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. 2022 Apr 28;18(4):e1010113. doi: 10.1371/journal.pgen.1010113

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program

Anurag Verma 1,2,3,*,#, Noah L Tsao 1,4,#, Lauren O Thomann 5, Yuk-Lam Ho 6, Sudha K Iyengar 7,8, Shiuh-Wen Luoh 9,10, Rotonya Carr 1,3,11, Dana C Crawford 8,12,13, Jimmy T Efird 14, Jennifer E Huffman 5, Adriana Hung 15, Kerry L Ivey 6,16,17, Michael G Levin 4, Julie Lynch 18, Pradeep Natarajan 5,19,20, Saiju Pyarajan 5,21, Alexander G Bick 5,22, Lauren Costa 6, Giulio Genovese 20,23,24, Richard Hauger 25, Ravi Madduri 26,27, Gita A Pathak 28,29, Renato Polimanti 28,29, Benjamin Voight 1,2,30,31, Marijana Vujkovic 1,3, Seyedeh Maryam Zekavat 5,32,33, Hongyu Zhao 28,33,34,35, Marylyn D Ritchie 2; VA Million Veteran Program COVID-19 Science Initiative, Kyong-Mi Chang 1,3, Kelly Cho 6, Juan P Casas 6,36, Philip S Tsao 37,38, J Michael Gaziano 6,36, Christopher O’Donnell 5,21,36, Scott M Damrauer 1,2,4,‡, Katherine P Liao 5,21,36,‡,*
Editor: Gregory S Barsh39
PMCID: PMC9049369  PMID: 35482673

Abstract

The study aims to determine the shared genetic architecture between COVID-19 severity with existing medical conditions using electronic health record (EHR) data. We conducted a Phenome-Wide Association Study (PheWAS) of genetic variants associated with critical illness (n = 35) or hospitalization (n = 42) due to severe COVID-19 using genome-wide association summary data from the Host Genetics Initiative. PheWAS analysis was performed using genotype-phenotype data from the Veterans Affairs Million Veteran Program (MVP). Phenotypes were defined by International Classification of Diseases (ICD) codes mapped to clinically relevant groups using published PheWAS methods. Among 658,582 Veterans, variants associated with severe COVID-19 were tested for association across 1,559 phenotypes. Variants at the ABO locus (rs495828, rs505922) associated with the largest number of phenotypes (nrs495828 = 53 and nrs505922 = 59); strongest association with venous embolism, odds ratio (ORrs495828 1.33 (p = 1.32 x 10−199), and thrombosis ORrs505922 1.33, p = 2.2 x10-265. Among 67 respiratory conditions tested, 11 had significant associations including MUC5B locus (rs35705950) with increased risk of idiopathic fibrosing alveolitis OR 2.83, p = 4.12 × 10−191; CRHR1 (rs61667602) associated with reduced risk of pulmonary fibrosis, OR 0.84, p = 2.26× 10−12. The TYK2 locus (rs11085727) associated with reduced risk for autoimmune conditions, e.g., psoriasis OR 0.88, p = 6.48 x10-23, lupus OR 0.84, p = 3.97 x 10−06. PheWAS stratified by ancestry demonstrated differences in genotype-phenotype associations. LMNA (rs581342) associated with neutropenia OR 1.29 p = 4.1 x 10−13 among Veterans of African and Hispanic ancestry but not European. Overall, we observed a shared genetic architecture between COVID-19 severity and conditions related to underlying risk factors for severe and poor COVID-19 outcomes. Differing associations between genotype-phenotype across ancestries may inform heterogenous outcomes observed with COVID-19. Divergent associations between risk for severe COVID-19 with autoimmune inflammatory conditions both respiratory and non-respiratory highlights the shared pathways and fine balance of immune host response and autoimmunity and caution required when considering treatment targets.

Author summary

Large population based genomic studies have discovered genetic variations associated with severe manifestations of Coronarvirus Disease 2019 (COVID-19). In this study, we screened for other human conditions that share associations with these same variants. Understanding shared genetic variants in known conditions, where the pathophysiology is better understood, can further inform the pathways by which SARS-CoV2, the virus that causes COVID-19, impacts multiple organ systems. While genetic variants associated with severe COVID-19 were also associated with known risk factors and poor outcomes related to COVID-19 such as deep venous thrombosis, a large subset of these variants were also associated with reduced risk of conditions largely comprised of immune-mediated diseases, e.g., psoriasis, lupus, rheumatoid arthritis. With regards to the latter, the shared genetic architecture between COVID-19 and immune-mediated conditions suggests that pathways controlling both immune tolerance and immunodeficiency are important for COVID-19 severity, with implications when considering targeting these pathways for treatment.

Introduction

Coronavirus disease 2019 (COVID-19) first identified in December of 2019[1], became a global pandemic by March 2020. As of September 2021, COVID-19, transmitted by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has resulted in the loss of over 5.4 million lives worldwide [2]. Identifying host genetic variants associated with severe clinical manifestations from COVID-19 can identify key pathways important in the pathogenesis of this condition. International efforts such as the COVID-19 Host Genetics Initiative (HGI)[3] have meta-analyzed genome-wide association study (GWAS) summary statistics at regular intervals to identify novel genetic associations with COVID-19 severity. Thus far, ten independent variants associated with COVID-19 severity at genome-wide significance have been identified, most notably at the ABO locus [4]. These GWASs have also identified variations in genes involving inflammatory cytokines and interferon signaling pathways such as IFNAR2, TYK2, and DPP9 [4].

The unprecedented availability of genome-wide data for COVID-19 provides an opportunity to study clinical conditions that share genetic risk factors for COVID-19 severity. Examining known conditions, each with a body of knowledge regarding important pathways and targets, may in turn improve our understanding of pathways relevant for COVID-19 severity and inform the development of novel treatments against this pathogen. The Phenome-Wide Association Study (PheWAS) is an approach for simultaneously testing genetic variants’ association with a wide spectrum of conditions and phenotypes [5]. The Veteran’s Affairs (VA) Million Veteran Program (MVP) has generated genotypic data on over 650,000 participants linked with electronic health record (EHR) data containing rich phenotypic data, enables large-scale PheWAS. Moreover, MVP has the highest racial and ethnic diversity of the major biobanks worldwide affording an opportunity to compare whether associations are similar across ancestries [6].

The objective of this study was to use existing clinical EHR data to identify conditions that share genetic variants with COVID-19 severity using the disease-agnostic PheWAS approach. Since COVID-19 is a new condition, identifying existing conditions which share genetic susceptibility may allow us to leverage existing knowledge from these known conditions to provide context regarding important pathways for COVID-19 severity, as well as how pathways may differ across subpopulations.

Methods

Ethics statement

The Million Veteran Program received ethical and study protocol approval from the VA Central Institutional Review Board (IRB) in accordance with the principles outlined in the Declaration of Helsinki. All individuals in the study provided written informed consent as part of the MVP.

Data sources

The VA MVP is a national cohort launched in 2011 designed to study the contributions of genetics, lifestyle, and military exposures to health and disease among US Veterans [6].

Blood biospecimens were collected for DNA isolation and genotyping, and the biorepository was linked with the VA EHR, which includes diagnosis codes (International Classification of Diseases ninth revision [ICD-9] and tenth revision [ICD-10]) for all Veterans followed in the healthcare system up to September 2019. The single nucleotide polymorphism (SNP) data in the MVP cohort was generated using a custom Thermo Fisher Axiom genotyping platform called MVP 1.0. The quality control steps and genotyping imputation using 1000 Genomes cosmopolitan reference panel on the MVP cohort has been described previously [7].

Genetic variant selection

An overview of the analytic workflow is outlined in Fig 1. Variants were derived from the COVID-19 HGI GWAS meta-analysis release v6 [3]. In this study, we analyzed the following HGI GWAS summary statistics: 1) hospitalized and critically ill COVID-19 vs. population controls denoted as “A2” in HGI, and referred to as “critical COVID” in this study, and 2) hospitalized because of COVID-19 vs. population controls, denoted as “B2” in HGI, referred to as “hospitalized COVID” in this study [3]. For each GWAS, variants with a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.01 were selected as candidate lead SNPs (3,502 associated with critical COVID, and 4,336 associated with hospitalized COVID). Variants with r2 <0.1 were clustered within a 250 kb region according to 1000 Genomes phase 3 trans-ancestry reference panel [8]. Then, the variant with the smallest p-value in the region was selected as lead variant, resulting in 45 independent variants associated with critical COVID and 42 variants associated with hospitalized COVID summary statistics. The lead variants from each set of GWAS summary statistics are available in S1 Table. We used the nearest gene approach to prioritize the potential causal genes. A gene with the smallest genomic distance to a lead variant was selected.

Fig 1. Overview of variant selection and PheWAS analysis design.

Fig 1

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Classification of race/ethnicity in MVP

Race and ethnicity was determined using the harmonized ancestry and race/ethnicity (HARE) method for four major groups [9] corresponding to: (1) African ancestry, non-Hispanic Black (AFR), (2) Asian ancestry, non-Hispanic Asian (ASN), (3) European ancestry, non-Hispanic White (EUR), and (4) Hispanic ancestry (HIS). Individuals without a HARE classification were most likely be from ancestries with insufficient numbers to train the HARE algorithm including Native American, Alaska Native, and Pacific Islanders. Briefly, the HARE method was an algorithm developed to assign each subject to one of four groups using both self-reported race/ethnicity as the input to train a machine learning algorithm using ancestry informative genetic markers. This approach provides a classification for each individual leveraging information from genomic markers if self-reported race/ethnicity was missing, enabling analyses that can stratify by the four major groups. When comparing HARE vs self-reported race and ethnicity, the statistical error rate was estimated at 0.05–0.46%.

Outcomes

Clinical data prior to the onset of the COVID-19 pandemic were used to reduce potential confounding bias from SARS-CoV-2 infection on existing conditions. Phenotypes were defined by phecodes from prior studies [5,10]. Each phecode represents ICD codes grouped into clinically relevant phenotypes for clinical studies. For example, the phecode “deep venous thrombosis” includes “venous embolism of deep vessels of the distal lower extremities,” and “deep venous thrombosis of the proximal lower extremity,” both of which have distinct ICD codes. Using this approach, all ICD codes for all Veterans in MVP were extracted and each assigned a phenotype defined by a phecode. ICD-9 and ICD-10 codes were mapped to 1,876 phecodes, as previously described [5,10].

For each phecode, participants with ≥2 phecode-mapped ICD-9 or ICD-10 codes were defined as cases, whereas those with no instance of a phecode-mapped ICD-9 or ICD-10 code were defined as controls. Based on our previous simulation studies of ICD EHR data, populations where the phecode comprises < 200 cases were more likely to result in spurious results [11], and we thus applied this threshold in four ancestry groups: AFR, ASN, HIS, and EUR. In total, we analyzed 1,617 (EUR), 1304 (AFR), 993 (HIS), 294 (ASN) phecodes from the MVP cohort.

Phenome-wide association studies

The primary PheWAS analysis used SNPs identified from the HGI GWAS of critical and hospitalized COVID, and tested association of these SNPs with phenotypes extracted from the EHR using data prior to the COVID-19 pandemic. Logistic regression using PLINK2 to examine the SNP association with phecodes and firth regression was applied when logistic regression model failed to converge. Regression models were adjusted for sex, age (at enrollment), age squared, and the first 20 principal components.

Ancestry-specific PheWAS was first performed in these four groups, and summary data were meta-analyzed using an inverse-variance weighted fixed-effects model implemented in the PheWAS R package [10]. We assessed heterogeneity using I2 and excluded any results with excess heterogeneity (I2 > 40%).

To address multiple testing, an association between SNP and phecode with FDR p < 0.01 was considered significant. Thus, the threshold for significance was p < 6.07 × 10−05 for critical COVID lead variants, and p < 4.13 × 10−05 for hospitalized COVID lead variants. In the main manuscript we highlight PheWAS significant associations using FDR < 0.01 and an effect size associated with increased or reduced risk for a condition by 10%, with complete PheWAS results provided in S2 and S3 Tables.

Results

We studied 658,582 MVP participants, with mean age 68 years, 90% male, with 30% participants from non-European ancestry (Table 1). The PheWAS was performed on 35 genetic variants associated with critical COVID-19, and 42 genetic variants (S1 Table) associated with hospitalized COVID, across 1,559 phenotypes.

Table 1. Patient characteristics of Million Veteran Program participants.

Characteristics Million Veteran Program
Number (%)
Total Patients 658,582
Male 592,516 (90)
Ancestry
European 464,961 (70)
African 123,120 (19)
Hispanic 52,183 (8)
Asian 8,329 (1)
Other 9,989 (2)
Comorbidities
Obesity (phecode = 278) 283,197 (43)
Hypertension (phecode = 401.1) 451,998 (69)
Type 2 Diabetes (phecode = 250.2) 227,575 (34)
Coronary Artery Disease (phecode = 411.4) 152,136 (23)
Chronic Kidney Disease (phecode = 585.2) 10,046 (15)
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From the trans-ancestry meta-analysis, we identified 151 phenotypes significantly associated with critical COVID GWAS-identified variants, and 156 associations with hospitalized COVID GWAS-identified lead variants (FDR, p<0.01). Among these lead variants with significant PheWAS associations, 10 SNPs were associated with reduced risk of critical and hospitalized COVID-19 in HGI. Six variants were common to both critical and hospitalized COVID and had significant PheWAS associations, namely, variations nearest to the genes ABO (rs495828 and rs505922), DPP9 (rs2277732), MUC5B (rs35705950), TYK2 (rs11085727), and CCHCR1 (rs9501257) (S2 and S3 Tables).

Association of ABO loci with known risk factors and outcomes related to COVID-19 severity

In the trans-ancestry meta-analysis, the phenotype with the strongest association with variants near ABO locus (rs495828 and rs505922) was “hypercoagulable state” (ORcritical_PheWAS = 1.48 [1.42–1.54], Pcritical_PheWAS = 1.84 × 10−40; ORhospitalized_PheWAS = 1.51 [1.46–1.56], Phospitalized_PheWAS = 2.11 × 10−55, Fig 2). The ABO loci had the largest number of significant PheWAS association findings, accounting for 35% (53/151) of significant phenotype associations in the critical COVID PheWAS, and 37% (59/156) in the hospitalized COVID PheWAS. The phenotypes with the most significant associations and largest effect size were related to hypercoagulable states and coagulopathies. As expected, conditions not related to coagulopathy associated with the ABO locus, included type 2 diabetes and ischemic heart disease, have been reported as risk factors for or are complications associated with COVID-19 severity and mortality [1,4,12,13] (Fig 2 and S2 and S3 Tables).

Fig 2. PheWAS results of candidate SNPs from GWAS of critically ill and hospitalized COVID-19.

Fig 2

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Significant associations between 48 SNPs from critical ill COVID GWAS (A) and 39 SNPs from hospitalized COVID (C) and EHR derived phenotypes in the Million Veteran Program. The phenotypes are represented on the x-axis and ordered by broader disease categories. The red line denotes the significance threshold using false discovery rate of 1% using the Benjamini-Hochberg procedure. The description of phenotypes is highlighted for the associations with FDR < 0.1 and odds ratio < 0.90 or odds ratio > 1.10. (B) and (D) A heatmap plot of SNPs with at least one significant association (FDR < 0.1). The direction of effect disease risk is represented by odds ratio. A red color indicates increased risk and blue color indicated reduced risk. The results with odds ratio < 0.90 or odds ratio > 1.10 are shown.

Associations between variants associated with COVID-19 severity and respiratory conditions and infections

Among 68 respiratory conditions, only 11 diseases had significant associations (FDR < 0.01) shared with genetic variants associated with severe COVID-19. The most significant association was observed between rs35705950 (MUC5B) and idiopathic fibrosing alveolitis (OR = 2.83 [2.76–2.90]; P = 4.12 × 10−191), also known as idiopathic pulmonary fibrosis (IPF). Similarly, rs2277732 near DPP9 was associated with IPF (OR = 1.16 [1.09–1.22]; P = 5.84 × 10−06), both association between MUC5B, DPP9 variants and IPF has been reported in previous studies [14]. However, the association of genetic variants with other respiratory conditions may represent novel findings: the association of intronic variant rs61667602 in CRHR1 with reduced risk of post inflammatory pulmonary fibrosis (OR = 0.84 [0.80–0.89]; P = 2.26× 10−12), “alveolar and parietoalveolar pneumonopathy” (OR = 0.80 [0.72–0.88]; P = 1.58 × 10−08) and IPF (OR = 0.87 [0.82–0.92], P = 7.5 × 10−07). We did not detect associations between any of the variants and other respiratory conditions which are known risk factors for COVID-19 such as chronic obstructive pulmonary disease (COPD, S2 and S3 Tables).

Associations between variants associated with COVID-19 severity and reduced risk for certain phenotypes

The rs11085727-T allele of TYK2, a lead variant from the both critically ill and hospitalized COVID GWAS was associated with a reduced risk for psoriasis (OR = 0.88 [0.86–0.91], P = 6.48 × 10−23), psoriatic arthropathy (OR = 0.82 [0.76–0.87], P = 6.97 × 10−12), and lupus (OR = 0.84 [0.76–0.91], P = 63.97 × 10−06). This TYK2 signal has been previously reported to be associated with reduced risk of psoriasis, psoriatic arthropathy, type 1 diabetes, systemic lupus erythematosus and RA as well as other autoimmune inflammatory conditions (Table 2) [15,16].

Table 2. Phenotypes sharing association with variants also associated with severe COVID-19 infection, with reduced odds of disease listed in order of p-value*.

Phenotype OR (95% CI) p-value Gene SNP COVID-severity
Psoriasis 0.89 [0.86–0.91] 6.48E-23 TYK2 rs11085727 Both
Rosacea 0.84 [0.8–0.89] 7.54E-16 HLA-DPB1 rs9501257 Critical
Psoriatic arthropathy 0.82 [0.77–0.88] 6.97E-12 TYK2 rs11085727 Both
Post-inflammatory pulmonary fibrosis 0.87 [0.83–0.92] 4.54E-09 NSF rs9896243 Critical
Vitiligo 0.69 [0.56–0.82] 3.03E-08 CCHCR1 rs111837807 Both
Sarcoidosis 0.74 [0.62–0.85] 1.80E-07 CCHCR1 rs111837807 Both
Lupus (localized and systemic) 0.84 [0.77–0.91] 3.97E-06 TYK2 rs11085727 Both
Cutaneous lupus erythematosus 0.79 [0.68–0.89] 6.21E-06 TYK2 rs11085727 Both
Post-inflammatory pulmonary fibrosis 0.85 [0.8–0.9] 2.26E-12 CRHR1 rs61667602 Hospitalized
Rheumatoid arthritis 0.84 [0.79–0.9] 4.20E-10 HLA-DRA rs9268576 Hospitalized
Idiopathic fibrosing alveolitis 0.81 [0.73–0.88] 1.58E-08 CRHR1 rs61667602 Hospitalized
Rheumatoid arthritis and other inflammatory polyarthropathies 0.88 [0.84–0.93] 6.34E-08 HLA-DRA rs9268576 Hospitalized
Other alveolar and parietoalveolar pneumonopathy 0.88 [0.83–0.93] 7.50E-07 CRHR1 rs61667602 Hospitalized
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*OR<0.9 and P<10–5 shown in table, full results in supplementary; if multiple related conditions, e.g. psoriasis, psoriasis vulgaris, psoriasis and related disorders, description with lowest p-value selected shown in table.

Ancestry specific PheWAS provide insights into differential disease risks

The PheWAS analyses performed across four major ancestry groups in MVP observed similar findings as the overall meta-analysis with few associations unique to a specific ancestry (Fig 3 and S8 Table). SNP rs581342 (LMNA), associated with severe COVID-19, was a highly prevalent variant among subjects with AFR ancestry (MAF = 0.53) and was associated with neutropenia (ORAFR = 1.29 [1.21–1.39] PAFR = 4.09 × 10−13); this association was also observed in HIS ancestry (ORHIS = 1.65 [1.32–2.06], PHIS = 8.84 × 10−06) but was not in the larger EUR ancestry (S8 Table). To follow-up on this association, we extracted data on laboratory values for white blood cell (WBC) count and neutrophil fraction on all subjects. LMNA was associated with lower WBC in AFR, EUR, and HIS ancestries. However, LMNA was associated with a lower median neutrophil fraction only among Veterans of AFR ancestry (beta = -1.84 [-1.94, -1.75], PAFR = 1 x 10−300) and HIS ancestry (beta = -0.67, PHIS = 7.2 x 10−13) but not among Veterans of EUR ancestry (beta = -0.09, PEUR = 0.005). Among individuals of AFR ancestry, each allele was associated with a 1.84% lower neutrophil fraction, where among individuals of HIS and EUR ancestry, each allele was associated with 0.67% and 0.04% reduction, respectively (S9 Table).

Fig 3. PheWAS results of candidate SNPs from GWAS of Hospitalized COVID-19 in individuals of AFR ancestry.

Fig 3

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The plot highlights the association between rs581342 SNP and neutropenia, which was only observed in the AFR ancestry. The phenotypes are represented on the x-axis and ordered by broader disease categories. The red line denotes the significance threshold using false discovery rate of 1% using the Benjamini-Hochberg procedure. The table on the top right of the plot shows the association results between rs581342 and neutropenia in other ancestries. The association was not tested among participants of ASN ancestry due to low case numbers.

Similarly, associations between rs9268576 (HL-DRA) and thyrotoxicosis was only observed in individuals of AFR ancestry. The EUR ancestry specific PheWAS identified 39 significant associations which were not observed in other ancestry groups. One such association was between MUC5B variant and phecode for “dependence on respirator [ventilator] or supplemental oxygen” (OREUR = 1.16 [1.11–1.12], PEUR = 1.72× 10−10) among individuals of EUR ancestry was not significant in other ancestry groups (S8 Table). It is important to note that the conditions with significant association among individuals of EUR ancestry had similar prevalence among other ancestries. However, since there were overall fewer subjects in non-EUR ancestry groups, this likely resulted in lower statistical power to detect associations. All ancestry specific PheWAS results are available in supplementary tables (S4, S5, S6, and S7 Tables, and S1 and S2 Figs).

Association with variation at sex chromosome

In the hospitalized COVID-19 GWAS, we identified rs4830964 as the only lead variant on chromosome X. The SNP is located near ACE2 and was associated with “non-healing surgical wound” (OR = 0.92 [0.89–0.96], P = 2.23× 10−05). Notably, the SNP had nominal association (p<0.05) with type 2 diabetes and diabetes related complications that are previously reported association with variation in ACE2 (S3 Table). We did not observe any association with this variant in the ancestry specific PheWAS analysis.

Discussion

In this large-scale PheWAS, we identified the shared genetic architecture between variants associated with severe COVID-19 and other complex conditions using data from MVP, one of the largest and most diverse biobanks in the world. Broadly, these risk alleles identified conditions associated with risk factors for severe COVID-19 manifestations such as type 2 diabetes and ischemic heart disease across all ancestries examined here. Notably, the strongest associations with the highest effect size were related to coagulopathies, specifically, hypercoagulable state including deep venous thrombosis and other thrombotic complications, also shared variants associated with severe COVID-19. In contrast, among respiratory conditions, only idiopathic pulmonary fibrosis and chronic alveolar lung disease shared genetic risk factors, with the notable absence of an association with COPD and other respiratory infections. When comparing findings across ancestry groups in MVP, we observed that a risk allele associated with severe COVID-19, LMNA, also shared an association with neutropenia among Veterans of AFR and HIS ancestry. Finally, we observed that variants associated with severe COVID-19 had an opposite association, or reduced odds with autoimmune inflammatory conditions, such as psoriasis, psoriatic arthritis, RA, and inflammatory lung conditions.

A classic GWAS tests the association between millions of genetic variants with the presence or absence of one phenotype, e.g., GWAS of deep venous thrombosis. In the COVID-19 HGI GWAS, the “phenotype” was patients hospitalized for or critically ill from COVID-19. Clinically, this population includes a mixture of patients with a complex list of medical conditions at high risk for severe COVID complications and those who had actual complications from COVID-19. Thus, we would anticipate that many of the significant phenotypes would be associated with risk factors such as obesity and deep venous thrombosis. Additionally, our findings suggest that the PheWAS approach can be a useful tool to identify clinical factors related to emerging infectious diseases regarding severity or complications when genomic data are available.

The PheWAS results of SNPs in the ABO locus served as a positive control for this study. Genetic variations in ABO are an established risk factor for COVID-19 severity. Patients with blood group A have a higher risk of requiring mechanical ventilation and extended ICU stay compared with patients with blood group O [17]. These same variations at ABO had known associations with a spectrum of blood coagulation disorders identified in studies pre-dating COVID-19 [18–20]. The PheWAS of ABO variants identified associations with increased risk of deep vein thrombosis, pulmonary embolism, and other circulatory disorders, in line with prior studies, and recent studies among patients hospitalized with COVID-19 [21–25].

Among the respiratory conditions, only idiopathic pulmonary fibrosis (IPF) and chronic alveoli lung disease had shared associations with the variants near genes MUC5B, CRHR1, and NSF. Located in the enhancer region of the MUC5B, rs35705950, is a known risk factor for IPF, and a high mortality rate was observed among the COVID-19 patients with pre-existing IPF [26]. However, the MUC5B variant is associated with a reduced risk of severe COVID-19 (OR = 0.89), suggesting the risk allele’s opposing effect for infection and pulmonary fibrosis. In a separate study of MVP participants tested for COVID-19, we identified a significant mediating effect of the MUC5B variant in reducing risk for pneumonia due to COVID-19 [27].

Several conditions shared genetic variants associated with severe COVID-19, however, the association was for reduced odds for these conditions. All except one, rosacea, have an autoimmune etiology. The existing literature can help explain some of the dual association between reduced risk of autoimmune conditions such as psoriasis and RA and increased risk of severe COVID-19 via TYK2. TYK2, a member of the Janus Kinase (JAK) family of genes, plays a key role in cytokine signal transduction and the inflammatory response [28], specifically in type 1 interferon signaling, part of the innate immune response blocking the spread of a virus from infected to uninfected cells. Partial loss of TYK2 function is associated with reduced risk for several autoimmune disorders such as RA and psoriatic disease, conditions treated with immunosuppressive therapy [15,29–32]. Humans with complete TYK2 loss of function have clinically significant immunodeficiency with increased susceptibility to mycobacterial and viral infections [28,33]. Thus, this observation for opposing associations of variants with COVID-19 and autoimmune conditions highlights the fine balance between host immune response and autoimmunity.

While non-white populations are disproportionately affected by COVID-19, the majority of studies still predominantly consist of individuals from EUR ancestry. The COVID-19 GWAS data from the HGI consists of participants from over 25 countries EUR (33% non-EUR samples), enabling identification of variants more prevalent in non-EUR populations. We used these data to perform a PheWAS on the linked genotype-phenotype data from MVP, the most racial and ethnically diverse biobank in the US. From this large-scale study across ancestries, we observed that a variant located in the LMNA gene locus was associated with a diagnosis of neutropenia in AFR ancestry and HIS, but not EUR which would otherwise would have been well powered to detect an association. LMNA was associated with lower WBC counts across all ancestries, but its association with a lower fraction of neutrophils was observed in AFR and HIS only, but not EUR, in line with the overall association with diagnoses codes for neutropenia. ASN ancestry comprised the smallest ancestry group in MVP and was not tested due to low case numbers for neutropenia.

LMNA variants are associated with a broad spectrum of cardiomyopathies such as dilated cardiomyopathies, familial atrial fibrillation. However, the association with neutropenia has not been previously reported. Neutropenia refers to an abnormally low number of neutrophils in the blood, and predisposing to increased risk of infection. Epidemiology studies have shown that lower neutrophil counts are more common in individuals of AFR ancestry [34,35] and are hypothesized to be a result of selection and generally considered benign. To our knowledge benign neutropenia has not been previously reported among individuals of HIS ancestry [36]. Whether low neutrophil levels may clinically impact COVID-19 outcomes remains to be seen and warrants further study.

Limitations

The PheWAS of risk alleles associated with severe COVID-19 did not observe an association between other chronic pulmonary conditions such as COPD, a risk factor for severe COVID manifestations [13,37,38]. This absence of association allows us to discuss a few limitations of the PheWAS approach. The PheWAS was designed to broadly screen for potentially clinically relevant associations between genes and thousands of phenotypes. The phenotypes are based on ICD codes, and the accuracy of these codes can vary across conditions. Misclassification of cases and controls would reduce power to detect associations. The clinical definition of COPD itself is an area of active discussion and thus could impact the already modest accuracy of COPD diagnostic codes, further limiting power to detect an association [39,40]. As well, the PheWAS has limited power to detect associations for uncommon conditions and may explain the absence of associations with another chronic pulmonary condition, CF which has a prevalence of 0.02% in this Veteran population. Alternatively, studies to date have yielded mixed results with regards to risk for severe COVID among patients with CF [41]. To enable a trans-ancestry study, we applied a conservative approach using 20 PCs to adjust all models, used in prior studies. One potential pitfall of this approach is that the models may be overadjusted and thus are more likely to miss few significant associations. Finally, COPD is a condition where cigarette smoking, an environmental risk factor, accounts for the majority of cases [42]. While genetics is an important aspect of COPD, the link between variants and COPD may be weaker compared to other conditions where genetic variants drive the phenotype, ABO blood type as an example. Thus, conditions such as COPD where environmental risk factors or where gene-environment interactions play a major role in risk, may be more difficult to identify in a standard PheWAS. Findings from this study suggest that variants associated with severe COVID-19 are also associated with reduced odds of having an autoimmune inflammatory condition. However, the results cannot provide information on the impact of actual SARS-CoV-2 infection in these individuals after diagnosis of an autoimmune disease.

Conclusions

The PheWAS of genetic variants reported to associate with severe COVID-19 demonstrated shared genetic architecture between COVID-19 severity and known underlying risk factors for both severe COVID-19 and poor COVID-19 outcomes, rather than susceptibility to other viral infections. Overall, the associations observed were generally consistent across genetic ancestries, with the exception of a stronger association with neutropenia among Veterans of AFR and HIS ancestry and not EUR. Notably, only few respiratory conditions had a shared genetic association with severe COVID-19. Among these, variants associated with a reduced risk for severe COVID-19 had an opposite association, with reduced risk for inflammatory and fibrotic pulmonary conditions. Similarly, other divergent associations were observed between severe COVID-19 and autoimmune inflammatory conditions, shedding light on the concept of the fine balance between immune tolerance and immunodeficiency. This balance will be important when considering therapeutic targets for COVID-19 therapies where pathways may control both inflammation and the viral host response.

Supporting information

S1 Table. List of lead variants from critical ill and hospitalized COVID GWAS included in the study.

(XLSX)

Click here for additional data file. (14.8KB, xlsx)
S2 Table. Meta-analysis summary statistics from PheWAS of 35 lead SNPs identified from critical ill COVID GWAS.

(XLSX)

Click here for additional data file. (8MB, xlsx)
S3 Table. Meta-analysis summary statistics from PheWAS of 42 lead SNPs identified from Hospitalized COVID GWAS.

(XLSX)

Click here for additional data file. (9.6MB, xlsx)
S4 Table. Summary statistics from EUR ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

(XLSX)

Click here for additional data file. (19.3MB, xlsx)
S5 Table. Summary statistics from AFR ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

(XLSX)

Click here for additional data file. (15.4MB, xlsx)
S6 Table. Summary statistics from HIS ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

(XLSX)

Click here for additional data file. (11.4MB, xlsx)
S7 Table. Summary statistics from ASN ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

(XLSX)

Click here for additional data file. (3.7MB, xlsx)
S8 Table. Ancestry specific comparison of PheWAS results.

(XLSX)

Click here for additional data file. (16.4KB, xlsx)
S9 Table. Ancestry specific comparison of association between rs581342 and median values of neutrophil fraction and white blood cell counts.

(XLSX)

Click here for additional data file. (9.5KB, xlsx)
S1 Fig

The PheWAS results of 48 SNPs from critical ill COVID GWAS by each ancestry a) European ancestry, b) African ancestry, c) Hispanic ancestry, and d) Asian ancestry.

(TIF)

Click here for additional data file. (1.3MB, tif)
S2 Fig

The PheWAS results of 39 SNPs from hospitalized COVID GWAS by each ancestry a) European ancestry, b) African ancestry, c) Hispanic ancestry, and d) Asian ancestry.

(TIF)

Click here for additional data file. (1.3MB, tif)
S1 Text. VA Million Veteran Program COVID-19 Science Initiative Membership & Acknowledgements.

(DOCX)

Click here for additional data file. (23KB, docx)

Acknowledgments

We are grateful to our Veterans for their contributions to MVP. Full acknowledgements for the VA Million Veteran Program COVID-19 Science Initiative can be found in the S1 Text. We would like to thank the Host Genetic Initiative for making their data publicly available (https://www.covid19hg.org/acknowledgements/). This publication does not represent the views of the Department of Veteran Affairs or the United States Government.

Data Availability

Full summary statistics of the results presented in the study are made available. Individual level dataset underlying this study cannot be shared outside the VA, except as required under the Freedom of Information Act (FOIA), per VA policy. However, upon request through the formal mechanisms in place and pending approval from the VHA Office of Research Oversight (ORO), a de-identified, anonymized dataset underlying this study can be created and shared. Upon request through the formal mechanisms provided by the VHA ORO, we would be able to provide sufficiently detailed variable names and definitions to allow replication of our work. Any requests for data access should be directed to the VHA ORO (OROCROW@va.gov), and should reference the following project and analysis: MVP035: A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program.

Funding Statement

This research is based on data from the Million Veteran Program, Office of Research and Development, Veterans Health Administration, and was supported by award MVP035. S.M.D. is supported by US Department of Veterans Affairs (IK2-CX001780). R.C. is supported by NIH grants R01 AA026302 and P30 DK0503060. K.P.L. is supported by NIH P30 AR072577, and the Harold and Duval Bowen Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Gregory S Barsh

2 Dec 2021

Dear Dr Verma,

Thank you very much for submitting your Research Article entitled 'A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program' to PLOS Genetics.

The manuscript was fully evaluated at the editorial level and by independent peer reviewers. The reviewers appreciated the attention to an important problem, but raised some substantial concerns about the current manuscript. Based on the reviews, we will not be able to accept this version of the manuscript, but we would be willing to review a much-revised version. We cannot, of course, promise publication at that time.

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Editor-in-Chief

PLOS Genetics

Gregory Copenhaver

Editor-in-Chief

PLOS Genetics

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I was involved in the review of this work when it was submitted to PLoS Medicine. I was invited to review the present manuscript as a revision to that submission. Overall, the paper is a substantial improvement over the previous version and addresses the majority of my earlier concerns. In particular, there is a much stronger justification for the choice of parameter values, and the removal of UKBB greatly improves the clarity of presentation. I only have one relatively minor concern remaining.

While it is common to adjust for the first k principal components (PCs) as a way to correct for broad-scale population structure, it may be good to show that the first 20 PCs do NOT align with the variables being modeled (associations with phecodes in this case). Otherwise, there is a concern that adjusting for PCs may lower the signal-to-noise ratio and artificially deflate the importance of one or more of the phecodes being investigated. The artificial deflation of importance may, for example, explain why the authors did not detect some of the known risk factors, such as COPD (Lines 268, 320).

Minor comments:

-eTable 1 -> S1 Table.

-The ancestry group acronyms EUR, AFR, etc. are used before they are defined.

-Line 237: “both severe and hospitalized COVID” -> “both critical and hospitalized COVID”

-Line 253: “have been reported as risk factors for or are complications associated with COVID-19 severity and mortality” -should cite the work being referenced in the sentence

-Figures 2A&C: Some of the labels are obscured by the overlap.

-Line 314: The acronym T2D (Type 2 Diabetes) has not been defined

-Line 332: The sentence “The clinical data used in this study pre-dates the emergence of COVID-19…” feels out of place and repeats what was stated in the Methods. Consider cutting.

Reviewer #2: This manuscript reports a carefully executed and clearly described phenome wide association study (PheWAS) of SNPs associated with critical and hospitalized COVID in over 650K ancestrally diverse participants in the Million Veteran Program (MVP). The Methods are in general well defined, the Results clearly presented, and the Discussion clear, though in places a bit verbose.

Given that its ethnic and ancestral diversity is a major strength of the MVP, it is surprising that the authors reported their findings by genetic ancestry groups alone and not by self-identified race/ethnic groups as well. While the two are highly correlated, they are not identical, and at minimum a description of the correlation between the two should be provided. Preferable would be to repeat the findings stratifying by self-identified groups and describing observed similarities and differences. MVP is one of the very few cohorts that can do this; this opportunity should not be missed. They should also describe how individuals who did not fit into one of the four HARE groups were analyzed.

The Methods do not describe how SNPs were assigned to genes (or vice versa). Typically 85-90% of GWAS associations lie in intergenic regions, some of which are known to play important regulatory roles in other genes. The only reference I could find was in line 238, simply, “…variations nearest to the genes….” This should be described in more detail, as should the search for important regulatory regions tagged by non-exonic SNPs. They should also describe how the SNPs in Table 2 and Figs 2 and 3 were selected as “representative” of these genes. Presumably they chose the SNP with the strongest association (by p-value?) but this does not seem to be stated anywhere.

The data table in Fig. 3 describe a stronger association of rs581342 and neutropenia in COVID hospitalized persons of Hispanic genetic ancestry than in those of African ancestry (OR 1.65, p 8.84 x 10-6 ) that exceeds the authors’ defined significance threshold of p < 4.13 × 10-05, yet it is never mentioned. While the stronger OR is a point estimate based on a small number of cases (318 HIS vs. 1,788 AFR) and its confidence interval likely overlaps with that in AFR, the significance level merits a discussion despite its not being consonant with the authors’ hypothesis of a relationship to the known benign neutropenia among African Americans. Or is there an error in the table?

The authors go into extensive discussions of PheWAS associations with idiopathic pulmonary fibrosis (lines 348-364), autoimmune diseases (lines 366-83) and neutropenia (lines 398-405) but provide almost no explanation of the observed lack of associations with pre-existing pulmonary disease aside from reiterating them in lines 319-20. The former three discussions could be cut back (particularly the seemingly tangential link to depression and anxiety in asthma in lines 360-61) and expand on reasons for the surprising lack of pulmonary disease associations or suggest research to clarify them.

Minor comments:

1. Line 360: There may be a word missing in “…variations in this gene have also shown associations enhanced improvement….”

2. Table 1: numbers of Asian, Other, and Chronic Kidney Disease seem to be incorrectly punctuated.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: No: Restrictions on access to VA data are beyond the authors' control but they do describe procedures for accessing the data.

**********

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Reviewer #1: Yes: Artem Sokolov

Reviewer #2: Yes: Teri Manolio

Decision Letter 1

Gregory S Barsh

20 Feb 2022

Dear Dr Verma,

We are pleased to inform you that your manuscript entitled "A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program" has been editorially accepted for publication in PLOS Genetics. Congratulations!

The revised manuscript was seen by both of the original reviewers and as you will see they are enthusiastic about moving forward (as are we).

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Yours sincerely,

Gregory S. Barsh

Editor-in-Chief

PLOS Genetics

Gregory Copenhaver

Editor-in-Chief

PLOS Genetics

www.plosgenetics.org

Twitter: @PLOSGenetics

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

Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Thank you for another opportunity to consider this manuscript. I really appreciate the authors sharing their insight into how the 20 principal components were chosen. The revisions address all of my remaining concerns, and I am happy to recommend this work for publication.

Reviewer #2: The authors have addressed my concerns.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Artem Sokolov

Reviewer #2: Yes: Teri A Manolio

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Data Deposition

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Acceptance letter

Gregory S Barsh

4 Apr 2022

PGENETICS-D-21-01344R1

A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program

Dear Dr Verma,

We are pleased to inform you that your manuscript entitled "A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out or your manuscript is a front-matter piece, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Agnes Pap

PLOS Genetics

On behalf of:

The PLOS Genetics Team

Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom

plosgenetics@plos.org | +44 (0) 1223-442823

plosgenetics.org | Twitter: @PLOSGenetics

Associated Data

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

    Supplementary Materials

    S1 Table. List of lead variants from critical ill and hospitalized COVID GWAS included in the study.

    (XLSX)

    Click here for additional data file. (14.8KB, xlsx)
    S2 Table. Meta-analysis summary statistics from PheWAS of 35 lead SNPs identified from critical ill COVID GWAS.

    (XLSX)

    Click here for additional data file. (8MB, xlsx)
    S3 Table. Meta-analysis summary statistics from PheWAS of 42 lead SNPs identified from Hospitalized COVID GWAS.

    (XLSX)

    Click here for additional data file. (9.6MB, xlsx)
    S4 Table. Summary statistics from EUR ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

    (XLSX)

    Click here for additional data file. (19.3MB, xlsx)
    S5 Table. Summary statistics from AFR ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

    (XLSX)

    Click here for additional data file. (15.4MB, xlsx)
    S6 Table. Summary statistics from HIS ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

    (XLSX)

    Click here for additional data file. (11.4MB, xlsx)
    S7 Table. Summary statistics from ASN ancestry PheWAS of lead SNPs identified from critical ill and hospitalized COVID-19 GWAS.

    (XLSX)

    Click here for additional data file. (3.7MB, xlsx)
    S8 Table. Ancestry specific comparison of PheWAS results.

    (XLSX)

    Click here for additional data file. (16.4KB, xlsx)
    S9 Table. Ancestry specific comparison of association between rs581342 and median values of neutrophil fraction and white blood cell counts.

    (XLSX)

    Click here for additional data file. (9.5KB, xlsx)
    S1 Fig

    The PheWAS results of 48 SNPs from critical ill COVID GWAS by each ancestry a) European ancestry, b) African ancestry, c) Hispanic ancestry, and d) Asian ancestry.

    (TIF)

    Click here for additional data file. (1.3MB, tif)
    S2 Fig

    The PheWAS results of 39 SNPs from hospitalized COVID GWAS by each ancestry a) European ancestry, b) African ancestry, c) Hispanic ancestry, and d) Asian ancestry.

    (TIF)

    Click here for additional data file. (1.3MB, tif)
    S1 Text. VA Million Veteran Program COVID-19 Science Initiative Membership & Acknowledgements.

    (DOCX)

    Click here for additional data file. (23KB, docx)
    Attachment

    Submitted filename: COVID_coverlett_PLoSGen_forReviewer.docx

    Click here for additional data file. (229.5KB, docx)
    Attachment

    Submitted filename: 2022.01.24_point-by-point_reviewer_response.docx

    Click here for additional data file. (31.2KB, docx)

    Data Availability Statement

    Full summary statistics of the results presented in the study are made available. Individual level dataset underlying this study cannot be shared outside the VA, except as required under the Freedom of Information Act (FOIA), per VA policy. However, upon request through the formal mechanisms in place and pending approval from the VHA Office of Research Oversight (ORO), a de-identified, anonymized dataset underlying this study can be created and shared. Upon request through the formal mechanisms provided by the VHA ORO, we would be able to provide sufficiently detailed variable names and definitions to allow replication of our work. Any requests for data access should be directed to the VHA ORO (OROCROW@va.gov), and should reference the following project and analysis: MVP035: A Phenome-Wide Association Study of genes associated with COVID-19 severity reveals shared genetics with complex diseases in the Million Veteran Program.

    Articles from PLoS Genetics are provided here courtesy of PLOS

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