{"paper_id":"274d67af-c853-4fa1-98af-eb4e223b256c","body_text":"The study design is shown in  Fig. 1 . In brief, we used a two-sample MR approach to test the association of thromboembolism-related genetic variants with long COVID. Two-sample MR leverages genetic data from two separate GWAS: the first one is used to extract genetic instruments for the exposure of interest (e.g., venous thromboembolism), while the second is used to test the association of these instruments with the outcome of interest (e.g., long COVID) 11 , 21 . When an MR analysis adheres to specific assumptions ( Fig. 1 ; see  Methods ), it provides a robust framework for testing the causal association of a given exposure with the corresponding outcome 11 , 21 .\nIn line with this framework, we first identified 35 genetic variants (i.e., genetic instruments) associated with venous thromboembolism in FinnGen. All these instruments provide separate estimates of the causal effect of venous thromboembolism on long COVID, which are then combined through meta-analysis. Primary analyses tested the association of these genetic instruments (collectively referred to as “genetically predicted venous thromboembolism risk”) with long COVID in the Long COVID Host Genetics Initiative. Downstream analyses (1) assessed the robustness of this association using different methods and external datasets; (2) tested the effects of genetically predicted venous thromboembolism risk on specific conditions with symptoms shared with long COVID (referred to as “long COVID-resembling phenotypes”); and (3) explored molecular mediators of long COVID using gene-focused MR approaches ( Fig. 1 ).\nGenetic instruments for venous thromboembolism were extracted from FinnGen 22 , using data from 412,181 participants (venous thromboembolism:  n cases =21,021 and  n controls =391,160). Venous thromboembolism cases were identified using diagnostic codes from hospital discharge and death cause registers (see  Methods ). Of the included participants, 44.1% were male, and the median age was 62.9 years 23 , 24 .\nThe association of genetically predicted venous thromboembolism risk with long COVID was tested in the Long COVID Host Genetics Initiative 25 , characteristics of which are provided in  Supplementary Table 1 . While the Long COVID Host Genetics Initiative included 25 cohorts (long COVID:  n cases =6,450 and  n controls =1,093,995), primary analyses excluded participants who met the criteria for long COVID but did not have a prior test-verified SARS-CoV-2 infection and therefore only used data from 12 cohorts ( n cases =3,018;  n controls =994,582). Of these 12 cohorts, the majority (83.3%) used questionnaires rather than electronic health data to ascertain long COVID ( Supplementary Table 1 ). Almost all participants (99.1%) included in these cohorts had European ancestry, 48.0% were male, and the weighted mean age across cohorts was 62.3 years 25 . Around 30.5% of long COVID cases had a history of hospitalization due to COVID-19.\nFor our main analyses, we included 35 independent genetic variants (linkage disequilibrium  R 2 <0.001) strongly associated with venous thromboembolism ( P <5×10 -8 ) in FinnGen. Additional information on these variants, including nearest genes, can be found in  Supplementary Table 2 . The mean  F -statistic was 114.5, suggesting adequate strength 11 . In primary analyses, we tested and pooled the effects of these variants on long COVID risk in the Long COVID Host Genetics Initiative using the inverse-variance weighted MR method. Each unit increase in the log-odds of genetically predicted venous thromboembolism risk was associated with 1.21-fold odds of developing long COVID (95% confidence interval [CI]: 1.08-1.35;  P =1.2×10 -3 ;  Fig. 2 ), indicating a significant positive association of thromboembolism-promoting genetic variants with long COVID.\nTo evaluate the potential role of horizontal pleiotropy (i.e., effects of genetic instruments on the outcome outside of its effects on the exposure), we tested the association of genetically predicted venous thromboembolism risk with long COVID using MR-Egger 26 . MR-Egger showed no evidence of horizontal pleiotropy ( P intercept >0.99), with an effect estimate closely aligned with our primary analysis (OR, 1.21 [95%CI, 1.00-1.46] per log-odds increase in genetically predicted venous thromboembolism risk;  P =5.1×10 -2 ). This suggests that genetic variants associated with venous thromboembolism affect long COVID risk via thromboembolic mechanisms rather than alternative pathways. Consistent findings were observed with the weighted median (OR, 1.26 [95%CI, 1.06-1.49] per log-odds;  P =7.4×10 -3 ) and mode-based (OR, 1.33 [95%CI, 1.12-1.56] per log-odds;  P =8.5×10 -4 ) estimators, providing additional support for a pleiotropy-robust association.\nWe also performed sensitivity analyses using different linkage disequilibrium  R 2  ( R 2 <0.001/ R 2 <0.001/ R 2 <0.01/ R 2 <0.1) and  P -value thresholds ( P <5×10 -4 / P <5×10 -6 / P <5×10 -8 / P <5×10 -10 ) to extract genetic instruments for venous thromboembolism. These analyses showed that the association of venous thromboembolism with long COVID was directionally consistent across all tested parameters ( Fig. 2 ;  Supplementary Table 3 ). An additional sensitivity analysis showed consistent results using Slope-Hunter, a method that corrects for potential stratification bias in MR analyses of phenotypes that are conditional on other phenotypes (i.e., long COVID being conditional on SARS-CoV-2 infection) (OR, 1.15 [95%CI, 1.02-1.29] per log-odds increase in genetically predicted venous thromboembolism risk;  P =2.3×10 -2 ). Additionally, we tested for reverse causation (i.e., long COVID increasing venous thromboembolism risk) by applying Steiger filtering, which did not reveal reverse causal variants.  27 Correspondingly, MR analyses in the opposite direction (see  Methods )—evaluating the effect of genetically predicted long COVID on venous thromboembolism risk—yielded null results (OR, 1.01 [95%CI, 0.94–1.09] per log-odds increase in genetically predicted long COVID risk;  P =0.79), providing no evidence for reverse causation.\nReplication analyses evaluated whether the observed associations were consistent across different exposure and outcome datasets (see  Methods ). First, we used externally derived genetic instruments for venous thromboembolism using data from the Million Veteran Program 28  and tested its association with long COVID in the Long COVID Host Genetics Initiative ( Supplementary Table 4 ). Second, we tested the genetic association of venous thromboembolism with long COVID in a subcohort of the Long COVID Host Genetics Initiative that applied stricter criteria for controls, restricting to participants with proven SARS-CoV-2 infection 25  ( Supplementary Table 5 ). Third, we tested this association in the Post-Hospitalization COVID-19 (PHOSP-COVID) study, a prospective cohort study designed to investigate the medium- and long-term sequelae of hospitalized COVID-19 29 , 30  ( Supplementary Table 6 ). All three replication analyses demonstrated a significant positive association of genetically predicted venous thromboembolism risk with long COVID ( Fig. 2 ). Additional sensitivity analyses, using the same replication datasets with varying linkage disequilibrium  R 2  and  P -value thresholds for genetic instrument extraction, yielded consistent results ( Fig. 2 ;  Supplementary Tables 4-6 ).\nTo contextualize these findings, we assessed whether genetic instruments for other conditions were associated with long COVID. First, to corroborate the observed association of venous thromboembolism with long COVID, we tested the effects of genetic instruments for deep vein thrombosis and pulmonary embolism on long COVID risk. Next, to evaluate the specificity of these associations, we extended our analysis to a range of conditions representing different organ systems and pathophysiological pathways (e.g., type 2 diabetes, dementia, rheumatoid arthritis). These analyses showed that only genetically predicted risk for deep vein thrombosis (odds ratio [OR], 1.10 [95%CI, 0.98-1.22] per log-odds increase in genetically predicted deep vein thrombosis risk;  P =9.3×10 -2 ) and pulmonary embolism (OR, 1.17 [95%CI, 1.02-1.34] per log-odds increase in genetically predicted pulmonary embolism risk;  P =2.2×10 -2 ) had effect estimates within the 95%CI of the venous thromboembolism-long COVID association. None of the conditions less closely related to venous thromboembolism had effect estimates within this 95%CI or a positive association reaching nominal significance ( Fig. 3a ;  Supplementary Table 7 ). These findings support a specific association of venous thromboembolism with long COVID rather than a generalized association across pathophysiologically distinct conditions.\nGiven that thrombotic events are frequently observed in individuals with COVID-19, correlating partly with disease severity 31 , 32 , and that severe COVID-19 is a thought to be a causal risk factor for long COVID 25 , we investigated whether acute COVID-19 severity might mediate the genetic association of venous thromboembolism with long COVID. Specifically, we tested the effects of genetically predicted venous thromboembolism risk across three categories of acute COVID-19 severity in the COVID-19 Host Genetics Initiative 17 , 33 : SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19 (defined as hospitalization with respiratory support or COVID-19-related death). Our results showed that genetically predicted venous thromboembolism risk had effect estimates within the 95%CI for its association with long COVID for both hospitalized COVID-19 (OR, 1.09 [95%CI, 1.02-1.16] per log-odds increase in genetically predicted venous thromboembolism risk;  P =1.6×10 -2 ) and critical COVID-19 (OR, 1.08 [95%CI, 0.99-1.17] per log-odds;  P =6.6×10 -2 ), but not for SARS-CoV-2 infection alone ( Fig. 3b ). Only the association with hospitalized COVID-19 reached nominal significance.\nBuilding on these findings, we performed multivariable MR 34  to estimate the direct effects of genetic predisposition to venous thromboembolism on long COVID while adjusting for its impact on acute COVID-19 severity. Consistent with previous findings 25 , all acute COVID-19 phenotypes had significant genetic associations with long COVID in these multivariable MR models ( Supplementary Table 8 ). However, genetically predicted venous thromboembolism was also significantly associated with long COVID across all models ( Supplementary Table 8 ), suggesting that its effects on long COVID are not mediated by acute COVID-19 severity alone. These findings imply that a predisposition to thromboembolism contributes to the risk of developing long COVID through mechanisms that are not solely dependent on the severity of acute infection.\nTo gain additional insights into the mechanisms driving the genetic association of venous thromboembolism with long COVID, we tested the associations of genetically predicted venous thromboembolism risk with conditions with symptom profiles shared with long COVID. Genetically predicted risk of venous thromboembolism was not associated with a higher risk of long COVID-resembling conditions or symptoms such as memory loss or mood disorders ( Fig. 3c ;  Supplementary Tables 9-10 ). Although there was no significant association with postviral fatigue (OR, 1.13 [95%CI, 0.88-1.44] per log-odds increase in genetically predicted venous thromboembolism risk;  P =0.35), confidence intervals were very wide, suggesting this association was potentially underpowered due to a small number of cases in FinnGen’s GWAS of postviral fatigue ( n cases =195;  n controls =382,198). Nevertheless, these findings suggests the genetic association of venous thromboembolism with long COVID represents a specific association, wherein thromboembolism plays a role in the pathophysiology of long COVID rather than the occurrence of its individual, non-specific symptoms.\nFinally, we aimed to identify potential molecular mediators of the thromboembolism-long COVID association by evaluating the effects of individual genes and their associated proteins on long COVID risk. First, we extracted “gene-specific genetic instruments” by identifying variants strongly associated with venous thromboembolism within or near different coagulation-related genes/loci (see  Methods ). Gene-specific instruments (including one or multiple independent variants per gene) were available for a total of 40 genes from 27 distinct loci ( Supplementary Tables 11-12 ). Most genes had only one genetic instrument ( n =32/40 [80.0%]). All mean  F -statistics of the gene-specific instruments were greater than 10 (mean, 65.2; range, 12.9-377.4), indicating adequate instrument strength 11 . Among the different gene-specific instruments,  F2R ’s instrument (rs73131633) had the strongest effect on long COVID risk ( Fig. 5a ). Each unit increase in the log-odds of genetically predicted venous thromboembolism risk mediated through  F2R  was nominally associated with 4.50-fold odds of developing long COVID (95%CI, 1.12-18.2;  P =3.5×10 -2 ).\nThe  F2R  gene encodes the protease-activated receptor 1 (PAR-1; i.e., the thrombin receptor). To verify its association with long COVID, we tested the effects of genetically predicted circulating levels of PAR-1 on long COVID risk. Genetically predicted circulating levels of PAR-1 were instrumented using a single intronic  F2R  variant (rs168753) associated with modified PAR-1 expression on platelets leading to altered procoagulant activity 36 , 37 . Each A allele is associated with a 0.15-unit (95%CI, 0.13-0.17) increase in circulating PAR-1 levels, and this variant is in moderate linkage disequilibrium ( R 2 =0.36) with rs73131633. It also represents the lead  F2R  signal for circulating PAR-1 levels in the UK Biobank Pharma Proteomics Project 38 . MR analyses using this instrument revealed that each unit increase in genetically predicted PAR-1 levels was associated with 1.80-fold odds of developing long COVID (95%CI, 1.07-3.04;  P =2.7×10 -2 ). This association was consistent across several genetic instrument parameters ( Fig. 5b ;  Supplementary Table 13 ). No significant associations were observed between genetically predicted circulating protein levels and long COVID risk for other markers of platelet or endothelial activation, including P-selectin, CD40 ligand, platelet factor 4, E-selectin, intercellular adhesion molecule 1 (ICAM-1), and vascular cell adhesion molecule 1 (VCAM-1) ( P >0.50 for all). Colocalization analyses supported the hypothesis of a causal variant for trait 1 (i.e., circulating PAR-1 levels) but not trait 2 (i.e., long COVID) as the most likely (75.8%), suggesting that statistical power was insufficient to discriminate between whether the genetic association was due to shared causal variants or variants in linkage disequilibrium (i.e., horizontal pleiotropy) 39 , 40 . The probability for shared causal variants, conditional on the presence of a causal variant for both traits, was 60.9%.\nWe also evaluated the association between genetically predicted  F2R  expression and long COVID risk. Using data from the Genotype-Tissue Expression (GTEx) project 41 , we identified the tissues with the highest levels of  F2R  expression and tested the effects of genetic instruments for those most relevant to long COVID pathophysiology. As GTEx lacks data on blood platelets—an important site of  F2R  expression 35 —we supplemented our analysis with genetic instruments for platelets from the GeneSTAR Research Study 42 . Each tissue-specific instrument was constructed using the variant most strongly associated with  F2R  expression within a 200-kilobase  cis -region of the gene. As  F2R ’s  cis -region partially overlaps with those of other genes (e.g.,  F2RL1  and  F2RL2 ) 43 , we also performed analyses using only intragenic variants. Our analyses revealed that genetically predicted  F2R  expression had positive effect estimates on long COVID risk across all tissues, with nominal significance for  F2R  expression in platelets, lung, and spleen tissue ( Fig. 5c ;  Supplementary Table 14 ). The strongest effect was observed in platelets, indicating that genetic variants increasing  F2R  expression in platelets are linked to higher long COVID risk, further supporting a mechanistic link between thromboembolism and long COVID.\n\nUsing genetic data from multiple cohorts, we tested the association of genetically predicted risk of venous thromboembolism with long COVID. Our primary analysis demonstrated that a higher genetically predicted risk of venous thromboembolism was significantly associated with an increased risk of developing long COVID. This association was robust across various sensitivity analyses, replicated using data from external cohorts, and was independent of acute COVID-19 severity. Additional analyses revealed no genetic associations of venous thromboembolism with any long COVID-resembling conditions (e.g., memory loss or mood disorders), suggesting a specific role for thromboembolism-related pathways in long COVID. Furthermore, gene-focused analyses identified PAR-1—a thrombin receptor involved in platelet and coagulation activation—as a potential contributor to its pathophysiology.\nThese results have several implications. First, our findings suggest that thromboembolic pathways are involved in the development of long COVID. This extends earlier observational research linking thromboinflammatory biomarkers to long COVID 7 , 8 , 44 . Prior analyses have shown that blood biomarkers of coagulation and inflammation (i.e., fibrinogen and D-dimer relative to C-reactive protein) can help predict cognitive defects in patients hospitalized for COVID-19 up to 12 months after infection 7 . Similarly, a recent prospective study of COVID-19 patients found that individuals who developed long COVID had blood protein changes indicative of thromboinflammation at 6 months post-infection 8 . However, these studies relied on observational approaches to identify molecular risk factors for long COVID. While such approaches are well-suited to identify biomarkers for the detection and prediction of diseases, they cannot always discern true causal associations from those influenced by confounding or reverse causation 11 . MR is an approach that can help deal with these challenges by using genetic variants as instruments for specific exposures. Because an individual’s genetic makeup is fixed at conception, MR analyses are less susceptible to external influences and reverse causation 11 . We observed associations of venous rather than arterial thrombosis phenotypes with long COVID. Arterial thrombosis is genetically distinct and more strongly influenced by genes involved in lipoprotein metabolism and vascular regulation than those related to thrombosis and coagulation 45 , 46 . Taken together, our findings provide human genetic evidence implicating thromboembolism-related pathways in the pathogenesis of long COVID.\nSecond, while thrombotic events are common in individuals with acute COVID-19 and correlate partially with disease severity 31 , 32 , our findings indicate that thromboembolic pathways may contribute to long COVID independently of acute disease severity. Epidemiological research indicates that individuals with a genetic predisposition to venous thromboembolism are more likely to experience acute thrombotic complications due to COVID-19 47 , which aligns with our observation that genetic predisposition to venous thromboembolism is associated with an increased risk of hospitalized COVID-19. A prior Mendelian randomization analysis further showed that genetically predicted susceptibility to SARS-CoV-2 infection was associated with venous thromboembolism, while genetically predicted venous thromboembolism risk was linked to acute COVID-19 severity 6 . Clinical trials have shown anticoagulant therapies (e.g., heparin) can favorably affect outcomes during acute COVID-19 48 , 49 . Nevertheless, despite the association of acute COVID-19 with thromboembolic events, our findings remained robust after adjustment for genetic risk of SARS-CoV-2 infection (or other acute COVID-19 phenotypes), with no evidence of reverse causation. Therefore, thromboembolic pathways may contribute to the development of long COVID independently of acute disease severity. This is consistent with  in vitro  evidence showing that the SARS-CoV-2 spike protein binds human fibrin, promoting thromboinflammation and neuropathology independent of active infection 50 . Together with recent reports of circulating SARS-CoV-2 spike in individuals with long COVID 51 , our findings support the hypothesis that SARS-CoV-2 creates a substrate for sustained thromboinflammation post-infection, potentially leading to chronic symptoms from multiple organ systems.\nThird, gene-focused analyses identified PAR-1 as a potential contributor to the pathogenesis of long COVID. PAR-1 is encoded by the  F2R  gene and represents the prototype of a small family of G-protein-coupled protease-activated receptors (PARs) 35 , 52 . Thrombin cleaves the amino-terminal extracellular domain of PAR-1, resulting in the formation of a new amino terminus that binds intramolecularly to the receptor. In addition, endothelial PAR-1 can also undergo metalloproteinase-dependent shedding that modulates its signaling activity 53 . In platelets, receptor cleavage prompts platelet activation 52 . In endothelial cells, PAR-1 activation triggers proinflammatory and prothrombotic responses, counterbalanced by the anti-inflammatory effects of activated protein C 54 . Human genetic data indicate that certain variants within  F2R  can affect platelet PAR-1 density and thrombin-induced platelet activation 36 , 55 . While PAR-1 predominantly functions as a receptor and not as a secreted protein in the circulation, we found that higher genetically predicted plasma levels of this protein (e.g., through receptor shedding, cleavage, or in extracellular vesicles) were associated with a higher risk of long COVID. We did not observe any associations with other markers of platelet or endothelial activation. PAR-1 also promotes coagulation-driven inflammation. For instance, in an experimental mouse model of sepsis, genetic and pharmacological inhibition of PAR-1 led to reduced inflammation and coagulation 56 . Consistent with these findings, recent data suggest that virus-exposed endothelium promotes thromboinflammation through a positive feedback loop sustained and enhanced by thrombin and PAR-1 57 . Taken together, these results support the concept that PAR-1 mediates virus-induced thromboinflammation, potentially contributing to chronic symptoms associated with SARS-CoV-2 infection. Targeting PAR-1 was previously studied for the prevention of thrombotic events 58 , 59 . Large trials found that inhibition of PAR-1 with vorapaxar reduced the risk of cardiovascular death or ischemic events in selected populations 58 ; however, it also increased the risk of major bleeding including intracranial hemorrhage 58 , 59 . Future animal experiments and clinical trials are needed to evaluate PAR-1 as a potential biomarker or therapeutic target for long COVID.\nAlthough this study benefited from multiple large-scale and well-profiled datasets, as well as a robust framework for causal inference using human genetics, certain limitations must be considered when interpreting the study findings. First, while MR analyses can be used to infer causality in exposure-outcome pairs, causal inference relies on the justification of the underlying MR assumptions 11 . The present study used a robust framework with multiple sensitivity and replication analyses and assessed the key MR assumptions where possible (see  Methods ), minimizing the chance of systematically violating the MR assumptions. Still, some genetic instruments (e.g., variants in coagulation factor genes) may affect long COVID risk through mechanisms other than venous thromboembolism. Although our sensitivity analyses did not suggest bias from horizontal pleiotropy, this possibility cannot be entirely excluded. Second, we only tested genetic instruments constructed using data from European-ancestry cohorts, limiting generalizability to other ancestries. Recent evidence indicates that long COVID may manifest differently across ancestries, with Black and Hispanic individuals experiencing higher rates of respiratory, circulatory, endocrine, and other systemic symptoms compared with White individuals 60 . Further research is needed to evaluate the drivers of long COVID in individuals with other ancestries. Third, certain phenotypes such as post-viral post-exertional malaise did not have GWAS data available and could therefore not be evaluated in relation to genetically predicted venous thromboembolism risk. Fourth, the use of partially overlapping cohorts can lead to bias towards the observational exposure-outcome association; however, given the limited number of long COVID cases in FinnGen ( n cases =263), the binary nature of the outcome phenotype (i.e., long COVID), and the strong genetic instruments for venous thromboembolism (mean  F -statistic, 114.5), any bias due to sample overlap was expected to be minimal 61 . Furthermore, replication analyses with non-overlapping exposure and outcome datasets (i.e., the Million Veteran Program and PHOSP-COVID cohorts) yielded highly consistent results, suggesting sample overlap did not strongly affect our findings. Fifth, although the gene-specific association of venous thromboembolism with long COVID in  F2R  had a large effect size, this association did not reach Bonferroni-significance due to limited power. Similarly, colocalization analyses linking circulating PAR-1 to long COVID were underpowered. Still, the probability of a shared causal variant, given a causal variant for long COVID, was 61.0%, indicating that shared variants were more likely than distinct ones and arguing against confounding from linkage disequilibrium.\nIn conclusion, this study found a strong and robust genetic association of venous thromboembolism with long COVID, pointing to thromboembolic pathways as potential drivers of post-acute sequelae following SARS-CoV-2 infection. The thrombin receptor PAR-1 was identified as a putative molecular contributor to long COVID pathogenesis. These findings provide human genetic evidence for a causal link between thromboembolism and long COVID and support further research to evaluate PAR-1 as a potential biomarker and therapeutic target for long COVID.\n\nMR analyses can be used to infer causality in certain exposure-outcome relationships, given that these analyses adhere to three key instrumental variable assumptions 11  ( Fig. 1 ). First, the tested genetic instruments must comprise genetic variants that are associated with the exposure of interest (i.e., relevance assumption). Second, there can be no confounding pathway between the genetic variants comprising the tested genetic instruments and the outcome (i.e., independence assumption). Third, there can be no horizontal pleiotropy in the association of the tested genetic instruments with the outcome, meaning that the tested genetic instruments can only affect the outcome through its effects on the exposure of interest (i.e., exclusion restriction assumption).\nThe relevance assumption was tested by estimating the  F -statistics (indicating instrument strength) for the variants included in the genetic instruments for venous thromboembolism 11 . The independence and exclusion restriction assumptions were evaluated by testing the associations of the genetic instruments for venous thromboembolism with a range of long COVID-resembling conditions 11 . We also performed replication analyses using a wide range of genetic instrument parameters and pleiotropy-robust methods (e.g., MR-Egger 26 ) to further evaluate the robustness of the observed associations against the aforementioned MR assumptions.\nGenetic association data for venous thromboembolism were obtained from FinnGen 22 . The FinnGen study is a large-scale genomics initiative that has analyzed over 500,000 Finnish biobank samples and correlated genetic variation with health data to understand disease mechanisms and predispositions. The present study used data from FinnGen data release 10, which includes 412,181 participants with genetic and phenotype data from 10 contributing biobanks. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen study protocol, and all participants provided informed consent 22 .\nThe genetic instruments for venous thromboembolism (I9_VTE) were extracted from a GWAS in 21,021 venous thromboembolism cases and 391,160 controls 24 . Venous thromboembolic events were defined using diagnostic codes from hospital discharge and death cause registers. In brief, qualifying events included diagnoses of pulmonary embolism and deep vein thrombosis. The exact codes used to construct the venous thromboembolism phenotype are listed in  Supplementary Table 15 . Participants were genotyped using different Illumina (Illumina Inc., San Diego, CA, USA) and Affymetrix arrays (Thermo Fisher Scientific, Santa Clara, CA, USA) 65 . Samples with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (more than 4 standard deviations from the mean) and non-Finnish ancestry were excluded 65 . Samples were imputed to the 1000 Genomes Project reference panel (phase 3) 66  prior to association analyses adjusted for age, sex, ten principal components of ancestry, FinnGen chip version 1 or 2, and legacy genotyping batch as covariates 65 . Variants with high missingness (>2%) and Hardy-Weinberg equilibrium  P <1×10 -6  were excluded 65 . Variants with minor allele frequency <1% were also excluded.\nGenetic association data for long COVID were obtained from the Long COVID Host Genetics Initiative 25 . Data from a total of 3,018 long COVID cases and 994,582 controls from 12 individual cohorts were included for primary analyses ( Supplementary Table 1 ). Consistent with the genetic ancestry of the exposure cohort, more than 99% of participants had European ancestry. The maximal overlap in effective sample size (calculated as [4 ×  n case  ×  n control ] / [ n case  +  n control ] 25 ) between the Long COVID Host Genetics Initiative and FinnGen 22  cohorts was 10.3%. Given the limited number of long COVID cases in FinnGen ( n cases =263), the binary nature of the outcome phenotype (i.e., long COVID), and the strong genetic instruments for venous thromboembolism (mean  F -statistic, 114.5), any bias due to sample overlap is expected to be minimal 61 . Furthermore, we performed replication analyses with different exposure and outcome datasets (i.e., the Million Veteran Program and PHOSP-COVID) without any sample overlap (see  Replication analyses ). Given that these datasets were smaller—and less well-powered—than the FinnGen and Long COVID Host Genetics Initiative datasets, we chose to use the latter set of datasets for primary analyses and the other for replication analyses. Long COVID Host Genetics Initiative participants provided informed consent for participation in their respective studies, with recruitment and ethics following study-specific protocols approved by their respective institutional review boards 25 .\nStudy participants were classified as long COVID cases if they had an earlier test-verified SARS-CoV-2 infection and met long COVID criteria at least three months since SARS-CoV-2 infection or COVID-19 onset 25 . Aligning with the World Health Organization guidelines 2 , individuals meeting long COVID criteria were defined as those who (1) reported presence of COVID-19 symptoms that could not be explained by alternative diagnoses; (2) reported ongoing significant impact on day-to-day activities; or (3) had any diagnosis codes of long COVID in their electronic health records. Primary analyses used population controls (i.e., all non-cases) as controls; these individuals were defined as those who were not identified as having long COVID using the above-mentioned criteria. As such, data from a total of 3,018 long COVID cases and 994,582 controls from 12 cohorts were included for primary analyses. All cohort-specific criteria to ascertain long COVID are shown in  Supplementary Table 1 . Each cohort study applied their own methods for genotyping, genotype and sample quality control, imputation, and association analyses ( Supplementary Table 1 ), according to a central analysis plan 25 . Each cohort’s GWAS was minimally adjusted for age, age 2 , sex, age × sex, and the first 10 genetic principal components. Prior to analysis, all variants with imputation INFO score <0.6 were excluded. Meta-analysis was performed using a fixed-effects inverse-variance weighted method.\nPrimary analyses used population controls to increase statistical power and limit selective inclusion of certain groups within the population, which can introduce collider bias in MR studies. Population controls were defined as those who were not identified as having long COVID using the above-mentioned criteria 25 . Replication analyses used a stricter control definition (i.e., all individuals with a history of SARS-CoV-2 infection but without long COVID criteria [ n cases =2,975;  n controls =37,935]). Additional details on the long COVID case and control phenotypes, including diagnostic codes and study-specific criteria, can be found in the  Supplementary Table 1  and in the main Long COVID Host Genetics Initiative paper 25 .\nIn our primary analysis, we tested the association of genetically predicted venous thromboembolism risk with long COVID. To do so, we extracted genetic instruments (genetic variants) for venous thromboembolism in FinnGen 22 . All genetic instruments were variants that were present in both the exposure and outcome GWAS datasets. To ensure that our genetic instruments were strongly associated with venous thromboembolism, we only included variants that were associated with venous thromboembolism at genome-wide significance ( P <5×10 -8 ). In addition, to limit bias caused by genetic variants in linkage disequilibrium, we only retained independent variants ( R 2 <0.001; clumped within regions of 10 megabases using PLINK [version 1.90] 67 ). The linkage disequilibrium reference panel for clumping was derived from the European panel of phase 3 of the 1000 Genomes Project 66 .\nPrimary analyses used the inverse-variance weighted method with multiplicative random effects 13  to pool the effects of genetic instruments for venous thromboembolism on long COVID. In downstream analyses, unless specified otherwise, all MR analyses pooling the effects of more than three variants used this method. MR analyses pooling the effects of two or three variants used the inverse-variance weighted method with fixed effects 13 ; those testing only one variant used the Wald ratio estimator 68 .\nAll associations were presented as odds ratios (ORs) and 95% confidence intervals (CIs) per log-odds increase in genetically predicted risk for the exposure condition, along with their corresponding  P -values. Two-sided  P <0.05 indicated statistical significance in primary analyses (i.e., the inverse-variance weighted estimate of genetically predicted venous thromboembolism risk on long COVID). All MR analyses were performed using the  TwoSampleMR 69  (version 0.5.7) and  MendelianRandomization 70  (version 0.7.0) packages in R (version 4.1.0) were used for analyses.\nTo contextualize the genetic association of venous thromboembolism with long COVID (i.e., the association observed in our primary analysis), we also tested the genetic associations of different other conditions with long COVID. These conditions were chosen from FinnGen 22  to represent a variety of organ systems and pathophysiological pathways with well-powered GWAS and included: atrial fibrillation (FinnGen phenotypee code: I9_AF), asthma (J10_ASTHMA_EXMORE), chronic kidney disease (N14_CHRONKIDNEYDIS), chronic lower respiratory disease (J10_LOWCHRON), coronary artery disease (I9_REVASC), dementia (F5_DEMENTIA), diabetes mellitus (T2D), hypertension (I9_HYPTENS), migraine (MIGRAINE_TRIPTAN), multiple sclerosis (G6_MS), osteoarthritis (M13_ARTHROSIS), and rheumatoid arthritis (M13_RHEUMA) ( Supplementary Table 15 ).\nConsistent with the primary analysis, all genetic instruments for these conditions were extracted from GWAS in FinnGen (release 10) 22 . We also tested the associations of genetically predicted risk for deep vein thrombosis (I9_PHLETHROMBDVTLOW) and pulmonary embolism (I9_PULMEMB) with long COVID to corroborate the genetic association of venous thromboembolism with long COVID. All tested phenotypes were defined using nationwide registries that were linked to the biological samples and harmonized across diagnosis-, procedure-, and drug-specific codes. The exact codes used to construct the different phenotypes are listed in  Supplementary Table 15 . The GWAS for these phenotypes (including genotyping, variant- and sample-wise quality control steps, and association analyses) were identical to those used for the GWAS of venous thromboembolism in FinnGen (see  GWAS of venous thromboembolism ). All GWAS were well-powered with an adequate number of cases per condition ( n >2,000) and multiple independent ( R 2 <0.001) variants reaching genome-wide significance ( P <5×10 -8 ).\nAll genetic instruments for these conditions were identified using a  P -value threshold of 5×10 -8  and a linkage disequilibrium  R 2  threshold of 0.001. The associations of these genetic instruments with long COVID were tested using the inverse-variance weighted method. As the main aim of these analyses was to compare the association observed for venous thromboembolism (i.e., our primary analysis) with the associations observed for other, unrelated conditions, statistical significance for these analyses was set at the same threshold (i.e., two-sided  P <0.05). In addition, to compare effect estimates, we also evaluated which conditions had effect estimates within the 95%CI of the venous thromboembolism-long COVID association.\nWe performed sensitivity analyses using different MR approaches and genetic instrument selection parameters to test the robustness of the association of genetically predicted venous thromboembolism risk with long COVID. First, to evaluate the possibility of horizontal pleiotropy (i.e., effects of genetic instruments on the target outcome beyond its effects on the exposure of interest) affecting the observed associations, we performed sensitivity analyses using MR methods that allow for horizontal pleiotropy (including MR-Egger, the weighted median estimator, and the weighted mode estimator). MR-Egger is an MR method used to test and correct for horizontal pleiotropy at the cost of lower precision and statistical power 26 . The weighted median estimator is an MR method that retains statistical power and remains valid in the presence of horizontal pleiotropy, provided that more than half of the total weight comes from valid genetic instruments 71 . The weighted mode-based estimator remains valid in the presence of horizontal pleiotropy if the largest proportion of instrument weight corresponds to valid genetic instruments 72 . Second, to explore the potential effects of residual correlation between the genetic instruments, we conducted sensitivity analyses with genetic instruments obtained using various linkage disequilibrium  R 2  thresholds ( R 2 <0.0001/ R 2 <0.001/ R 2 <0.01/ R 2 <0.1). Third, we also conducted additional sensitivity analyses with genetic instruments extracted using various  P -value thresholds ( P <5×10 -4 / P <5×10 -6 / P <5×10 -8 / P <5×10 -10 ). These sensitivity analyses were performed in line with previous reports 62 , 63 , 73 . Although there is no consensus on the exact values to use for these sensitivity analyses, we chose the aforementioned  R 2  and  P -value thresholds to cover a reasonable range of parameters at fixed intervals. As MR analyses using multiple genetic variants from across the genome conventionally use a genome-wide significance threshold ( P <5×10 -8 ) and a relatively stringent  R 2  threshold ( R 2 <0.001), the different  R 2  and  P -value parameters for sensitivity analyses were centered around these values. Fourth, because long COVID can be considered a phenotype that progresses from SARS-CoV-2 infection, we performed sensitivity analyses correcting for collider bias using Slope-Hunter, a method that can adjust for collider bias in GWAS of conditional analyses with potentially correlated direct genetic effects on incidence (e.g., SARS-CoV-2 incidence) and outcome (e.g., long COVID) 74 . Fifth, we performed Steiger filtering, which removes genetic variants that explain more variation in the outcome than in the exposure of interest 27 , to evaluate the possibility of reverse causation affecting the observed results. Sixth, we performed MR analyses in the opposite direction (i.e., with long COVID as the exposure and venous thromboembolism as the outcome) to further explore the possibility of reverse causation. Genetic instruments were identified from independent ( R 2 <0.001) genome-wide significant ( P <5×10 -8 ) variants associated with long COVID in the Long COVID Host Genetics Initiative ( Supplementary Table 16 ), using the same GWAS data as those used for the outcome in primary analyses. These reverse MR analyses used the inverse-variance weighted method. Because the genetic instruments for long COVID that were identified using a  P -value threshold of 5.0×10 -8  (mean  F -statistic, 35.2) only included two variants, we also performed an additional Mendelian randomization analysis using a  P -value threshold of 5.0×10 -6  (mean  F -statistic, 23.8), yielding similar results (OR, 0.99 [95%CI, 0.95-1.03] per log-odds increase in long COVID risk;  P =0.52).\nSensitivity analyses were considered robust if (1) MR-Egger suggested no horizontal pleiotropy ( P ≥0.05 for the intercept test or  P <0.05 for the intercept test with  P <0.05 for the causal test) and all pleiotropy-robust methods (i.e., the MR-Egger, weighted median, and weighted mode-based estimators) had a consistent direction of effect; (2) MR estimates were directionally consistent across all linkage disequilibrium  R 2  thresholds used to identify genetic instruments; (3) MR estimates were directionally consistent across all  P -value thresholds used to identify genetic instruments; (4) MR analyses with Slope-Hunter were directionally consistent; (5) MR analyses with Steiger filtering yielded directionally consistent results (or Steiger filtering did not identify any “reverse causal” variants); and (6) there was no significant positive association of genetically predicted long COVID risk on venous thromboembolism.\nReplication analyses evaluated whether the observed associations were consistent across different exposure and outcome datasets. First, we identified genetic instruments for venous thromboembolism using a data source external to the FinnGen 22  dataset (i.e., the data source used to identify the genetic instruments used for our primary analyses). To this end, we used data from 8,929 venous thromboembolism cases and 181,337 controls with European ancestry from the Million Veteran Program 28 . In brief, the Million Veteran Program recruited individuals aged 19 to >100 years from more than 50 Veterans Affairs Medical Centers across the USA since 2011 75 . Participants with at least two qualifying diagnostic codes for deep vein thrombosis or pulmonary embolism in their electronic health record were classified as cases 28 . Participants were genotyped using a customized Affymetrix Axiom array (Thermo Fisher Scientific, Santa Clara, CA, USA). Samples with excess heterozygosity, excess missingness (>2.5%), or discordance between genetically inferred sex and phenotypic gender were excluded 28 . Samples were imputed to the 1000 Genomes Project reference panel (phase 3) 66  prior to association analyses adjusted for age, sex, and five principal components of ancestry 28 . Variants with ancestry-specific Hardy-Weinberg equilibrium  P <1×10 -20 , posterior call probability <0.9, imputation INFO score <0.3, or call rate <97.5% were excluded, as were variants deviating >10% from their expected allele frequency based on reference data from the 1000 Genomes Project 66 . Variants with minor allele frequency <1% were also excluded. The genetic instruments for venous thromboembolism used for replication were identified using the same approach as for the instruments used in our primary analysis (e.g., using a  P -value threshold of 5×10 -8  and a linkage disequilibrium  R 2  threshold of 0.001). The Million Veteran Program received ethical and study protocol approval by the Veterans Affairs Central Institutional Review Board, and informed consent was obtained from all participants 28 .\nSecond, we evaluated whether the observed associations persisted when we used stricter criteria for controls in the Long COVID Host Genetics Initiative GWAS 25 . Unlike our primary analyses, which used population controls (i.e., all study participants without long COVID criteria;  n controls =994,582), these replication analyses only included proven controls (i.e., all study participants with proven SARS-CoV-2 infection but without long COVID criteria;  n controls =37,935).\nThird, we tested the association of genetically predicted venous thromboembolism risk (using genetic instruments from FinnGen) with long COVID in the Post-Hospitalization COVID-19 (PHOSP-COVID) study, a prospective cohort study designed to investigate the medium- and long-term sequelae of hospitalized COVID-19 29 , 30 . PHOSP-COVID recruited adults who were discharged from one of 53 hospitals across the United Kingdom between March 5 and November 30, 2020, after confirmed or clinician-diagnosed COVID-19 29 , 30 . A total of 1,097 participants with genetic data and information on long COVID status ( n cases =697;  n controls =400) were included in this analysis. Long COVID status was primarily based on the question “Do you feel fully recovered from COVID-19?” during the a visit occurring at least 3 months after COVID-19 diagnosis. Participants who answered “no” were classified as long COVID cases. For participants who responded with “not sure” or who had a missing response, long COVID status was determined by their answers to the EuroQol five-dimension five-level (EQ-5D-5L) questionnaire. At their first visit (i.e., at least 3 months after COVID-19 diagnosis), participants completed the EQ-5D-5L questionnaire for their health state at that moment and retrospectively for their health state before COVID-19 diagnosis. Patients were categorized as having long COVID if the difference between the current and pre-COVID-19 EQ-5D-5L scores was ≤-0.1. Samples were genotyped using the Illumina Global Screening Array v3.0 (Illumina Inc., San Diego, CA, USA). Samples with call rates <95%, discordance between genetically imputed and recorded sex, excess relatedness (third degree of kinship), or excess heterozygosity (more than 4 standard deviations from the mean) were excluded, as were duplicates. Genetic variants with Hardy-Weinberg equilibrium  P <1×10 -6 , call rates <99%, or minor allele frequencies <1% were also excluded. Genetic association analyses for long COVID were adjusted for age, age 2 , sex, age × sex, and ten principal components of genetic ancestry. The PHOSP-COVID study was approved by the Leeds West Research Ethics Committee, and all participants provided informed consent 29 , 30 .\nTwo-sided  P <0.05 indicated statistical significance for all replication analyses. In addition to the three main replication analyses, which used the same methods as those used for our primary analyses (i.e., genetic instruments with  P <5×10 -8  and  R 2 <0.001 tested using the inverse-variance weighted method), we also performed additional analyses using different  P -value ( P <5×10 -4 / P <5×10 -6 / P <5×10 -8 / P <5×10 -10 ) and linkage disequilibrium  R 2  ( R 2 <0.0001/ R 2 <0.001/ R 2 <0.01/ R 2 <0.1) thresholds to identify genetic instruments. Analyses were considered robust if they were directionally consistent across all genetic instrument construction parameters.\nWe tested the associations of genetically predicted venous thromboembolism risk with acute COVID-19 in the COVID-19 Host Genetics Initiative (release 7) 17 , 33 . The COVID-19 Host Genetics Initiative includes GWAS data for three acute COVID-19 categories based on disease severity: SARS-CoV-2 infection (defined as any SARS-CoV-2 infection with or without symptoms of any severity); hospitalized COVID-19 (defined as hospitalization due to symptoms associated with SARS-CoV-2 infection); and critical COVID-19 (defined as hospitalization with respiratory support or death due to symptoms associated with SARS-CoV-2 infection) 17 , 33 . All three acute COVID-19 severity categories were tested as separate outcomes. We only used genetic data from individuals with European ancestry to match the ancestry of the outcome cohort (i.e., the COVID-19 Host Genetics Initiative 17 , 33 ) with that of the exposure cohort (i.e., FinnGen 22 ). All protocols for studies included in the COVID-19 Host Genetics Initiative followed local ethics recommendations, and informed consent was obtained when required 17 , 33 .\nThe different GWAS meta-analyses—excluding the 23andMe samples—included 122,616 cases and 2,475,240 controls for SARS-CoV-2 infection; 32,519 cases and 2,062,805 controls for hospitalized COVID-19; and 13,769 cases and 1,072,442 controls for critical COVID-19 17 , 33 . Each contributing cohort genotyped the samples, performed variant-wise and sample-wise quality control, and conducted association analyses independently following a central analysis plan. The different cohort-specific GWAS were filtered for variants with imputation INFO scores >0.6 and meta-analyzed using the inverse-variance weighted method. Variants with minor allele frequency <1% were excluded. Consistent with our primary analysis, we tested the associations of our main selection of genetic instruments for venous thromboembolism with the three aforementioned acute COVID-19 phenotypes using the inverse-variance weighted MR method.\nWe performed multivariable MR 34  analyses to estimate the direct effects of genetic predisposition to venous thromboembolism on long COVID while adjusting for its impact on acute COVID-19. Multivariable MR is an extension of conventional (i.e., univariable) MR that allows for multiple exposures to be modelled at once 34 . We performed three sets of multivariable MR analyses; in each analysis, venous thromboembolism was modelled as an exposure together with one acute COVID-19 phenotype (i.e., SARS-CoV-2 infection, hospitalized COVID-19, and critical COVID-19) while long COVID was considered the outcome. Genetic instruments were independent variants ( R 2 <0.001) reaching genome-wide significance ( P <5×10 -8 ) in either the GWAS of venous thromboembolism or the GWAS of the acute COVID-19 phenotype under study. The independent genetic effects of each exposure on long COVID were estimated using the inverse-variance weighted method for multivariable MR studies, implemented using the  mr_mvivw()  function from the  MendelianRandomization 70  package (version 0.7.0) in R.\nAs the main aim of the conventional (i.e., univariable) MR analyses was to compare the genetic effects of venous thromboembolism on long COVID (i.e., our primary analysis) with those on acute COVID-19 phenotypes, statistical significance for these analyses was set at the same threshold (i.e., two-sided  P <0.05). We also evaluated which associations had effect estimates within the 95%CI of the venous thromboembolism-long COVID association. Results from multivariable MR analyses were considered robust if venous thromboembolism had a significant independent association (i.e., two-sided  P <0.05) with long COVID across models.\nWe tested the genetic associations of venous thromboembolism with a range of “long COVID-resembling” phenotypes. These long COVID-resembling phenotypes included various conditions sharing similar symptoms—yet likely distinct mechanisms—to those observed in long COVID 3 . The phenotypes were chosen based on prior evidence suggesting overlapping symptoms with long COVID 3  and were tested if they had GWAS data available in FinnGen (release 10) 22 .\nAll phenotypes were defined using nationwide registries that were linked to the biological samples and harmonized across diagnosis-, procedure-, and drug-specific codes 22 . The tested phenotypes included: chronic lower respiratory disease (FinnGen phenotype code: J10_LOWCHRON), functional dyspepsia (K11_FUNCDYSP), irritable bowel syndrome (K11_IBS), endometriosis (N14_ENDOMETRIOSIS), fibromyalgia (M13_FIBROMYALGIA), anxiety disorders (F5_ALLANXIOUS), memory loss (MEMLOSS), sleep disorders (SLEEP), mood disturbances (F5_MOOD), depression (F5_DEPRESSIO), and postviral fatigue (G6_POSTVIRFAT). Additional information on the tested long COVID-resembling phenotypes can be found in  Supplementary Table 10 . The GWAS for these phenotypes (including genotyping, variant-wise and sample-wise quality control, and association analyses) were identical to those used for the GWAS of venous thromboembolism in FinnGen (see  GWAS of venous thromboembolism ).\nTo avoid sample overlap, we used genetic instruments from the Million Veteran Program 28  (see  Replication analyses ) to test the effects of genetically predicted venous thromboembolism risk on long COVID-resembling phenotypes. These analyses used the inverse-variance weighted MR method. Statistical significance was set at the same threshold as in our primary analysis (i.e., two-sided  P <0.05). We also evaluated which associations had effect estimates within the 95%CI of the venous thromboembolism-long COVID association.\nWe constructed “gene-specific genetic instruments” by identifying variants associated with venous thromboembolism within or near different coagulation-related genes/loci. These were defined as all genes included in the “ Complement and coagulation cascades ” gene set from the KEGG database 76  ( Supplementary Table 17 ). All gene-specific genetic instruments were genetic variants associated with venous thromboembolism within a  cis -region 200 kilobases of the indicated coagulation-related gene in FinnGen 22 . Because a  cis -region only comprises a small region within the genome, and because sensitivity analyses showed that genetically predicted venous thromboembolism risk was still associated with long COVID when using more lenient  P -value thresholds (i.e.,  P <5×10 -4  or  P <5×10 -6 ) ( Supplementary Table 4 ), we also included genetic variants associated with venous thromboembolism at a sub-genome-wide significance threshold to increase the number of genes with valid gene-specific genetic instruments. More specifically, if a gene’s  cis -region included variants associated with venous thromboembolism at  P <5×10 -8 , we used this threshold to identify the genetic instruments; if not, we used  P <5×10 -6  or  P <5×10 -4 , depending on the presence of  cis -variants surpassing those  P -value thresholds. Independent genetic variants were selected using a linkage disequilibrium  R 2  threshold of 0.001. When the genetic instruments were the same for different neighbouring genes, we grouped these genes into one (i.e., a locus).\nMR analyses pooling the effects of more than three genetic variants used the inverse-variance weighted method with multiplicative random effects 13 ; those pooling the effects of two or three variants used the inverse-variance weighted method with fixed effects 13 ; those testing the effects of only one variant used the Wald ratio estimator 68 . Considering that genetically predicted venous thromboembolism was strongly associated with long COVID and that this association was robust across multiple genetic instrument  P -value thresholds, and that gene-specific genetic instruments inherently include fewer genetic variants than genetic instruments identified from the entire genome, we did not formally correct these analyses for multiple comparisons. Instead, we prioritized associations based on effect size and used two-sided  P <0.05 to indicate statistical significance.\nBecause  F2R ’s gene-specific genetic instrument had largest effect on long COVID across all gene-specific genetic instruments, downstream analyses focused on this gene and its gene products (i.e.,  F2R  RNA and the PAR-1 protein). First, we tested the effects of genetically predicted circulating levels of PAR-1 on long COVID risk using a genetic instrument from the UK Biobank Pharma Proteomics Project (UKB-PPP) 38 .\nThe UKB-PPP is a collaboration between 13 biopharmaceutical companies that funded the measurement of 2,923 circulating proteins in participants from the UK Biobank, a population-based cohort of individuals aged 40–69 years recruited between 2006 and 2010 across the United Kingdom 38 . A total of 54,306 UK Biobank participants who donated blood samples at baseline or follow-up study visits were selected for proteomic profiling and were included in the UKB-PPP cohort. These samples were collected in EDTA (9 mL) vacutainers and fractioned to 850 μL aliquots of EDTA plasma, buffy coat, and red cells. The blood samples were centrifuged at 2,500 g for 10 minutes at 4°C. Proteins were quantified using proximity extension assay technology (Olink Explore 3072 platform [Olink Proteomics, Inc; Waltham, MA, USA]). Sun  et al . 38  performed a GWAS of circulating proteins measured using Olink technology in a discovery cohort of 35,571 UKB-PPP participants of European ancestry. Genotyping, imputation, and quality control of the UK Biobank samples were performed as described previously 38 , 77 . In addition to checking for sex mismatch, sex chromosome aneuploidy, and heterozygosity, imputed genetic variants were filtered for imputation INFO score >0.7. Individual protein levels were inverse-rank normalized prior to association analyses, which were adjusted for age, age 2 , sex, age × sex, age 2  × sex, batch, assessment center, genetic array, time between blood sampling and measurement, and 20 principal components of genetic ancestry. The UK Biobank received approval from the North West Multi-center Research Ethics Committee, and all participants provided informed consent.\nWe extracted a genetic instrument for circulating PAR-1 levels that was an independent (linkage disequilibrium  R 2 <0.001) genetic variants within 200 kilobases of  F2R  strongly associated with circulating PAR-1 levels ( P <5×10 -8 ). Such variants are also called  cis- protein quantitative trait loci ( cis- pQTLs) and have favorable effects on the assumptions of MR analyses 62 – 64 . The aforementioned thresholds resulted in only one variant (i.e., rs168753) that was used as the genetic instrument for circulating PAR-1; therefore, the association of this instrument with long COVID was tested using the Wald ratio estimator 68 . Sensitivity analyses used different  P -value ( P <5×10 -4 / P <5×10 -6 / P <5×10 -8 / P <5×10 -10 ) and linkage disequilibrium  R 2  ( R 2 <0.0001/ R 2 <0.001/ R 2 <0.01/ R 2 <0.1) thresholds to identify genetic instruments. In addition, because  F2R ’s  cis -region partially overlaps with those of neighboring genes (i.e.,  F2RL1  and  F2RL2 ) 43 , we also performed analyses with genetic instruments selectively including intragenic variants to limit pleiotropic effects of extragenic variants potentially influencing other genes’ expression patterns. Two-sided  P <0.05 indicated statistical significance for this corroboratory analysis, and directional consistency was used as the criterium for robustness in sensitivity analyses.\nWe next examined whether the observed association between genetically predicted PAR-1 levels and long COVID reflected general platelet or endothelial activation rather than a PAR-1–specific effect. To this end, we also tested associations of genetically predicted circulating levels of other platelet and endothelial activation markers—including P-selectin, CD40 ligand, platelet factor 4, E-selectin, intercellular adhesion molecule 1 (ICAM-1), and vascular cell adhesion molecule 1 (VCAM-1)—with long COVID.\nTo complement the MR, we performed colocalization analyses to test for shared causal variants between circulating PAR-1 levels and long COVID. Analyses were carried out with variants within 200 kilobases of  F2R , using the  coloc.abf()  function from the R package  coloc  (version 5.2.2) 78 . The output of these analyses was expressed as test statistics that estimate the posterior probabilities of five hypotheses: (1) no causal variant for either trait; (2) a causal variant for the first but not the second trait; (3) a causal variant for the second but not the first trait; (4) distinct causal variants underlying both traits; and (5) one shared causal variant underlying both traits 39 , 40 . A high posterior probability for the fifth hypothesis (>80%) indicates strong evidence of colocalization, suggesting a high likelihood of a shared causal variant driving both traits. A high posterior probability for the fourth hypothesis (>80%) indicates strong evidence of distinct causal variants, suggesting a high likelihood of confounding by linkage disequilibrium (or horizontal pleiotropy) in the corresponding MR analysis. If there is no high probability for the fourth or fifth hypothesis (<80% for each) but the MR association is statistically significant and not a false positive finding, this suggests that the colocalization analysis is likely not adequately powered to distinguish between whether the MR association is due to a shared causal variant or a variant in linkage disequilibrium (i.e., horizontal pleiotropy) 39 , 40 . In the latter situation, we also estimated the probability for shared causal variants, conditional on there being at least one variant associated with each trait under study, calculated as: (PP H4 ) / (PP H3  + PP H4 ) 39 , 40 , where PP H3  indicates the posterior probability of the fourth hypothesis (i.e., distinct causal variants for both traits) and PP H4  that of the fifth hypothesis (i.e., one shared causal variant for both traits).\nIn addition to analyses focused on the PAR-1 protein, we also performed MR analyses testing the effects of genetically predicted  F2R  RNA levels on long COVID risk. To this end, we selected five tissues or cell types with (1) high  F2R  expression (queried using the Genome-Tissue Expression [GTEx] database 41 ); (2) biological relevance to long COVID; and (3) at least one valid variant near  F2R  associated with  F2R  expression (i.e.,  cis -expression quantitative trait locus [ cis -eQTL]). The highest median  F2R  RNA levels in GTEx were observed for the following tissues: aorta, coronary artery, tibial artery, skin (not sun-exposed), skin (sun-exposed), minor salivary gland, lung, and spleen 41 . Because we assumed that the skin (both sun-exposed and not sun-exposed) and minor salivary gland had less direct biological relevance to long COVID in comparison with the other tissues, we did not carry these tissues forward for further analyses. Given high  F2R  expression in platelets 35 —a cell type not included in the GTEx database 41 —we added platelets to our list of tissues and cell types of interest. Genetic instruments for platelets were obtained from the GeneSTAR Research Study 42 , while those for the other tissues (i.e., aorta, coronary artery, tibial artery, lung, and spleen) were queried in GTEx (v8) 41 .\nWe used the GTEx (v8) dataset 41  to select genetic instruments for  F2R  expression in the aforementioned tissues. In brief, the GTEx project has created a resource of gene expression levels from normal (i.e., non-diseased) tissues from deceased human donors. GTEx included 838 donors with available RNA sequences and genotypes, collectively providing 17,382 samples from 52 tissues and two cell lines 79 . A total of 715 (85.3%) participants had European ancestry and were used to identify genetic instruments in the present analysis. RNA sequencing libraries were generated using the Illumina TruSeq protocol (Illumina Inc., San Diego, CA, USA); gene-level expression levels were quantified as reads per kilobase of transcript per million mapped reads 41 . Participants were genotyped using the Illumina Human Omni 2.5M and 5M Beadchips and imputed using the multi-ethnic reference panel from the 1000 Genomes Project (phase 3) 66 . Samples with fewer than 10 million mapped reads or outlier expression measurements were removed 41 . Variants with call rates <95%, minor allele frequencies <1%, Hardy-Weinberg equilibrium  P <1×10 -6 , or imputation INFO score <0.4 were excluded. Genetic association analyses of transcript levels were adjusted for PEER factors, sex, genotyping platform, and three principal components of genetic ancestry. The protocol for the GTEx project was reviewed by Chesapeake Research Review Inc., Roswell Park Cancer Institute’s Office of Research Subject Protection, and the institutional review board of the University of Pennsylvania. For all donors, consent was obtained via next-of-kin consent 41 .\nWe used data from the GeneSTAR Research Study 42  to obtain genetic instruments for  F2R  expression in platelets. In brief, the GeneSTAR Research Study was designed to examine gene-environment determinants of platelet reactivity in response to low-dose aspirin therapy 80 . GeneSTAR included siblings identified from probands with coronary artery disease at age <60 years, as well as the spouses and adult offspring of the siblings. A total of 307 apparently healthy individuals were included for platelet eQTL analyses, of whom 180 (58.6%) had European ancestry and were used to construct genetic instruments in the present analysis 42 . Once platelets were isolated from whole blood samples, RNA sequencing libraries were generated using the Illumina TruSeq protocol (Illumina Inc., San Diego, CA, USA). Transcript abundances were quantified as fragments per kilobase of transcript per million sequenced reads. Participants underwent whole genome sequencing as part of the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program 81 . Low-expression outliers and samples with poor quality or unexpected familial relationships were excluded 42 . Variants were filtered for autosomal single-nucleotide polymorphisms with at least two samples per genotype and a call rate of >80%. Genetic association analyses of transcript levels were adjusted for sex, age, RNA sequencing batch, 15 principal components of the filtered and log-transformed gene expression matrix, and three principal components of genetic ancestry. The GeneSTAR Research Study was approved by the institutional review board of the Johns Hopkins Medical Institutions, and all participants provided informed consent 80 .\nWe identified a genetic instrument for tissue-specific  F2R  expression using independent (linkage disequilibrium  R 2 <0.001) genetic variants within 200 kilobases of  F2R  associated with  F2R  RNA levels from the GeneSTAR Research Study 42  (for platelets) or GTEx project 41  (for the other tissues). Similar to our analyses using gene-specific genetic instruments for venous thromboembolism (see  Gene-specific genetic instruments for venous thromboembolism ), each tissue-specific genetic instruments’  P -value threshold depended on the association strength of the different variants near  F2R . If  F2R ’s  cis -region included variants associated with tissue-specific  F2R  expression at  P <5×10 -8 , we used this threshold for the genetic instrument; if not, we used  P <5×10 -6  or  P <5×10 -4 , depending on the presence of  cis -variants surpassing those  P -value thresholds. This approach resulted in one genetic variant (i.e., the strongest  cis -eQTL) for each tissue-specific genetic instrument; therefore, the association of these instruments with long COVID was tested using the Wald ratio estimator 68 . The only tissue without adequate  cis -eQTLs for  F2R  was coronary arterial tissue, which was excluded from the analyses. Similar to the analyses using  cis- pQTLs for PAR-1, we also performed analyses that only included intragenic variants. The genetic instruments for  F2R  expression in tibial arterial tissue included rs253072 (using variants within 200 kilobases) and rs250730 (using only intragenic variants), respectively; those for splenic tissue included rs253072 and rs250750; those for pulmonary tissue included rs2227750 and rs12735; and those for platelets included rs253072 and rs32934. We could only obtain an adequate instrument for aortic tissue using variants within 200 kilobases (i.e., rs9293695) and not using intragenic variants alone. Two-sided  P <0.05 indicated statistical significance for these confirmatory analyses.","source_license":"CC-BY-4.0","license_restricted":false}