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. Author manuscript; available in PMC: 2024 Nov 1.
Published in final edited form as: Blood Cells Mol Dis. 2023 Jul 20;103:102782. doi: 10.1016/j.bcmd.2023.102782

Genetic Examination of Hematological Parameters in SARS-CoV-2 Infection and COVID-19

Quan Sun 1,#, Bryce Rowland 1,#, Wanjiang Wang 1, Tyne W Miller-Fleming 2, Nancy Cox 2, Misa Graff 3, Annika Faucon 2, Megan M Shuey 2, Elizabeth E Blue 4, Paul Auer 5, Yun Li 1,6,*, Vijay G Sankaran 7,*, Alexander P Reiner 8,*, Laura M Raffield 6,*
PMCID: PMC10507673  NIHMSID: NIHMS1922923  PMID: 37558590

Abstract

People hospitalized with COVID-19 often exhibit altered hematological traits associated with disease prognosis (e.g., lower lymphocyte and platelet counts). We investigated whether inter-individual variability in baseline hematological traits influences risk of acute SARS-CoV-2 infection or progression to severe COVID-19. We report inconsistent associations between blood cell traits 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 also assessed the shared genetic architecture of baseline blood cell traits on COVID-19 outcomes by Mendelian randomization (MR) analyses. We found significant relationships between COVID-19 severity and mean sphered cell volume after adjusting for multiple testing. However, MR results differed significantly across different freezes of COVID-19 summary statistics and genetic correlation between these traits was modest (0.1), decreasing our confidence in these results. We observed overlapping genetic association signals between other hematological and COVID-19 traits at specific loci such as MAPT and TYK2. In conclusion, we did not find convincing evidence of relationships between the genetic architecture of blood cell traits and either SARS-CoV-2 infection or COVID-19 hospitalization, though we do see evidence of shared signals at specific loci.

Keywords: COVID-19, genome-wide association studies, Mendelian randomization, genetic correlation, hematological traits

Introduction

The COVID-19 pandemic caused by the SARS-CoV-2 virus has been responsible for >764 million cases and >6.9 million deaths worldwide as of April 2023 [1]. The clinical course and severity of SARS-CoV-2 infection and illness are heterogeneous. While SARS-CoV-2 infection is characterized by respiratory manifestations and pulmonary complications, infection can elicit a complex immune-inflammatory and thrombotic host response with multi-organ system involvement [2]. Thus, hematologic abnormalities, including T cell lymphopenia, expanded peripheral immature neutrophils, activated monocytes, thrombocytopenia, and lower hemoglobin levels are often observed in hospitalized COVID-19 patients and can fluctuate with disease progression and severity [3–7]. Some clinical hematologic traits measured at the time of hospital admission have been associated with response to treatment and more severe COVID-19 [4, 5]. Further, hematologic, immune, and hemostatic abnormalities may contribute directly to organ damage and dysfunction associated with severe COVID-19 given the central role of blood cells in tissue oxygenation, innate and adaptive immune response and thrombosis [8–14]. Therefore, establishing causal pathways between blood cell and COVID-19 traits could lead to the discovery of effective treatments for COVID-19 through repurposing existing drugs currently used to treat blood or immune-related disorders [15].

The effect of an individual’s underlying or “baseline” (i.e., before acute infection) hematologic profile on SARS-CoV-2 infection susceptibility and COVID-19 severity is currently not well understood. Prior studies have reported associations between blood cell traits and COVID-19 severity using blood cell indices measured after time of infection or hospitalization, though some signals are inconsistent [4–7, 16–19]. When blood cell indices are measured after SARS-CoV-2 infection, associations between COVID-19 susceptibility or prognosis with blood cell abundance may reflect acute alterations due to infection or co-morbidities related to the course of COVID-19 illness (i.e., reverse causality). To better characterize the relationship between baseline blood cell measurements and risk of SARS-CoV-2 infection or COVID-19 hospitalization, we tested for association between hematological values measured prior to SARS-CoV-2 infection and incident COVID-19 outcomes in two longitudinal biobank datasets, UK Biobank (UKB) and the Vanderbilt University Medical Center Synthetic Derivative (VUMC SD).

Genetic factors contributing to hematologic conditions or to inter-individual variation in hematologic or immune response parameters may influence host susceptibility to COVID-19 outcomes [20]. Baseline blood cell measurements are influenced by genetics, as well as by long-term environmental, medical, sociodemographic and lifestyle/behavioral factors [21, 22]. Compared to observational studies that assess measured blood cell traits either pre-infection or during acute illness, studies that leverage genetic variants which have been constant for a given individual since birth may be useful in assessing shared genetic architecture and disentangling putative causal relationships between blood cell traits and COVID-19. Therefore, we conducted genetic correlation and two-sample Mendelian randomization (MR) analyses of blood cell phenotypes with SARS-CoV-2 infection and COVID-19 severity using summary statistics from the COVID-19 Host Genetics Initiative (HGI) together with a published genome-wide association study (GWAS) of hematologic traits [23]. Further, we evaluated individual coincident loci from these two analyses. These analyses allowed us to investigate whether genetically determined blood cell traits measured prior to disease initiation are related to COVID-19 susceptibility.

Methods

We examined the relationship between measured blood cell indices and COVID-19 traits in two biobank cohorts. Next, we assessed coincidence between GWAS loci associated with SARS-CoV-2 infection or COVID-19 severity and loci identified for blood cell traits. Finally, in order to examine the shared genetic architecture of COVID-19 and hematological traits, we performed a genetic correlation analysis with LD Score Regression [24] and a MR analysis with the Inverse Variant Weighted estimator [25], with MR-Egger [26] and the weighted median estimator also performed as sensitivity analyses. We analyzed publicly available summary statistics for three COVID-19 phenotypes from the HGI: COVID-19 severity as measured by severe respiratory infection (phenotype A2), COVID-19 severity as measured by hospitalization (phenotype B2), and SARS-CoV-2 infection (phenotype C2) [27]. For hematological traits, we utilized summary statistics from the largest GWAS to date of 408,112 European ancestry participants in UKB [23]. We note that all genomic positions throughout this manuscript are from build 37, and all LD calculations are from TOP-LD, [28] unless otherwise noted.

Data

UK Biobank (UKB) Samples from Measured Blood Trait Analyses

UKB (http://www.ukbiobank.ac.uk/resources/) recruited 500,000 people aged between 40–69 years in 2006–2010, establishing a prospective biobank study to understand risk factors for common diseases such as cancer, heart disease, stroke, diabetes, and dementia. Hematological traits were assayed as previously described [21]. Genotyping on custom Axiom arrays and subsequent quality control has been previously described [29]. Samples were also excluded based on factors likely to cause major perturbations in hematological indices: positive pregnancy status, certain drug treatments, cancer self-report, International Classifiers of Disease (ICD9 and ICD10) disease codes, and surgical procedures (Supplementary Table 6). Individuals who withdrew consent were also excluded and only samples with complete data for covariates and phenotypes were included (n= 423,358).

Vanderbilt University Medical Center (VUMC) Clinical Cohort Participants

VUMC is a major comprehensive and tertiary care center in Nashville, Tennessee. The SD, a de-identified copy of the electronic health record (EHR), contains longitudinal clinical information for over 3.3 million individuals [30]. The database incorporates information from multiple sources, including diagnostic and procedure codes (ICD and Current Procedural Terminology [CPT], respectively), demographics (age and EHR-reported race, ethnicity, and gender), text from clinical care notes (i.e. discharge summaries, nursing/progress notes, family history, and problem lists), medications, and laboratory values. The QualityLab pipeline was used to extract and clean laboratory values for over 275 million observations across 1.5 million patients as previously described [31].

UKB Summary Statistics for Hematological Traits

Summary statistics were obtained from a prior GWAS in UKB participants of European ancestry [23]. Briefly, a GWAS was conducted on 29 hematological traits, with adjustment for age, age-squared, sex, principal components and cohort specific covariates (e.g., study center, cohort, etc). Residuals were obtained and inverse normalized for cohort level GWAS.

HGI COVID-19

We downloaded the round 5 (January 18, 2021) and round 7 (April 8, 2022) COVID-19 summary statistics for three phenotypes, excluding UKB and 23andMe (which did not provide genome-wide summary statistics) from https://www.covid19hg.org (Supplementary Table 7). The severe illness phenotype was defined as patients who were hospitalized due to symptoms associated with laboratory-confirmed SARS-CoV-2 infection and required respiratory support or whose cause of death was associated with COVID-19 (5,870 cases and 1,155,203 controls in freeze 5; 17,472 cases and 725,695 controls in freeze 7). The hospitalization phenotype is a binary indicator of patients who were hospitalized for symptoms associated with laboratory-confirmed SARS-CoV-2 infection (11,829 cases and 1,725,210 controls in freeze 5; 40,929 cases and 1,924,400 controls in freeze 7). Population controls were used in both cases, including individuals whose exposure status to SARS-CoV-2 was unknown. Both phenotypes were defined using diagnostic criteria following the Diagnosis and Treatment Protocol for Novel Coronavirus Protocol [32]. Individuals with positive reported infection status were compared against population controls (42,557 cases and 1,424,707 controls in freeze 5; 143,839 cases and 2,257,647 controls in freeze 7).

Measured Blood Cell Analysis

Blood cell indices were directly measured at UKB in-person visits, as described above. We assessed the association of each measured blood cell trait with SARS-CoV-2 infection status (positive test versus population) and with COVID-19 hospitalization (hospitalized case versus population). Logistic regression analyses for case/control analyses were adjusted for age, sex, assessment center, self-reported ethnicity (based on Data-Field 21000), time elapsed between blood cell trait measurement and either SARS-CoV-2 test date or date of data download, and a blood cell trait and time elapsed interaction term. SARS-CoV-2 test results were available through February 24, 2021, and hospitalization related data were available through March 7, 2021 at time of download (March 12, 2021). Cause of death data was available through February 16, 2021. For consistency with data available in VUMC SD, we included as a hospitalized case anyone with a positive SARS-CoV-2 test originating when they were an inpatient at a hospital, or with a positive test up to 14 days before hospital admission, during hospital episode, or within 7 days after hospital discharge. This definition would not exclude cases identified incidentally in individuals hospitalized for other reasons. Controls are defined as the rest of the population to match genetic analyses [33].

VUMC SD Replication

We also assessed association of measured blood cell traits with SARS-CoV-2 positive test status and COVID-19 hospitalization in the VUMC SD [34]. Lab values were extracted from de-identified medical records and cleaned as previously described, using the QualityLab pipeline [31]. At least 50 cases were required for a model to be tested. A SARS-CoV-2 positive test was determined by either a positive SARS-CoV-2 test or related diagnostic code, U07.1. Controls included all individuals with data for a given blood cell trait and absence of any SARS-CoV-2 positive test. Cases for the COVID-19 hospitalization analysis are individuals with a SARS-CoV-2 positive test and a hospitalization code (for any indication) in the seven days before or 30 days after a positive SARS-CoV-2 test. Controls for the COVID-19 hospitalization analysis are similarly defined as all individuals with blood cell trait data that do not meet the case definition; however, we note that we also performed a sensitivity analysis using as controls individuals with a positive SARS-CoV-2 test and no hospitalization code within a similar time window (with similar results, not shown). Case and control populations were extracted from the SD by an experienced programmer with data current to October 2020.

Logistic regression models were adjusted for age, EHR-reported sex and race and ethnicity, time elapsed between blood cell trait measurement and either SARS-CoV-2 test date or end of follow-up, and a blood cell trait and time elapsed interaction term. As is commonly done in biobank data, median blood cell trait values were used across all available timepoints, with time of follow-up calculated in reference to the median age at which a given blood cell measurement was obtained. VUMC SD and UKB results were combined using fixed effects meta-analysis.

Analysis of Coincident Genetic Association Signals

We assessed evidence for coincident genetic association signals at COVID-19 loci which were associated with blood cell traits in a previous GWAS [23, 27], using summary statistics excluding UKB. We used the European TOP-LD [28] reference panel to establish COVID-19 loci in linkage disequilibrium (LD; r2 > 0.6) with distinct blood cell trait variants.

Genetic Correlation

LD Score Regression is a statistical technique to estimate genetic correlation between complex traits using only GWAS summary statistics [24]. We estimated genetic correlation for each combination of blood cell trait and COVID-19 phenotype. Statistical significance was evaluated as nominally significant at a p-value less than 0.05, and Bonferroni significant using 0.05/87. Pairs of traits which demonstrated at least nominal statistical significance of non-zero genetic correlation were prioritized in the MR analysis.

Mendelian Randomization (MR)

MR was performed to estimate the causal effect of hematological traits on the three COVID-19 phenotypes. 16,900 conditionally independent associations previously identified in UKB participants of European ancestry were used as instruments for the hematological traits[23]. Briefly, these distinct variants were determined as conditionally independent signals via stepwise multiple regression. The IVW estimator served as our primary estimator of the causal effect. As a sensitivity analysis, we fit the MR-Egger model in order to assess the evidence of directional pleiotropy via the MR-Egger intercept test. If the MR-Egger test demonstrated moderate statistical evidence away from the null hypothesis that it is zero (p < 0.2), we report the MR-Egger estimate of the causal effect rather than the IVW estimator. We further estimated the weighted median causal effect for robustness.

Results

Measured Blood Cell Analysis

We tested for associations between 15 hematologic traits measured at baseline and COVID-19 hospitalization and SARS-CoV-2 infection among 423,358 UKB participants (Methods, Supplementary Table 1). Basophil percentage (β =−0.72, p =3.67e-4) demonstrated association with SARS-CoV-2 infection at the Bonferroni-adjusted threshold (Table 1). Mean corpuscular hemoglobin (β = −0.38, p = 2.75e-3), and mean corpuscular volume (β = −0.19, p = 3.28e-4) demonstrated evidence of association with COVID-19 hospitalization at the same Bonferroni-adjusted threshold (Table 2). None of these significant signals replicated in our independent analysis of up to 1,037,358 VUMC SD participants (Supplementary Table 1, Table 1, Table 2, Supplementary Table 2).

Table 1: Measured blood cell traits associated with SARS-CoV-2 infection in UK Biobank (UKB) and Vanderbilt University Medical Center’s Synthetic Derivative (SD).

SE, standard error. Bonferroni adjusted threshold α = 0.05/15=0.003.

Blood cell trait Effect_UKB SE_UKB P-value_UKB Effect_SD SE_SD P-value_SD
Basophil (%) −0.72 0.20 3.67E–04 0.00 0.17 0.996
Eosinophil (%) −0.03 0.07 0.66 −0.21 0.09 0.02
Hematocrit (%) −0.11 0.04 3.86E–03 −0.04 0.02 0.01
Hemoglobin concentration (g/dL) −0.23 0.11 0.04
Lymphocyte (%) −0.02 0.02 0.38 −0.003 0.01 0.82
Mean corpuscular hemoglobin (pg) −0.09 0.07 0.23 −0.03 0.02 0.09
Mean corpuscular hemoglobin concentration (g/dL) 0.30 0.13 0.02
Mean corpuscular volume (fL) −0.08 0.03 0.01 0.003 0.01 0.67
Mean platelet volume (fL) −0.10 0.13 0.42 0.08 0.05 0.11
Monocyte (%) −0.07 0.05 0.17 −0.004 0.02 0.83
Neutrophil (%) 0.03 0.02 0.11 0.001 0.004 0.85
Platelet count (per nL) 0.00 0.00 0.04 −0.01 0.005 0.23
Red blood cell count (per pL) −0.30 0.33 0.37 0.26 0.69 0.71
Red cell distribution width (fL) −0.09 0.13 0.51 0.08 0.03 2.70E–03
White blood cell count (per nL) 0.01 0.08 0.94 −0.12 0.02 4.78E–09
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Table 2: Measured blood cell traits associated with COVID-19 hospitalization in UK Biobank (UKB) and Vanderbilt University Medical Center’s Synthetic Derivative (SD).

SE, standard error. Bonferroni adjusted threshold α = 0.05/15=0.003.

Blood cell trait Effect_UKB SE_UKB P-value_UKB Effect_SD SE_SD P-value_SD
Basophil (%) −0.57 0.40 0.15 −0.25 0.33 0.46
Eosinophil (%) 0.03 0.13 0.82 −0.19 0.14 0.17
Hematocrit (%) −0.08 0.07 0.22 −0.04 0.02 0.08
Hemoglobin concentration (g/dL) −0.22 0.19 0.25
Lymphocyte (%) 0.01 0.03 0.66 −0.02 0.02 0.35
Mean corpuscular hemoglobin (pg) −0.38 0.13 2.75E–03 −0.02 0.03 0.59
Mean corpuscular hemoglobin concentration (g/dL) 0.07 0.22 0.75
Mean corpuscular volume (fL) −0.19 0.05 3.28E–04 0.01 0.01 0.29
Mean platelet volume (fL) 0.39 0.23 0.08 0.18 0.11 0.08
Monocyte (%) −0.10 0.09 0.24 0.03 0.04 0.43
Neutrophil (%) 0.00 0.03 0.97 0.02 0.01 4.82E–03
Platelet count (per nL) 0.00 0.00 0.23 −0.0003 0.01 0.97
Red blood cell count (per pL) 0.59 0.59 0.32 −0.44 1.02 0.67
Red cell distribution width (fL) 0.35 0.22 0.12 0.25 0.04 8.85E–10
White blood cell count (per nL) 0.13 0.12 0.31 −0.15 0.04 1.03E–04
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As a follow-up analysis, we meta-analyzed the measured blood cell – COVID-19 outcome results from UKB and VUMC SD (Supplementary Tables 1 and 2). Total white blood cell count demonstrated evidence of association with both SARS-CoV-2 infection (β = −0.11, p = 1.73 e-8) and COVID-19 hospitalization (β = −0.12, p = 6.17 e-4); however, these associations were primarily driven by the VUMC SD cohort. Additionally, red blood cell distribution width was associated with COVID-19 hospitalization (β = 0.25, p = 2.79 e-10) and hematocrit was associated with SARS-CoV-2 infection (β = −0.05, p = 6.60e-4) (Tables 1 and 2). Given these inconsistencies, the remainder of our analyses focus on genetic summary statistic-based analyses, which enable the assessment of invariant factors impacting blood cell abundance across the life-course.

Analysis of Coincident Loci for SARS-CoV-2/COVID-19 and Blood Cell Traits

For individual variants previously found to be associated with SARS-CoV-2 infection and COVID-19 severity in large meta-analyses from HGI [33], we assessed whether these signals were coincident with a statistically distinct blood cell trait associated variant. If two loci are coincident, this suggests blood cell abundance could be a putative mediator of the SARS-CoV-2/COVID-19 association. We note that such locus-specific analyses are important even when there is no genome-wide genetic correlation, where differing directions of effect in different regions of the genome could lead to a null genome-wide result [35]. We obtained summary statistics for conditionally independent GWAS significant variants, i.e., distinct variants, from prior GWAS in UKB participants of European ancestry [23]. For each sentinel variant for SARS-CoV-2 infection, COVID-19 severe illness, and hospitalization from the HGI full meta-analysis results, we considered the corresponding sentinel variant from the meta-analyses excluding UKB (column 1 and 2 of Supplementary Table 3). We then assessed coincidence between distinct GWAS variants for blood cell traits from GWAS in UKB and sentinel variants for both SARS-CoV-2 infection and COVID-19 severe illness and hospitalization from the HGI meta-analysis [33], based on LD (Methods). We note that new “freezes” including additional COVID-19 and SARS-CoV-2 cases and controls were released while this paper was in preparation; we performed all analyses with both freeze 5 and freeze 7; these freezes both included the release of summary statistics which excluded UKB, providing an independent set of samples for blood cell vs SARS-Cov-2/COVID-19 genetic analyses.

Overall, 17 HGI COVID-19 related sentinel variants in freeze 7 and 3 in freeze 5 were in moderate LD (r2 > 0.6) with ≥1 distinct blood cell trait variant (Supplementary Table 3). We note that all the 3 COVID-19 sentinel variants in freeze 5 were still coincident in freeze 7 at locus level, even if the sentinel variant differed slightly. Of the 17 COVID-19 sentinel variants in freeze 7, 4 are located within highly differentiated regions of the genome with complex patterns of polymorphism, pleiotropy, LD and evolutionary selection (ABO [36, 37] and HLA [38]), which would make analysis of coincident signals difficult to interpret and outside the scope of this manuscript. Therefore, we focus on the remaining COVID-19 sentinel variants based on their associations. We would like to highlight (1) rs34725611 (chr19:10,477,067, TYK2) which was associated with reported infection and overlaps two GWAS signals for neutrophil percentage and lymphocyte count; and (2) rs63750417 (chr17:44060775, MAPT) which was associated with GWAS signals for mean reticulocyte volume and mean sphered cell volume. We also summarize the strength of information linking variants to functional consequences utilizing hematology related functional annotations provided by VAMPIRE [39].

Relationship of chromosome 19 (10Mb-11Mb) locus to white blood cell phenotypes

The minor allele for rs34725611 (chr19:10,477,067) is both associated with higher risk of reported infection in the freeze 7 leaving UKB HGI meta-analysis and is in strong LD (r2>0.9) with distinct GWAS variants for neutrophil percentage and lymphocyte count in UKB (Figure 1). Minor alleles of the two variants were associated with increased lymphocyte count and decreased neutrophil percentage. Similar results were observed using freeze 5 SARS-CoV-2 infection summary statistics. rs34725611 is in almost perfect LD (r2 = 0.993) with a TYK2 missense variant rs2304256 [23]. Associations between these highly correlated variants and both SARS-CoV-2 phenotypes and white blood cell phenotypes are likely to be driven by rs2304256 based on this functional annotation (Supplementary Figure 1).

Figure 1. Coincident loci analysis results of the TYK2 locus for rs34725611 and (a) neutrophil percentage; (b) lymphocyte count in UKB GWAS.

Figure 1.

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rs34725611 (diamond), a sentinel variant in the HGI COVID-19 freeze 7 GWAS for SARS-CoV-2 infection, was found to be a coincident signal with rs11085725 for neutrophil percentage and with rs35251378 for lymphocyte count, which both are distinct signals in Vuckovic et al. [23] Triangles are conditionally independent GWAS variants for blood cell traits as determined by conditional analysis in Vuckovic et al. [23]

Another variant, rs74956615 (chr19:10,427,721) was found to be a sentinel variant in the freeze 5 HGI meta-analysis associated with increased risk for severe illness and hospitalization due to COVID-19. rs74956615 is in high LD (r2 = 0.75) with rs34536443 (chr19:10,463,118), another missense variant in TYK2. rs34536443 was reported as a distinct GWAS variant for lymphocyte count, lymphocyte proportion, platelet count and plateletcrit. Our results suggest that the association between rs74956615 and severe illness and hospitalization due to COVID-19 coincides with lymphocyte and platelet phenotypic associations near TYK2 (Supplementary Table 3), suggesting links with immunity and thrombosis related cell types. We note that rs34536443 becomes the sentinel variant in the freeze 7 HGI meta-analysis for both severe illness and hospitalization.

Relationship of chromosome 17 (43.5Mb-44.5Mb) locus to red blood cell phenotypes

rs63750417 (chr17:44,060,775), a missense variant in MAPT, was a sentinel variant in the freeze 7 HGI meta-analysis associated with hospitalization due to COVID-19. Its LD proxy (r2 = 0.941), rs7218319 (chr17: 44,126,365), was the sentinel for severe illness. Both variants are in high LD (r2 = 0.941 and 0.91) with rs58879558 (chr17:44,095,467), which was reported as a distinct GWAS variant associated with mean reticulocyte volume and mean sphered cell volume (Figure 2). Furthermore, rs58879558 was linked to MAPT as a category 1 variant in the VAMPIRE tool, with consistent blood cell derived epigenetics, quantitative trait loci, and 3D chromatin interaction annotations linking this variant to MAPT [39].

Figure 2. Coincident loci analysis results of the MAPT locus for rs63750417 and (a) mean reticulocyte volume; (b) mean sphered cell volume in UKB GWAS.

Figure 2.

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rs63750417 (diamond), a sentinel missense variant in the HGI COVID-19 freeze 7 GWAS for hospitalization due to SARS-CoV-2, was found to be a coincident signal with rs58879558 for distinct signals of mean reticulocyte volume and mean sphered cell volume in Vuckovic et al. [23] Triangles are conditionally independent GWAS variants for blood cell traits as determined by conditional analysis in Vuckovic et al. [23]

Genetic Correlation Analyses

To assess the shared genetic architecture of blood cell traits with COVID-19 severity and SARS-CoV-2 reported infection, we performed LD Score Regression to estimate the genetic correlation with both freeze 7 and freeze 5 HGI meta-analysis summary statistics. Genetic correlation provides an estimate of the overlap in terms of evidence of associations for a pair of complex traits with existing genome-wide summary statistics [24]. Overall, we estimated genetic correlations for 29 blood cell trait phenotypes with reported SARS-CoV-2 infection, severe COVID-19 illness, and hospitalization.

In the freeze 7 results, there are three COVID-19 outcomes and blood cell trait pairs showing significant genetic correlation at the Bonferroni-corrected threshold (α = 0.05/87) (Supplementary Table 4). Specifically, mean sphered cell volume is significantly positively correlated with COVID-19 hospitalization; in addition, two red blood cell phenotypes, hemoglobin and red blood cell counts, are negatively correlated with COVID-19 reported infection. Note that the pair of mean sphered cell volume and COVID-19 hospitalization also showed the most significant genetic correlation in freeze 5 results (p = 0.035). Furthermore, we identified 20 more nominally significant (p < 0.05) genetic correlations in freeze 7 based results, though we note there is limited overlap between freeze 5 and freeze 7 findings (Supplementary Table 4).

Mendelian Randomization Analyses

To further assess shared genetic architecture as well as potential causal associations between baseline blood cell traits (exposures) and COVID-19 traits (outcomes), we performed MR analysis, again based on both freeze 7 and freeze 5 summary statistics. In contrast to genetic correlation and analysis of coincident loci, MR analyses attempt to assess the causal effect of one trait on another, not simple local or genome-wide sharing of genetic association signal [35]. Our instrumental variables were the distinct variants identified from direct conditional analysis for previous GWAS signals for blood cell traits in individuals of European ancestry, as used above for analysis of coincident loci (Methods; [23]). For our analysis, we used the HGI COVID-19 summary statistics excluding UKB participants[40] to prevent potential confounding. For completeness, we present MR results from the inverse-variance weighted (IVW), MR-Egger, and weighted median causal effect estimates in Supplementary Table 5. We recommend prioritizing the IVW causal effect estimate on the log odds ratio scale for interpretation when there is no significant evidence of directional pleiotropy (MR-Egger Intercept p-value > 0.05). Otherwise, we would prioritize the MR-Egger causal effect estimate above others. We recommend specific results to prioritize, highlighted in yellow in Supplementary Table 5, with the weighted median causal effect estimate provided for robustness.

We found one significant MR test at a stringent Bonferroni-adjusted threshold (α = 0.05/87) in freeze 7. Our results suggest that variants affecting mean sphered cell volume also affect COVID-19 hospitalization (IVW causal estimate 0.52, p = 5.5e-4), with no significant evidence of directional pleiotropy effects (MR-Egger Intercept p = 0.49). This is consistent with our genetic correlation analysis between mean sphered cell volume and COVID-19 hospitalization, but we note such causal relationship was not observed in freeze 5 MR analyses (IVW causal estimate 0.32, p = 0.39). Further, we found 16 nominal significant MR tests with no significant directional pleiotropy effects (IVW causal p < 0.05 and MR-Egger Intercept p > 0.05). We also identified 6 blood cell trait-outcome pairs that had a nominal causal effect on COVID-19 traits using the MR-egger causal effect estimate in the presence of significant directional pleiotropy effects (MR Egger p < 0.05 and MR-Egger Intercept p < 0.05). However, we note that such associations are often not echoed by the freeze 5 findings, most do not have significant evidence of genetic correlation, and significant findings for basophil proportion in freeze 5 are now nonsignificant (Supplementary Table 5).

Discussion

Given the extensive and variable reports that altered blood cell phenotypes are found in the setting of COVID-19 illness following SARS-CoV-2 infection, we studied both associations between baseline blood cell traits and COVID-19 outcomes as well as potential causal relationships with blood cell traits as exposures for COVID-19 outcomes via MR. We identified some shared genetic loci between COVID-19 outcomes and blood cell traits, as well as one potential causal relationship between mean sphered cell volume and COVID-19 hospitalization in the MR analyses using freeze 7 COVID-19 GWAS summary statistics. However, our results do not support a clear role of baseline blood cell traits prior to SARS-CoV-2 infection with either the risk of SARS-CoV-2 infection or COVID-19 hospitalization, and the results from different freezes and biobanks are largely inconsistent.

The modest hematologic trait associations with COVID-19 outcomes we observed in the UKB (e.g., basophil percentage and reported SARS-CoV-2 infection) could not be replicated in the VUMC SD cohort and had little support from the existing literature on blood cell indices measured at time of infection/hospital admission. We hypothesize the inconsistencies between the UKB and VUMC SD cohorts may be due to multiple factors, summarized below. The somewhat more compelling associations specific to VUMC SD of lower white blood cell counts with higher odds of reported SARS-CoV-2 infection (perhaps reflecting immune suppression) and of higher RDW with higher odds of COVID-19 hospitalization (concordant with previous associations of RDW at time of COVID-19 hospitalization with mortality [41], as well as prior RDW associations with clonal hematopoiesis [42], inflammation[43], and mortality in other contexts [44, 45]) were not replicated in UKB.

Lack of replication may in part be due to systematic differences in biobank recruitment. Patients were clinically ascertained for VUMC SD, with nonrandom reasons for which blood cell assays are ordered in this clinical setting, versus population-based sampling in UKB across a narrow age range, with a recruitment bias towards healthier participants than the UK population as a whole [46]. These led to substantial differences in cohort characteristics (Supplementary Table 1) and variable sample sizes across blood cell traits in VUMC SD (Supplementary Table 2). Blood cells might be measured more frequently in VUMC SD in participants impacted by certain diseases or using certain medications. Another important factor is the different timing of major COVID-19 outbreaks between US and UK, though most individuals are not vaccinated in either cohort given end of follow-up times. Such differences between populations can lead to variability in measured phenotypes and highlights the value of examining genetic variation underlying such traits, though genetic findings can also be susceptible to gene × gene and gene x environment impacts that may vary by population. Based on the nonsignificant MR results of our significant blood cell trait/COVID-19 related outcome pairs from the measured blood cell trait analysis, the measured blood cell traits that are most strongly associated with COVID-19 related hospitalization in the combined VUMC SD and UKB analysis, including RDW, may be tagging more general inflammatory pathways as opposed to playing a causal role in disease pathogenesis.

Our MR results identified one significant pair at a stringent Bonferroni-adjusted threshold (α = 0.05/87) in freeze 7; this pair is not even nominally significant in the freeze 5 results; we also do not observe in freeze 7 significant relationships reported in prior MR studies for COVID-19 related phenotypes and blood cell traits. In our view, these inconsistencies between different freezes and across different studies make the results less trustworthy. Sun et al. [47] reported an MR analysis of WBC phenotypes using the set of distinct genetic variants from Vuckovic et al. [23] and Chen et al. [48] with COVID-19 freeze 5 GWAS results, and associated genetically predicted higher basophils and myeloid white blood cell count with lower risk of severe COVID-19. Our variant sets (for freeze 5) differ slightly from those in Sun et al. due to their incorporation of distinct signals from the multi-population analysis of blood cell traits and the analysis of the multi-population HGI summary statistics; we also additionally include the most up to date freeze 7 summary statistics, with significantly increased sample size. Inclusion of multi-population summary statistics by Sun et al. [47] may lead to issues with the validity of genetic correlation and MR methods, due to differences in LD across global populations and across different multi-population summary statistic sets. Similarly, Wang et al. also performed a MR analysis for hematological parameters and severe COVID-19, but using freeze 4 of the HGI summary statistics without consideration of multiple testing or of the distinct signal list derived in Vuckovic et al. by direct conditional analysis. [49] Similar limitations also apply to another MR analysis of a broad range of phenotypes with COVID-19 traits [50], which included blood cell traits, and reported a protective association of higher neutrophils, granulocytes, and myeloid white blood cells. Here, we reported results from both freeze 5 and freeze 7; we emphasize the heterogeneity between both our results and prior papers, and between the two freezes presented here. These inconsistencies weaken our confidence in any true causal relationship between the tested blood cell phenotypes and SARS-CoV-2 infection/COVID-19 severity.

However, despite the lack of genome-wide correlations across blood cell and COVID-19 related traits, we did discover some genetic regions that show shared signals at some specific loci such as TYK2 (tyrosine kinase 2). The TYK2 missense variant rs34536443 (p.Pro1104Ala), has been previously associated with risk of various autoimmune diseases [51], as well as with higher lymphocyte count and lower platelet count. Another TYK2 missense variant rs2304256, highlighted in SARS-CoV-2 infection analyses, was also reported to be associated with autoimmune conditions lupus [52] and type 1 diabetes [53], along with the lymphocyte and neutrophil associations highlighted here. TYK2 is involved in immune response in humans and TYK2 deficiency results in impairment of cytokine response in mouse models [54, 55]. Given the role of TYK2 in host autoimmunity, it is possible that the association with lower platelet count may represent an autoimmune phenomenon due to autoantibodies that inhibit platelet production. We also highlight mean reticulocyte volume and mean sphered cell volume locus MAPT (microtubule associated protein tau); MAPT has primarily been studied in relationship to neurodegenerative diseases [56] and its potential role in red blood cell biology is less understood.

We would highlight several key limitations in this work. First, 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 monocyte or lymphocyte subsets (such as non-classical monocytes) may be relevant to COVID-19 and have been highlighted in some recent analyses[57], but could not be assessed here. Second, we note that for both our measured blood cell traits and genetic analyses, similar to the broader COVID-19 GWAS literature, misspecification of controls is certainly an issue, particularly for infection status as many infections are likely being missed, especially in early waves of the pandemic. However, even this imperfect phenotype has proved useful for prior genetic discovery efforts, and for comparability and maximizing statistical power, we use a similar definition here. Future genetic and epidemiological analyses with more refined phenotyping of COVID-19 related phenotypes including complications observed in severe cases may offer novel insights. Third, we note that while our measured blood cell analyses both included individuals from multiple race and ethnicity groups, our genetic analyses were limited to those of European ancestry due to technical considerations related to need for similarity of LD patterns across genetic summary statistic datasets. Finally, we note that while several blood cell trait-associated loci coincide with signals for COVID-19 severity and SARS-Cov-2 infection, it is difficult to formally evaluate enrichment, given the high polygenicity and large number of genome-wide significant signals known for highly heritable blood cell phenotypes, and coincidence with a blood cell trait signal also does not exclude pleiotropic relationships with other intermediate phenotypes.

In conclusion, despite the strong epidemiological links with blood cell indices and COVID-19 related phenotypes in individuals with existing SARS-CoV-2 infection, as well as the reasonable putative biological relevance through roles in immunity, oxygenation, and thrombosis, we see little conclusive evidence of association between pre-infection blood cell indices and incident SARS-CoV-2 infection or COVID-19 severity, using either epidemiological or genetic analysis methods. We do observe evidence of coincidence at some individual genetic loci (such as TYK2) between SARS-CoV-2 infection and COVID-19 severity related variants and blood cell related variants. We are hopeful that as more balanced case-control studies with increased sample sizes become available for COVID-19 related phenotypes more definitive conclusions regarding the similarity of blood cell count and COVID-19 genetic architecture can be raised.

Supplementary Material

MMC2
MMC1

Acknowledgements

The SD projects at Vanderbilt University Medical Center are supported by numerous sources: institutional funding, private agencies, and federal grants. These include the NIH funded Shared Instrumentation Grant S10OD017985 and S10RR025141; CTSA grants UL1TR002243, UL1TR000445, and UL1RR024975 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Genomic data are also supported by investigator-led projects that include U01HG004798, R01NS032830, RC2GM092618, P50GM115305, U01HG006378, U19HL065962, R01HD074711; and additional funding sources listed at https://victr.vumc.org/biovu-funding/.

This research has been conducted using the UK Biobank Resource under Application Number 25953. We would like to thank the Covid-19 Host Genetics Initiative for sharing the results of their analyses.

Funding statement

This work was supported by the National Institutes of Health (R01HL146500, U01HG011720, National Center for Advancing Translational Sciences KL2TR002490 (LMR)).

Footnotes

Conflict of interest disclosure

The authors declare that they have no competing interests.

Ethics approval statement

All UK Biobank data was downloaded through application #25953. UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval. Vanderbilt Synthetic Derivative data is accessed with approval from the Vanderbilt Institute for Clinical and Translational Research and Vanderbilt University IRB #200750.

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Data availability statement

The COVID-19 HGI GWAS summary statistics are available at https://www.covid19hg.org. Blood cell trait summary statistics are at ftp://ftp.sanger.ac.uk/pub/project/humgen/summary_statistics/UKBB_blood_cell_traits/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

MMC2
NIHMS1922923-supplement-MMC2.xlsx (56.7KB, xlsx)
MMC1
NIHMS1922923-supplement-MMC1.docx (1.5MB, docx)

Data Availability Statement

The COVID-19 HGI GWAS summary statistics are available at https://www.covid19hg.org. Blood cell trait summary statistics are at ftp://ftp.sanger.ac.uk/pub/project/humgen/summary_statistics/UKBB_blood_cell_traits/.

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