significant difference in risk of hospitalization (OR = 1.09; 95% CI: 0.89, 1.33; p = 0.41) or risk of severe disease (OR = 0.97; 95% CI: 0.77, 1.22; p = 0.77) associated with a standard deviation increase in log-transformed 25OHD level (Table 2; Fig 1).

Table 2

Mendelian randomization results.

Outcome	Number of SNPs*	IVW OR (95% CI)	IVW p-value	IVW SNP heterogeneity p-value	Egger alpha	Alpha p-value
25OHD primary analysis with all SNPs
Susceptibility	80	0.95 (0.84, 1.08)	0.44	0.009	0.003 (−0.004, 0.009)	0.39
Hospitalization	80	1.09 (0.89, 1.33)	0.41	0.065	0.0004 (−0.010, 0.011)	0.93
Severe disease	80	0.97 (0.77, 1.22)	0.77	0.140	0.008 (−0.004, 0.020)	0.17
25OHD sensitivity analysis restricted to genes in the vitamin D pathway
Susceptibility	11	0.94 (0.81, 1.08)	0.39	0.204	0.002 (−0.024, 0.029)	0.86
Hospitalization	11	1.04 (0.75, 1.46)	0.81	0.003	0.028 (−0.033, 0.089)	0.39
Severe disease	11	0.92 (0.68, 1.25)	0.59	0.117	0.044 (−0.008, 0.096)	0.13
25OHD sensitivity analysis after removal of SNPs identified by PhenoScanner
Susceptibility	9	0.91 (0.71, 1.17)	0.48	0.110	0.002 (−0.034, 0.038)	0.91
Hospitalization	9	1.02 (0.61, 1.73)	0.93	0.008	0.012 (−0.065, 0.089)	0.77
Severe disease	9	1.05 (0.64, 1.73)	0.85	0.127	0.032 (−0.038, 0.103)	0.40

Confidence intervals were obtained using normal approximations.

*Number of SNPs retained for this analysis.

25OHD, 25-hydroxy vitamin D; IVW, inverse-variance weighted; OR, odds ratio; SNP, single nucleotide polymorphism.

Fig 1

Odds ratio point estimates and 95% confidence intervals for the effect of a 1-SD increase in 25OHD levels (on the log scale) on COVID-19 susceptibility and severity.

Restricted to 25-OHD Genes: analysis restricted to SNPs near the 4 genes involved in known vitamin D metabolic pathways. PhenoScanner Filtered: analysis restricted to the 4 genes above, and with removal of SNPs identified as having other associations in PhenoScanner. Full results including odds ratios, confidence intervals, and p-values are available in S1 Table. 25OHD, 25-hydroxy vitamin D; IVW, inverse-variance weighted; MR, Mendelian randomization.

Horizontal pleiotropy assessment and sensitivity analysis

Using the MR–Egger intercept terms, we did not observe evidence of horizontal pleiotropy. While they have less statistical power than IVW meta-analysis, the 6 sensitivity meta-analyses we used also showed no evidence of an association between 25OHD levels and COVID-19 susceptibility, hospitalization, and severe disease, with each confidence interval crossing the null in the primary analysis using all SNPs (Fig 1; S1 Table). Our results are therefore unlikely to be strongly biased by horizontal pleiotropy.

We also restricted our analysis to SNPs that reside close to the 4 genes directly involved in 25OHD metabolism. This left 11 SNPs, explaining 2.9% of 25OHD variation. Using IVW, each standard deviation increase in log-transformed 25OHD was again not associated with COVID-19 susceptibility (OR = 0.94; 95% CI: 0.81, 1.08; p = 0.39), hospitalization (OR = 1.04; 95% CI: 0.75, 1.46; p = 0.81), and severe disease (OR = 0.92; 95% CI: 0.68, 1.25; p = 0.59). For the 3 phenotypes, the MR–Egger intercept term did not support bias from directional horizontal pleiotropy.

Lastly, we used the PhenoScanner [37,38] tool to check if the SNPs used in the MR study were associated with other phenotypes. Using PhenoScanner, rs11723621 was associated with white blood cell level, and rs6127099 was associated with glomerular filtration rate [42,43]. In both cases, the association with each phenotype was mild compared to the SNP’s effect on 25OHD level, as rs11723621 explained less than 0.03% of the variance in white blood cell count, and rs6127099 explained less than 0.001% of the glomerular filtration rate variance. Removing these SNPs from the 11 SNPs above further decreased the proportion of 25OHD variance explained to 1.4%. While confidence intervals widened, effect estimates when restricting our analysis to these SNPs remained null for susceptibility (OR = 0.91; 95% CI: 0.71, 1.17; p = 0.48), hospitalization (OR = 1.02; 95% CI: 0.61, 1.73; p = 0.93), and severe disease (OR = 1.05; 95% CI: 0.64, 1.73; p = 0.85).

Genetic instrument heterogeneity

Overall, our results showed little evidence of heterogeneity of effect between our genetic instruments (Table 2). We nonetheless observed that for at least 1 of the 3 analyses, we would have rejected the null hypothesis of homogeneous genetic effects in the COVID-19 hospitalization phenotype. However, given the large number of hypotheses tested, this may be due to chance.

Discussion

In this large-scale MR study, we did not find evidence to support increasing 25OHD levels in order to protect against COVID-19 susceptibility, hospitalization, or severity. This lack of evidence was consistent across phenotypes, sensitivity analyses, and choice of genetic instruments. Differences between our findings and those reported in observational studies [6] may reflect the fact that associations between vitamin D and COVID-19 may be confounded due to factors difficult to control for even with advanced statistical adjustments, such as socioeconomic status, institutionalizaton, or medical comorbidities associated with lower vitamin D levels. While our study assessed the association between genetically determined levels of 25OHD and COVID-19, these results can still inform us on the role of vitamin D supplementation. Specifically, in contrast to observational studies, our findings do not support an association between higher 25OHD level and better COVID-19 outcome, and therefore do not support the use of vitamin D supplementation to prevent COVID-19 outcomes. Further, while one randomized trial [15] showed a benefit of vitamin D supplementation, it used an endpoint at risk of bias due to the unblinded intervention (admission to the critical care unit) and had a small sample size (n = 75); a larger, double-blinded randomized trial [16] of 240 patients showed no effect of a single high dose of vitamin D3 on mortality, length of stay, or risk of mechanical ventilation. Thus, findings from the largest randomized trial to date are concordant with our MR results.

Our study’s main strength is MR’s track record of predicting RCT outcomes for multiple medical conditions [9-11,21-24,44,45]. Our study also leverages, to our knowledge, the largest cohort of COVID-19 cases and controls currently available (even outside of genetic studies) and the largest study on genetic determinants of 25OHD levels to date. Using these data sources, we were able to obtain results robust to multiple sensitivity analyses.

Our study still has limitations. First, our results do not apply to individuals with vitamin D deficiency, and it remains possible that truly deficient patients may benefit from supplementation for COVID-19-related protection and outcomes. However, individuals who are found to have frank vitamin D deficiency, should undergo replacement for bone protection. Second, our study may suffer from weak instrument bias, especially within sensitivity analyses that restricted to smaller sets of genetic instruments. In 2-sample MR, this bias would tend to make estimates closer to the null. Nonetheless, similar studies have been able to use MR to establish an association between 25OHD levels and other diseases (most notably multiple sclerosis [25]), suggesting that these instruments are strong enough to find such associations. Further, given the large percentage of individuals from the UKB shared between the vitamin D exposure GWASs [28] and the severe COVID-19 phenotype GWASs, this analysis is close to a 1-sample MR, which would show bias towards the observational study association. Given that this analysis also shows largely null effects, we do not suspect that weak instruments bias is a significant issue in our results. Third, given that vitamin D levels are affected by season (with higher levels after sunlight exposure), even if our SNP instruments were obtained from a GWAS that controlled for season of blood draw, effect attenuation by averaging the effect of 25OHD levels on COVID-19 over all seasons may influence results. Nevertheless, a recent study in a Finnish cohort (where sun exposure greatly varies by season) showed that genetic determinants of 25OHD level were able to discriminate between individuals with a predisposition to varying levels of 25OHD, regardless of the season [46]. Therefore, while the cyclical nature of 25OHD level is not completely modeled by MR, the size of this bias is likely small. Fourth, our MR analyses assume a linear exposure–outcome relationship. While this may slightly bias our results, simulation studies have previously shown that this assumption provides adequate results when looking at a population effect [27]. Therefore, for the purpose of vitamin D supplementation in the general population, our conclusions should still be valid. However, as pointed out above, we are not able to test the effect of vitamin D deficiency on COVID-19 outcomes. Lastly, as we only studied the effect of 25OHD and COVID-19 in individuals of European ancestry, it remains possible that 25OHD levels might have different effects on COVID-19 outcomes in other populations. However, previous RCTs on vitamin D supplementation have given similar results in populations of various ancestries [44,45].

In conclusion, using a method that has consistently replicated RCT results from vitamin D supplementation studies in large sample sizes, we find no evidence to support a protective role for higher 25OHD in COVID-19 outcomes. Specifically, vitamin D supplementation as a public health measure to improve COVID-19 outcomes is not supported by this MR study. Most importantly, our results suggest that investment in other therapeutic or preventative avenues should be prioritized for COVID-19 RCTs.

STROBE case–control study checklist.

(DOC)

Click here for additional data file.

Cohorts used for each outcome phenotype for the COVID-19 Host Genetics Initiative.

(DOCX)

Click here for additional data file.

Genetic instrument summary statistics.

(DOCX)

Click here for additional data file.

Vitamin D metabolism pathway and genes involved.

(DOCX)

Click here for additional data file.

Results from Mendelian randomization sensitivity analyses.

(DOCX)

Click here for additional data file.

Acknowledgment of data contributors and the COVID-19 Host Genetics Initiative.

(DOCX)

Click here for additional data file.

GEN-COVID Multicenter Study.

(DOCX)

Click here for additional data file.

4 Sep 2020

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Requests from the editors:

1.Abstract: Please structure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions). Please combine the Methods and Results sections into one section, “Methods and findings”.

2. Abstract: Methods: Please provide some background demographic information on the participants contributing to the GWAS used to obtain variants associated with 25OHD, GWAS for Covid-19.

3. Abstract: Line 85: Please provide the p values associated with the OR along with the 95% CIs.

4. Abstract: Line 86-87: This sentence could be clarified or expanded upon, as it seemed from the results that there was some variation in the findings between the primary and secondary/sensitivity analyses.

5. Abstract: In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

6. Abstract: Conclusions: Please address the study implications without overreaching what can be concluded from the data; the phrase "In this study, we observed ..." may be useful. At lines 91-92, we suggest softening the language/recommendation here-“...individuals should not use vitamin D supplements…” to focus instead on what the data do and do not support (e.g. “...these findings do not provide supporting evidence for the use of vitamin D…”)

7. Author Summary: At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

8. Throughout text: Please use square brackets for in-text citations, like this [1].

9. Methods: Prospective protocol: Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

10. Results: Lines 276-281: Please clarify for the MR Egger estimates of 25OHD and hospitalization that effects did not reach statistical significance, in contrast to what you saw with the primary IVW analysis for hospitalization.

11. Results: Line 273, 287: In these two instances, the term "trend" is used- once where statistical significance is not reached, and once where it is reached. The term “trend” should be used only when the test for trend has been conducted. Please revise accordingly.

12. Results: Line 315: Please clarify if this is the result for susceptibility with the extended phenotypes definition.

13. Results: In presenting the results, it would be helpful to more explicitly point out consistencies and inconsistencies between analyses by clarifying where secondary analyses differed from the main results, although you do allude to this where you mention widening confidence intervals (for example, at line 300-303: “While confidence intervals widened, effect estimates when restricting our analysis to these 22 SNPs remained similar for susceptibility (0.77; 95% CI: 0.48, 1.23; P=0.27), hospitalization (2.89; 95% CI: 1.18, 7.06; P=0.02), and severe disease (2.52; 95% CI: 0.63, 10.0; P=0.19), though this did not reach statistical significance in the latter case.”

14. Discussion: Please slightly re-organize the Discussion as follows: a short, clear summary of the article's findings; what the study adds to existing research and where and why the results may differ from previous research; strengths and limitations of the study; implications and next steps for research, clinical practice, and/or public policy; one-paragraph conclusion.

15. Figure 1: This diagram might be more appropriate as a supporting information file. Please define all abbreviations used in the figure in the Figure legend. It might be helpful to call attention to particular points of the pathway most relevant for your study (e.g. particular genes involved.

16. Figure 2 and supporting information Figure 2.1 (in Supplement 3): Please provide the labels for the X axis.

17. Checklist: Please ensure that the study is reported according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

18. Data availability statement: Thank you for noting that the SNPs are available in published studies. If it is possible, it would be helpful to have a summary supporting information file, or a more specific reference (DOI/accession number) to find the repository genetic instrument SNPs used in your study.

Comments from the reviewers:

Reviewer #1: Overall comments: Thank you very much for the opportunity to review this manuscript entitled "Vitamin D and Covid-19 Susceptibility and Severity: a Mendelian Randomization Study". This is an interesting topic of research, which should be of importance to Covid-19. However, prior to publication, I feel that the manuscript needs some substantial work to evaluate the reliability of the results and explain to the readers how to interpret the results.

The authors used two-sample Mendelian randomization analysis to determine the role of vitamin D status in Covid-19. As the authors suggest, MR is a powerful method for inferring causality, which seems to replace RCTs. However, its careless use can lead to many false results, especially for two-sample Mendelian randomization study. The authors made a conclusion "genetically increased 25OHD levels did not protect against Covid-19 susceptibility, or severity, and in some analyses was associated with worsened outcomes, which support vitamin D supplementation to prevent Covid-19 outcomes". Actually, the essence of MR is used to explore the causal relationship of risk factors to the outcome, but it could not replace RCT to explore the effect of reducing risk factors on the outcome. Therefore, I don't think this conclusion is appropriate. How this finding can guide the impact of vitamin D supplements on COVID-19 needs to be further explored.

Specific Comments:

Abstract:

1. I would reword "GWASs of Covid-19 susceptibility and severity from the Covid-19 Host Genetics Initiative were used to test the effect of 25OHD levels on these outcomes" It's not entirely clear what the authors mean. Do they mean that GWASs of Covid-19 susceptibility and severity from the Covid-19 Host Genetics Initiative were used to perform instrument-outcome associations?

2. The sentence "the genetically increased 25OHD levels tended to increase the odds ratio of severe disease, while OR = 2.21; 95% CI: 0.87-5.55" should be reworded. The finding is not consistent with its explanation.

3. Needs for more detailed information of Extensive sensitivity analyses "Extensive sensitivity analyses probing the assumptions of MR provided consistent estimates.". In addition, the result seems to be inconsistent in different instrumental variables and different methods.

Introduction

4. Needs some rewording: " In the case of vitamin D, MR has been able to provide causal effect estimates consistently in line with those obtained from RCTs, and would therefore support investments in 25OHD supplementation trials in Covid-19, if a benefit was shown". How to balance the findings of MR study and RCT?

5. Aside from the mentioned several core assumptions of MR, it is important to note the linearity assumptions within the IV framework.

Method

6. The authors should provide heterogeneity tests of individual genetic variants to explore the robust of IVW method.

7. More sensitivity analysis methods, including but not limited to the weighted median and penalized weighted median methods, should be carried out.

8. As far as I know, the COVID-19 host genetics initiative provided the summary data for severe COVID-19, COVID-19 and hospitalized COVID-19 with control groups were subjects from the general population without the specific phenotype, subjects who were COVID-19 negative based on prediction or self-report, or subjects who had COVID-19 without hospitalization. The author used extended phenotypes, but limited the controls as subjects who were COVID-19 negative based on prediction or self-report, or subjects who had COVID-19 without hospitalization. The authors should give a reason for the selected controls.

9. The definition of exposure is unclear. The detailed information of exposure- vitamin D and the information of summary data for the selected SNP associated with vitamin D should be provided.

Result

10. The description of the results seems to be inconsistent with the results in the table, such as, the MR Egger results are inconsistent with Egger OR as the Table 2 showed. In addition, the result seems to be inconsistent in different instrumental variables and different methods.

Discussion

11. The increasing observational studies indicate that vitamin D deficiency is associated with increased COVID-19 risk[1, 2]. The authors should explain more deeply for the contradictory results in the discussion. In addition, the findings should be interpreted more cautiously for guiding future 25OHD supplementation trials.

12. However, the unmeasured confounders or alternative causal pathways may be still affected those results because of the limitation of the method. A previous study found that serum 25OHD concentrations are highly heritable is winter season, which suggests a varied role of genetic factors, dependent on the UVB intensity[3]. So how to reduce the bias of UVB against results? I suggested that it should be added as a limitation.

13. As mentioned above, it is important to note the linearity assumptions within the IV framework. If the effect of vitamin D supplementation on Covid-19 may be nonlinear, how to interpret this finding?

Referrences

1) Meltzer Do Auid- Orcid: --- Fau - Best TJ, Best Tj Fau - Zhang H, Zhang H Fau - Vokes T, Vokes T Fau - Arora V, Arora V Fau - Solway J, Solway J. Association of Vitamin D Deficiency and Treatment with COVID-19 Incidence. LID - 2020.05.08.20095893 [pii] LID - 10.1101/2020.05.08.20095893 [doi].

2) Mitchell F. Vitamin-D and COVID-19: do deficient risk a poorer outcome? (2213-8595 (Electronic)).

3) Karohl C, Su S Fau - Kumari M, Kumari M Fau - Tangpricha V, Tangpricha V Fau - Veledar E, Veledar E Fau - Vaccarino V, Vaccarino V Fau - Raggi P, et al. Heritability and seasonal variability of vitamin D concentrations in male twins. (1938-3207 (Electronic)).

Reviewer #2: In this paper authors investigate whether genetically predicted vitamin D levels are associated with Covid-19 susceptibility and severity using Mendelian randomisation approach. The rationale for the work is very well justified, and the work conducted is impressive. However, I have a few major comments that would need to be addressed before this study is published:

1. Context relating to respiratory tract infections and vitamin D RCTs is misrepresented

Dozens of RCTs investigating the role of vitamin D in respiratory tract infections were conducted to date; majority show benefit (either statistically significant, or non-significant with OR suggestive of benefit). In particular see:

Martineau AR et al. Vitamin D supplementation to prevent acute respiratory tract infections: systematic review and meta-analysis of individual participant data. BMJ. 2017;356:i6583. Published 2017 Feb 15. doi:10.1136/bmj.i6583

In the Introduction, statement associated with Reference 14 (line 123) therefore misrepresents the context. Of note, cited paper found increased risk in treated arm for upper respiratory tract infections, but not for lower respiratory tract infections - later probably being more relevant in the context of Covid-19.

Moreover, Castillo et al. published a RCT that high-dose vitamin D supplementation significantly reduced need for ICU (ie decreased risk of severe disease) - findings that are at odds with results reported here.

Castillo ME, Entrenas Costa LM, Vaquero Barrios JM, Alcala Dıaz JF, Miranda JL, Bouillon R, Quesada Gomez JM, E¨ ffect of Calcifediol Treatment and best Available Therapy versus best Available Therapy on Intensive Care Unit Admission and Mortality Among Patients Hospitalized for COVID-19: A Pilot Randomized Clinical study¨, Journal of Steroid Biochemistry and Molecular Biology (2020)

2. Need more information on cohorts, and on control definition and risk of misclassification

Cohorts are referenced, but information given in the paper (or supplementary) is not enough to critically understand the work and implications, particularly in relation to cohorts used - descriptive analysis covering: age, gender, period covered (case/control assessment during which months of 2020?), countries included, ethnicity, comorbidities, BMI…).

I suggest expanding Table 1 to include this for all cohorts used.

Among cohorts used, are disease outcomes harmonised?

Misclassification (likelihood of false negatives in particular) is very likely and it would be good to report what it might be, for example using prevalence of disease in the cohort and what was in the population cohort was sampled from at that time. Eg in UKBiobank it is reasonable to expect a high proportion of [mild or asymptomatic] cases among controls given that testing was not widespread - which also means mild cases are "missing" amongst cases.

3. Meaning behind the reported findings

What does "one standard deviation on the logarithmic scale" of genetically increased 25OHD level mean - ie. what is the corresponding increase in nmol/L in circulating 25OHD concentration associated with this?

Given that vitD genetic proxy explains 1-4% of variance, it probably means that only a very small difference in circulating 25OHD (couple of nmol/L perhaps?) can be explained by the genetic proxy. Combined with the increasingly accepted notion that the relation between vitamin D and health outcomes follows a sigmoid (with threshold effect) rather than linear curve (Heaney R, 2013, Nutrition Reviews), is this finding strong enough to inform guidelines on vitamin d supplementation?

4. Interpretation of the findings

Looking at main results table (Table 2), it would also be true to say:

"When we used genetic instruments that are most likely to affect Covid-19 outcomes via their effect on 25OHD (after remoal of SNPs identified by Phenoscanner), a statistically significant reduction in Covid-19 susceptibility was noted among those with higher genetically predicted 25OHD (OR=0.38, 95%CI: 0.19-0.75). A suggestive decrease in risk was also noted when analysis only included genes in the vitamin D pathway (OR=0.61, 95%CI: 0.37-1.01)."

Of note, these results are reported incorrectly in the current version of the manuscript (line 278: "….we…found.. increased 25OHD levels on susceptibility …".) - in all analysis, DECREASED risk for susceptibility was found (clear in Figure 2).

It would be a more balanced paper if these findings were brought forward and discussed in the paper also - particularly how to explain different direction of the effect for susceptibility and severity.

Reviewer #3: This is an interesting paper, but I'm afraid that the results are strongly affected by selection / collider bias. For me, the primary analysis is valid, but the secondary analyses are faulty. The reason is that the primary analysis is confirmed absence versus presence of COVID-19, whereas the secondary analyses are severe versus mild COVID - hence even controls are selected as those who have COVID-19. A better analysis would be severe COVID versus no COVID. As per Table 1 of "Contextualizing selection bias in Mendelian randomization: how bad is it likely to be?" by Gkatzionis and Burgess, when selection into the sample depends on the risk factor (which is clearly the case for the secondary analyses here, as COVID status of participants would associate with vitamin D levels), bias is in the opposite direction as the observational association. Hence the COVID-19 severity analyses provide associations in the harmful direction. Hence the paper is interest, but currently mainly as an exposition of the bias that can arise due to improper selection of controls.

Overall, this is an interesting paper, but I would encourage the authors to only perform analyses where the comparison group is population-based controls. My personal preference is severe COVID versus control, as severe COVID is less susceptible to selection (see https://www.medrxiv.org/content/10.1101/2020.06.18.20134676v1.full.pdf). But there are other GWAS datasets that are available (see https://www.medrxiv.org/content/10.1101/2020.09.15.20165886v1.full.pdf - although we did also consider severe versus mild disease, so not fully following my own advice!).

I think a paper on this subject would be interesting, but the results and message would likely change substantially if the authors concentrated on the primary analysis (which suggests a null result), so will only provide broad comments at this point:

1. Abstract: "Genetically increased 25OHD levels by one standard deviation on the logarithmic scale had no clear effect on susceptibility but tended to increase the odds ratio..." - I'd prefer to see non-causal language in the results section of the manuscript. What you observe is that "Genetically higher 25OHD levels... associate with the risk of hospitalization". What you infer from this is that 25OHD is not a causal risk factor, but you do not show this directly - the causal aspect of the manuscript is an inference from the findings, not the finding itself. In other places too - for me, causal language is fine when describing the aims and interpretation of the work (although should be clear that causal interpretation is subject to untestable assumptions), but not when describing the results of analyses.

2. Did you account for correlation between genetic variants? Even if the variants were conditionally independent in their associations with 25OHD, they may well be correlated in their distributions.

3. Given the recommendation to focus on the results versus controls that suggested associations in the protective direction (albeit not statistically significant), you may want to think again about weak instrument bias and the preference for one-sample MR. I don't feel strongly here, but if the results change, then the argument that weak instrument bias is unimportant as the estimates are in the harmful direction no longer holds.

4. A further limitation is that summary data MR cannot address nonlinearity. This is not a serious limitation for the main public health question, which relates to large-scale population interventions, but it may still be that vitamin D supplementation has a benefit for those who are vitamin D deficient.

In summary, if this paper is re-written to focus on the primary analysis and other analyses that suggest a null effect, then it would be a valuable contribution to the literature.

---

Stephen Burgess

Any attachments provided with reviews can be seen via the following link:

[LINK]

23 Dec 2020

Submitted filename: Rebuttal VitD PlosMed v2.docx

Click here for additional data file.

8 Feb 2021

Dear Dr. Richards,

Thank you very much for submitting your revised manuscript "Vitamin D and Covid-19 Susceptibility and Severity: a Mendelian Randomization Study" (PMEDICINE-D-20-04227R2) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also sent to the three original reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, we would like to consider a revised version that addresses the remaining reviewers' and editors' comments. Please do address the concerns of Reviewer 2 in your response. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we may seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

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Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/plosmedicine/s/submission-guidelines#loc-methods.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Title: Please include some information on the study population if feasible.

Abstract: Please rename the “Introduction” section to “Background”

Abstract: Please spell out Mendelian randomization for MR at first use.

Abstract: Background: Line 72-73: Please revise to mitigate causal language: “Here, we used two-sample MR to assess evidence supporting a causal effect of circulating 25OHD levels on Covid-19 susceptibility.” or similar.

Abstract: Methods and Findings: Please provide some detail regarding this GWAS: “Genetic variants strongly associated with 25OHD levels in a 443,734-participant genomewide association study (GWAS) were used as instrumental variables.”

Abstract: Methods and Findings Line 79-80: Please clarify this sentence- are these two cohorts of individuals with COVID-19, or is one intended to be without COVID-19. Also, please clarify briefly the manner of how it was determined that the individuals had COVID-19.

Abstract: Conclusion: We suggest adding an opening sentence such as: “In this 2 sample MR study, we did not observe evidence to support an association between 25OHD levels and COVID-19 susceptibility, severity, or hospitalization.” or similar.

Author summary: Why was the study done?: We suggest deleting the first bullet point.

Author summary (and throughout the text): Please consistently capitalize the Mendelian in Mendelian randomization.

Methods: Line 220-221: Please qualify this statement with “to the best of our knowledge” or similar.

Methods: Line 237-243: Please clarify the description to indicate, for the hospitalization and severity cohort, whether the controls were individuals without severe Covid/hospitalized for Covid and did or did not have Covid-19.

Methods: Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist).”

Results: Line 320: Please change “clear” to “statistically significant” or similar, and please revise the sentence to: “For a standard deviation increase in log-transformed 25OHD level, we observed no significant association with odds of susceptibility (OR = 0.97; 95% CI:0.85, 1.10; P = 0.61).”

Results: Line 321-324: To address the comment of Reviewer 2, please clarify the relationship between 1SD increase in biomarker vs instrument.

Results: Line 329: The word “clear” is ambiguous, it could be removed or replaced.

Results: Line 324-326: Please revise to “Similarly, we observed no significant difference in risk of hospitalization (OR =1.11; 95% CI: 0.91, 1.35; P = 0.30) or risk of severe disease (OR = 0.93; 95% CI: 0.73, 1.17; P = 0.53) associated with a standard deviation increase in log-transformed 25OHD level (Table 2 and Figure 1).”

Results: Line 331: To avoid overly causal language we suggest referring to associations when reporting the results.

Results Line 338-341: Please revise to “Using IVW, each standard deviation increase in log-transformed 25OHD was again not associated with Covid-19 susceptibility (OR = 0.962; 95% CI: 0.83, 1.11;P = 0.594), hospitalization (OR = 1.07 [95% CI: 0.78, 1.47]; P = 0.668) and severe disease (OR = 0.869; 95% CI: 0.635, 1.19; P = 0.378).”

Results: Line 347-350: Please revise to “In both cases, the association with each phenotype was mild compared to their effect on 25OHD level, as rs11723621 explained less than 0.03% of the variance in white blood cell counts, and rs6127099 explained less than 0.001% of the glomerular filtration rate variance[39,40].”

Results: Line 360: Please change “hypothesis” to “hypotheses”

Discussion: Line 364-366: We suggest revising slightly to avoid overly causal language: “In this large-scale MR study, we did not find evidence to support that genetically increased 25OHD levels are protective against Covid-19 susceptibility, hospitalization, or severity. This lack of evidence supporting a causal association was consistent across phenotypes, sensitivity analyses, and choice of genetic instruments.” or similar.

Discussion: Line 371-374: Please include the citation for the study described here. Also we would suggest revising to remove the first instance of the word “small” as it is emphasized later in the statement, and also clarify the “flawed endpoint” to make your point more apparent: “Further, while a small randomized trial showed benefit of vitamin D supplementation, this trial used a flawed endpoint and a small sample size, and it is therefore unable to invalidate our results.”

Discussion: Line 404: Please revise to “...for the purpose of vitamin D supplementation…”

Discussion: Line 406: Please revise to “...as we only studied the effect of 25OHD and Covid-19 in individuals of European ancestry, it remains possible…”

Checklist: We agree the utility of the STROBE for case-control studies is limited, and the STROBE-MR does not seem to have been finalized/published. If the authors feel the STREGA extension on the STROBE items is most appropriate, please use that.

Figure 1: Would it be possible to include a note in the legend that p values are given in Supporting Information table 4 (if I have that correct)?

Page 11: The sections Data Availability, Funding Source, Competing Interest, and Transparency statement can be removed from the main text of the manuscript. Please ensure that all information is entered in the appropriate place in the manuscript submission forms (Financial Disclosure, Competing Interests, Data Availability)

Comments from the reviewers:

Reviewer #1: Authors have answered my comments. I still have a minor suggestion, as follows,

The main finding that genetically increased 25OHD levels did not protect against COVID-19 susceptibility, hospitalization, or severity, indicates that endogenous 25OHD levels are not causallly associated with COVID-19 susceptibility, hospitalization, or severity, but not completely equal to the main conclusion. I suggest the authors to make the conculusion more carefully.

Reviewer #2:

Findings from this study have much improved after re-analysis with the most up-to-date dataset available. Process is streamlined, results clearer and presentation improved.

It is unfortunate however that other sections of the paper have deteriorated since the original submission and paper needs substantial editing for coherence, typos, accuracy and most notably context and interpretation of findings.

For example:

- abstract line 80 should presumably state "…1,284,876 WITHOUT Covid-19…"

- authors inconsistently use "vitamin D level" when they mean "genetically predicted 25OHD level" (eg Author Summary)

Major concerns at this stage are as follows:

1. In terms of methodology, the key issue with genetic instruments in this case is not that the instrument is weak, but that it's association with outcome is time-varying.

Genetic instruments explain large fraction of the variance in winter, but virtually none in the summer. This issue has been flagged in various MR contexts eg.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5123677/

https://academic.oup.com/aje/article/188/1/231/5098387

In the discussion section, authors need to explain how the time-varying relationship between the instrument and the biomarker might have affected their null-findings, given that this has not been incorporated in the analysis.

2. The level of biased presentation remains highly concerning.

For example, the original submission cited selected finding from one RCT on respiratory tract infections, that showed an increased risk with vitamin D. After the individual-patient data meta-analysis of ~30 RCTs that published in BMJ was brought to authors' attention (https://www.bmj.com/content/356/bmj.i6583) - a study that showed stat sign beneficial effect of vitD overall (adjusted odds ratio 0.81, 0.72 to 0.91), and statistically significant effect among vitD deficient with substantial effect size (adjusted odds ratio 0.30, 0.17 to 0.53), authors choose to cite the statistically insignificant but "numerically worse all-cause mortality" outcome form this study (introduction line 160).

In the refs to support the agreement between non-significant MR and RCTs, authors provide refs (Introduction line 179), but fail to mention that while statistically not significant, "numerically beneficial" effect of vit D (or OR at 1) was found in majority of primary and secondary analysis reported in these.

Authors do not mention vitD MR studies that found a stat sign beneficial effect of genetically predicted vitD level, eg https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4582411/

Authors might consider citing observational vitD study in UK Biobank

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204679/

3. How did authors calculate that 1 SD in genetically predicted 25OHD corresponds to 21.1 nmol/L?

Mean 25OHD in entire UK Biobank is 48.6 nmol/L and SD is 21.1 nmol/L. One SD in the instrument is not equal to 1 SD in the biomarker, particularly if instrument explains only ~4% of the variance.

Given the authors' previous study (ref 27), it should be easy and it would be informative to use actual UK Biobank data to show the correlation between instruments (genetically predicted 25OHD) and actual 25OHD level. For example, creating quintiles of genetically predicted vitD level and giving mean and SD of measured 25OHD for each quintile in genetically predicted 25OHD.

4. Interpretation of findings is not in line with results.

Eg last line of Abstract/results states that results do not apply to vitD deficient individuals. Appropriate conclusion should therefore be: "Our results do not support that individuals who are vitamin D sufficient be advised to take vitD supplements."

In terms of guiding future research, a more constructive recommendation should be building on well-known issues with vitamin D RCTs - for example recruiting vitamin D deficient individuals in the trial (it is established that high proportion of vitD sufficient individuals in the trial limits the study from detecting the beneficial effect, as no futher benefit can be expected among those who are vitD sufficient at baseline). Or recommendation for future MR studies in this area - eg using a design that allows for a time-varying relationship that exists between the instrument and biomarker.

In terms of using MR to prioritise drugs for testing in RCT, there are two things to consider:

1) many potential medicines do not have a naturally occurring biomarker and cannot be interrogated in MR setting so it's hard to rank them against those that do

2) vit D is suitable for co-administration and hence factorial trial design

Reviewer #3: The authors have responded appropriately to the reviewer comments, and the paper is much improved. Couple of very minor comments (in no order of importance!):

1. Line 336: "Second, we restricted out analysis" should be "Second, we restricted our analysis".

2. Line 188: "However, this assumption still provides valid results" - For me, this could be clearer. The property is this: causal estimation in Mendelian randomization assumes a linear causal relationship between the exposure and outcome. But if the relationship is truly non-linear, then the estimate is still a valid test of the causal null hypothesis, and the estimate represents the population-averaged effect of a shift in the distribution of the exposure.

3. "rejected the null hypothesis of lack of heterogeneity" - Would "homogeneity" be clearer than "lack of heterogeneity"? Also, it's not clear what is being referenced here - heterogeneity/homogeneity in what? I presume from context in the variant-specific causal estimates, but it's not clear from the text ("heterogeneity in the Covid-19 hospitalization phenotype" seems to suggest something else).

4. Line 281 - "that effects Covid-19 outcomes" -> "that affects Covid-19 outcomes"

5. Acknowledgement: the Supplements here are incorrectly numbered.

Any attachments provided with reviews can be seen via the following link:

[LINK]

18 Feb 2021

Submitted filename: Rebuttal.docx

Click here for additional data file.

5 Mar 2021

Dear Dr. Richards,

Thank you very much for re-submitting your manuscript "Vitamin D and COVID-19 Susceptibility and Severity in the COVID-19 Host Genetics Initiative: a Mendelian Randomization Study" (PMEDICINE-D-20-04227R3) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Mar 12 2021 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1. Title: Please capitalize the first word in the subtitle, and use sentence case throughout: “Vitamin D and COVID-19 susceptibility and severity in the COVID-19 Host Genetics Initiative: A Mendelian randomization study"

2. Abstract: Line 89: Please revise "clear" to "significant" or "statistically significant" in this sentence.

3. Abstract: Conclusion: Line 100: Please change “mean” to “means” in this sentence.

4. Author Summary: What do these findings mean? We suggest revising the first bullet point, to focus more on the findings/advance of your particular study- “Vitamin D is a highly confounded variable, and traditional observational studies are at high risk of biased estimates.”

5. Author Summary: What do these findings mean? We suggest revising to: “Taken together with literature supporting concordance between MR studies and RCTs investigating vitamin D effectiveness, these findings suggest that other therapeutic and preventative avenues should be prioritized for COVID-19 trials.” or similar.

6. Throughout: Please include a space between the preceding word and bracket of the in-text citation.

7. Introduction: Line 212: Please revise the final sentence: “...to test the relationship between genetically increased 25OHD level on COVID-19 susceptibility and severity.” or similar, to avoid causal implications.

8. Methods: Line 289: We suggest “undertook multiple analyses” rather than “undertook extensive analysis” here.

9. Discussion: Lines 392-395: Please clarify this sentence: “These findings highlight the confounded association between vitamin D and COVID-19 due to factors such as older age, institutionalization, or medical comorbidities, that are all linked to lower vitamin D levels and cannot be controlled for even when using advanced statistical adjustments.” Perhaps: “Differences between our findings and those reported in observational studies [references] may reflect the fact that associations between vitamin D and COVID-19 may be confounded due to factors difficult to control for even with advanced statistical adjustments, such as older age, institutionalizaiton or medical comorbidities linked to lower vitamin D levels.” or similar.

10. Reference List: Please double check the formatting of all references, and please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references.

For example, please indicate that # 16 is a preprint. For # 25, PLOS Med should be PLoS Med.

11. Table 1: Please define all abbreviations in the legend, including GWAS, UKB, CPAP, BiPAP.

12. Checklist: Thank you for including the STROBE checklist. Please make your responses more distinct from the checklist items, if possible (italics for your notes, or a separate column on the right).

Any attachments provided with reviews can be seen via the following link:

[LINK]

8 Mar 2021

Submitted filename: Rebuttal.docx

Click here for additional data file.

31 Mar 2021

Dear Dr Richards,

On behalf of my colleagues and the Academic Editor, Cosetta Minelli, I am pleased to inform you that we have agreed to publish your manuscript "Vitamin D and COVID-19 susceptibility and severity in the COVID-19 Host Genetics Initiative: A Mendelian randomization study" (PMEDICINE-D-20-04227R4) in PLOS Medicine.

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Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine