Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID

Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID Zhongshan Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5807438/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Long COVID presents a significant public health challenge, characterized by over 200 reported symptoms spanning multiple organ systems. Despite its complexity, genome-wide association studies (GWAS) offer a pathway to uncovering genetic risk factors, though progress has been hindered by the disorder's symptom heterogeneity and the limited power of available datasets. Recent long COVID GWASs have highlighted key genetic associations, such as variants close to FOXP4 , ABO , and HLA-DQA1 , while underscoring the limitations posed by small sample sizes, restricted diversity, and misclassification biases. Here, we performed an integrative analysis using COVID-19 GWAS data from the Host Genetics Initiative (HGI, release 7) by leveraging proxy phenotypes to overcome the challenges of limited sample size or heterogeneity of long COVID-specific cohorts. 62 independent single nucleotide polymorphisms (SNPs) were prioritized as potentially associated with long COVID and categorized into three groups: (1) Severe COVID-19-Specific SNPs, such as SNPs mapped to two well-known loci involved in SARS-CoV2 entry ( ACE2 [rs190509934] and TMPRSS2 [rs12329760]), as well as variants of DPP9 (rs7251000), FOXP4 (rs12660421) and HLA-DQA2 (rs17219281), exhibiting associations with severe COVID-19 but displaying weaker signals in non-hospitalized COVID-19 cases; (2) SNPs Associated with Both Severe and Mild COVID-19, including the SNP close to ABO (rs505922), representing a catalog of SNPs predispose to both acute COVID-19 and chronic long COVID; and (3) Non-Hospitalization-Specific SNPs, such as variants in KCTD16 (rs62401842) and WASF3 (rs56143829), highlighting genetic contributors specific to mild COVID-19 cases that might also contribute to long COVID. Further transcriptome-wide association studies (TWASs) across 48 GTEx tissues, leveraging GWAS data on COVID-19 hospitalization, susceptibility, and long COVID from the HGI consortium, revealed distinct tissue-specific patterns of association. Compared to the acute COVID-19 phenotypes, long COVID exhibited weaker association signals across heart, brain, and muscle-related tissues, as determined by correlations between gene expression of adjacent genes of candidate SNPs (43 of 62 SNPs) and different COVID phenotypes. Notable TWAS hits included DPP9 (rs7251000), CCR1 (rs17078348), and THBS3 (rs41264915). Phenome-wide TWASs also identified additional significant associations with long COVID related phenotypes, such as HLA-DQA1 (rs17219281), HLA-A (rs9260038), and HLA-C (rs1634761) associated with immune-related diseases, GSDMB (rs9916158) associated with asthma, FOXP4 (rs12660421) linked with sleep duration, highlighting their potential roles in long COVID pathophysiology. Therefore, current integrative approach offers a scalable framework for long COVID research by maximizing the statistical power of existing large-scale COVID-19 GWASs and provides novel insights into the genetic underpinnings of long COVID. Biological sciences/Genetics/Genetic association study/Genome-wide association studies Health sciences/Diseases/Infectious diseases/Viral infection COVID-19 SARS-CoV2 Long COVID GWAS TWAS Hospitalization Susceptibility Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Long COVID remains a critical and complex public health issue, with a diverse array of over 200 reported symptoms affecting multiple organ systems. These symptoms, which include fatigue, cognitive impairment, respiratory issues, and cardiovascular problems, can persist well beyond the acute phase of COVID-19 infection, revealing a unique challenge for researchers to identify genetic risk factors using genome-wide association studies (GWAS), as the wide variation in symptom clusters may arise from different biological mechanisms of long COVID and require extensive sample sizes to detect meaningful associations. To address this complexity, long COVID GWAS requires not only large and diverse patient datasets but also refined approaches to phenotype classification and statistical analyses. Among the most significant efforts to understand the genetic basis of long COVID is the large GWAS conducted by Lama et al. 1 , which analyzed approximately 3,500 long COVID cases but not powerful enough to reveal the full genetic landscape due to several key limitations. Primarily, the study drew cases from early phases of the COVID-19 pandemic based on more immediate post-acute symptoms rather than on late-onset long COVID, which can emerge years after initial infection. As a result, potential long COVID cases with delayed symptom onset may be classified as controls, resulting in dilution of statistical power needed to detect true genetic associations. Despite these limitations, Lama et al. 1 identified a single gene, FOXP4 , on chromosome 17, reaching genome-wide significance for association with long COVID. FOXP4 variants were found to be strongly associated with both long COVID and severe COVID-19, in line with a shared genetic predisposition that may drive both conditions. However, the stronger association with severe COVID-19 suggests that FOXP4 may contribute primarily to risk factors influencing severe acute COVID-19 cases that later progress to long COVID. Given that long COVID can also follow mild COVID-19, additional genetic contributors associated specifically with mild cases likely remain undiscovered. Another long COVID GWAS conducted by Taylor et al. 2 provided further insights into genetic factors specific to a subtype of long COVID by investigating genetic overlap between long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a condition with substantial symptom overlap, including chronic fatigue and cognitive impairments. They suggested that it is beneficial to analyze long COVID subtypes separately, as different symptom profiles could be associated with distinct genetic and biological pathways. However, their GWAS faced limitations due to its relatively small sample size (n < 1,000 long COVID cases) with restricted statistical power. Additionally, the study only reported SNPs derived by network analysis and did not identify any SNPs that reached genome-wide significance for long COVID, emphasizing the need for larger, more statistically robust studies. The absence of genome-wide significant SNPs underscores a major challenge in long COVID research: even well-defined subtypes require substantial sample sizes to detect genetic variants with confidence. More recently, a multi-ancestry GWAS was deposited at the preprint server medRxiv by Chaudhary et al. 3 , which expanded the scope of long COVID GWAS by including participants from diverse ancestral backgrounds. Notably, several immune-related loci, such as ABO , BPTF , and HLA-DQA1 , were reported to be linked with chronic fatigue syndrome (CFS), fibromyalgia, and depression, suggesting potential immune dysregulation in long COVID. Despite this, this multi-ancestry long COVID GWAS still has limited sample size in non-European samples regarding the heterogeneity of long COVID symptoms, leading to the lack of sufficient power to detect associations for less common symptom clusters or subtle genetic variants. Furthermore, the study relied on self-reported cases, which may introduce misclassification biases, particularly given that long COVID lacks a single diagnostic marker. Self-reported symptoms, though valuable for capturing diverse experiences, can introduce noise that complicates statistical analysis and gene identification. To advance insights into long COVID genetics, additional large-scale GWAS or alternative analyses that utilize existing COVID-19 GWAS summary statistics are essential to uncover genetic factors masked by current sample size limitations. Integrative approaches leveraging large publicly available GWAS of COVID-19 hospitalization and SARS-CoV-2 susceptibility may provide the statistical power required to detect SNPs linked to long COVID. In this study, we performed an integrative analysis of COVID-19 GWASs from the Host Genetics Initiative (HGI, release 7), assuming these phenotypes of COVID-19 hospitalization and susceptibility considered together to be a proxy phenotype of long COVID. Hypothesizing that long COVID may develop after either mild or severe COVID-19, this study was aimed to identify potential shared or hospitalization-specific genetic elements, as well as non-hospitalization-specific genetic elements. The current analysis prioritized 62 top independent SNPs potentially associated with long COVID into three SNP categories, containing SNPs mapped to two well-known loci involved in SARS-CoV2 entry ( ACE2 [rs190509934] and TMPRSS2 [rs12329760]): (1) “Severe COVID-19-Specific SNPs”, such as these previously reported long COVID risk SNPs rs12660421 ( FOXP4 ), rs10774679 ( OAS3 ), and rs17219281 ( HLA-DQA1 ), which show significant associations in both GWASs of hospitalized COVID-19 vs. general population and SARS-CoV-2 infection vs. general population with relatively weaker associations in hospitalized vs. non-hospitalized COVID-19; (2) “SNPs Associated with Both Severe and Mild COVID-19”, including the well-known long COVID risk SNPs, rs505922 ( ABO ), covering SNPs that may contribute to long COVID risk across a range of COVID-19 severities; (3) an extra SNP category specifically emerged from non-hospitalized COVID-19 patients (“Non-hospitalization-specific long COVID SNPs”) was further explored to prioritize 20 potential long COVID SNPs, such as the reported long COVID SNP rs62401842 ( KCTD16 ), as well as rs56143829 ( WASF3 ), with a relative lower association significance threshold. These prioritized SNPs were evaluated in association with long COVID based on published long COVID GWASs, and transcriptome-wide association studies (TWAS) were performed for these HGI COVID-19 hospitalization/susceptibility/long COVID GWASs to determine the expression level association of genes close to these candidate SNPs with these phenotypes, resulting in the conclusion that long COVID is likely to have different tissue etiology, particularly occurring in heart, brain, and muscle related tissues. Further phenome-wide TWASs were conducted to evaluate additional candidate genes potentially involved in long COVID. This integrative approach prioritizes SNPs as well as genes close to them with potential relevance to long COVID, offering a scalable alternative to screen long COVID risk loci by maximizing the statistical power of existing large-sample COVID-19 GWAS data from HGI. Methods Genome-wide Screening of Genetic SNPs Potentially Associated with Long COVID Given the challenge of identifying genetic associations with long COVID due to its diverse symptomatology and the limited cohort sizes in GWAS, proxy phenotypes such as SARS-CoV-2 susceptibility (also refer to COVID-19 susceptibility in this study) and COVID-19 hospitalization were leveraged to prioritize relevant SNPs. This strategy is supported by evidence that long COVID can develop following both mild and severe COVID-19, suggesting shared or distinct genetic risk factors may influence acute-phase outcomes of COVID-19 (e.g., hospitalization, non-hospitalization, or susceptibility to SARS-CoV2 infection) and subsequent long COVID susceptibility. Publicly available COVID-19 GWAS datasets were downloaded from the HGI (release 7), including HGI-C2 (COVID-19 [n=159,840] vs. the general population [n=2,782,977]), HGI-B1 (hospitalized [n=16,512] vs. non-hospitalized COVID-19 [n=71,321]), and HGI-B2 (hospitalized COVID-19 [n=44,986] vs. the general population [n=2,356,386]), and analyzed using an integrative analysis pipeline to prioritize potential long COVID SNPs. Although HGI also shared the long COVID GWASs with 4 slightly different case-control designs that were published by Lammi et al 1 , these data were not initially utilized to prioritize long COVID SNPs due to the fact that these long COVID GWASs contain potential bias to severe COVID-19, which may lead to the missing of mild COVID-19 related long COVID risk SNPs. Again, the previously published HGI long COVID GWASs only identified one genome-wide significant locus represented by FOXP4 . Therefore, in the current study the SNP prioritization workflow primarily focused on two key long COVID risk SNP categories related to severe COVID-19 that are potentially relevant to long COVID. The first SNP categories is “Severe COVID-19-Specific SNPs”, which includes SNPs associated with both SARS-CoV-2 susceptibility and COVID-19 hospitalization but tends to be more prevalent within hospitalized than in non-hospitalized cases. These SNPs are hypothesized to influence long COVID risk based on the epidemiological observation that multiple SARS-CoV2 infections and COVID-19 hospitalizations elevate the likelihood of developing the long COVID condition 4 . The following selection criteriawas applied, including (a) P ≤0.05 in HGI-C2, capturing SNPs linked to COVID-19 susceptibility; (b) P ≤0.05 in HGI-B1, identifying SNPs that predispose individuals to COVID-19 hospitalization rather than mild COVID-19; (c) P ≤0.05 in HGI-B2, identifying SNPs distinguishing hospitalized COVID-19 individuals from the general population; (d) P >0.05 in the differential GWAS of HGI-B1 vs. HGI-B2, excluding SNPs with differential effect sizes between these comparisons; (e) all require to display consistent effect directions across HGI-C2, HGI-B1, and HGI-B2. Meanwhile, the second SNP categories is “SNPs Associated with Both Severe and Mild COVID-19”, which influence both mild and severe COVID-19 outcomes that are plausible candidates for long COVID risk, given that non-hospitalized cases can also develop long COVID. These SNPs are unlikely to show significant differentiation between hospitalized and non-hospitalized cases but may exhibit associations with SARS-CoV-2 susceptibility and COVID-19 hospitalization when compared to the general population. The selection criteria for this group were performed as (a) P ≤0.05 in HGI-C2, identifying COVID-19 susceptibility-related SNPs; (b) P ≤0.05 in HGI-B2, identifying SNPs associated with COVID-19 hospitalization; (c) P >0.05 in HGI-B1, indicating no significant difference in SNP effects between severe and mild COVID-19; (d) P ≤0.05 in the differential GWAS of HGI-B1 vs. HGI-B2, capturing SNPs with distinct effects in these datasets; (e) all prioritized SNPs need to be fulfill consistent effect size directions across HGI-C2 and HGI-B2. Finally, SNPs met either of the above two sets of conditions were further evaluated based on the criterion of passing the P <1E-7 in any of the three GWAS datasets (HGI-B1, HGI-B2, or HGI-C2). This prioritization framework provides a systematic approach to identifying genetic variants potentially predisposing individuals to long COVID by leveraging large-scale COVID-19 GWAS data, resulting in 42 top independent SNPs that were subjected to further evaluation on its potential involvement in long COVID by COVID19_GWAS_Analyzer 5 , 6 (https://github.com/chengzhongshan/COVID19_GWAS_Analyzer). Identification of SNPs Potentially Associated with Long COVID in Non-Hospitalized Cases Current COVID-19 GWAS analyses from the HGI are predominantly biased toward hospitalized cases, potentially overlooking SNPs associated with long COVID that emerge specifically in non-hospitalized individuals. To address this gap, SNPs were filtered using the following criteria: (1) P >0.05 in the GWAS comparing hospitalized cases with the general population (HGI-B2), to exclude SNPs associated with COVID-19 hospitalization; (2) P ≤0.05 in the GWAS comparing hospitalized cases with non-hospitalized cases (HGI-B1), to capture SNPs significantly differentiating these two groups; (3) P ≤0.05 in the differential GWAS between HGI-B1 and HGI-B2, to retain SNPs with differential association signals between these datasets. To further refine the selection, independent SNPs met a suggestive significance threshold ( P <1E-5) were prioritized, ensuring no other genome-wide significant SNPs were located within a 500-kb window in either HGI-B2 or HGI-C2. The use of a relaxed significance threshold aimed to account for the higher heterogeneity among non-hospitalized cases in HGI-B1 and the inherent bias of HGI GWASs toward identifying SNPs associated with hospitalization. This approach enables the identification of SNPs that might represent unique genetic contributors to long COVID risk in non-hospitalized individuals. Comparing Two COVID-19 Hospitalization GWASs Using the Differential Z-Score Method To assess differences in effect sizes of SNPs between two COVID-19 GWAS datasets, such as one GWAS comparing hospitalized cases with non-hospitalized cases (HGI-B1) and the other GWAS comparing hospitalized cases with the general population (HGI-B2), as well as comparison SNP effect size between sex-stratified COVID-19 hospitalization GWASs, differential z-score method 6 was applied in current study. This approach quantifies the disparity in effect sizes (β) of each SNP between two GWASs by calculating a differential z-score, which incorporates adjustments for potential sample overlap between studies. The corresponding p-value was derived from the normalized differential z-scores. The differential z-score is defined as: Here, gwas1.β and gwas2.β represent the effect sizes for a given SNP from the two GWASs, while gwas1.se and gwas2.se denote the corresponding standard errors. To account for genome-wide correlation in effect sizes caused by overlapping samples, differential z-scores were normalized using the SAS procedure “proc stdize” with the “std” method. Subsequently, differential p-values were computed based on the normalized differential z-scores. The cumulative distribution function of the standard normal distribution (“pnorm”) was used to calculate p-values from the normalized scores. Transcriptome-Wide Association Analysis Using S-MetaXcan Transcriptome-wide association analysis (TWAS) was conducted using S-MetaXcan 7 , which imputes gene expression from GWAS summary statistics. Four COVID-19 GWAS datasets from the HGI were analyzed, including HGI-B1, HGI-B2, HGI-C2, and HGI-B1vs-B2, alongside four long COVID GWAS datasets with subtly distinct case-control definitions from HGI 1 and an additional 221 GWASs from the UK Biobank (UKB) (all sample sizes >5,000) shared by Benneal’s lab (https://www.nealelab.is/uk-biobank). Reference transcriptomic data from 48 tissues in GTEx v7 8 were utilized for the analyses. S-MetaXcan 7 employs multivariate regression to integrate evidence across different GTEx tissues (n=48), enhancing detection power. Default settings were applied, with the conditional number for eigenvalue set at 30 during singular value decomposition (SVD) truncation and genes on autosomal chromosomes were included in the analysis. First, TWAS results for COVID-19 (n=8, collectively referred to as COVID TWASs) were evaluated for genes proximal to prioritized long COVID-associated SNPs (±500kb to the candidate SNP termed as tag SNP). Genes showing nominal significance (P < 0.05) in any of the COVID TWASs across the 48 GTEx tissues were prioritized for further investigation. To expand these findings, phenome-wide association analyses were conducted for the prioritized genes. Associations were examined across a TWAS database aggregating 193 previously published TWAS results 7 and 229 TWASs generated in this study, yielding a total of 418 TWAS datasets. Statistical analysis software and availability of codes and data Unless otherwise specified, all analyses were performed using SAS OnDemand for Academics (freely available at https://www.sas.com/en_us/software/on-demand-for-academics.html). The scripts and datasets used in this study are available from the corresponding author upon reasonable request. Local Manhattan plots for the top candidate long COVID-associated SNPs and all supplementary tables are publicly accessible at the GitHub link (https://github.com/chengzhongshan/COVID19_GWAS_Analyzer/tree/main/LongCOVID_data_and_scripts/LongCOVID_SNP_LocalManhattanPlots). Results Prioritizing Genetic Variants Associated with Long COVID Using COVID-19 GWAS Data Long COVID, characterized by over 200 symptoms spanning multiple organ systems, presents significant challenges for GWAS due to difficulties in assembling large, representative cohorts. To date, only four genome-wide significant loci— FOXP4 , ABO , HLA-DQA1 , and BPTF —have been identified for long COVID 1 , 3 . To uncover additional genetic long COVID risk loci, we developed an integrative workflow (Fig. 1a) leveraging publicly available COVID-19 GWAS data. Acute COVID-19 phenotypes, such as SARS-CoV-2 susceptibility, COVID-19 hospitalization, and non-hospitalization, were used as proxy traits for the long COVID phenotype to prioritize genetic variants potentially linked to long COVID. The analysis utilized data from the HGI release 7, including GWAS of hospitalized vs. non-hospitalized cases (HGI-B1), hospitalized cases vs. the general population (HGI-B2), and SARS-CoV-2 infection vs. the general population (HGI-C2). These GWASs comprise large sample sizes (16,512, 44,986, and 159,840 COVID-19 cases for HGI-B1, HGI-B2, and HGI-C2, respectively; Fig. 1a) and exhibit minimal genomic inflation (λ close to 1). As shown in Supplementary Fig. S1 and Fig. 1a, loci near SLC6A20 and ABO displayed notable significance in association signals. These regions, previously validated as COVID-19 risk loci in the literature 9 , are well-established contributors to COVID-19 susceptibility and severity. Taken together, the robustness of these three large COVID-19 GWAS datasets, coupled with the strong correlation between long COVID, SARS-CoV-2 susceptibility, and COVID-19 severity, underscores their utility in identifying potential genetic risk variants for long COVID. Categorization of Prioritized Long COVID Risk SNPs Related to COVID-19 Severity To explore the genetic basis of long COVID, we hypothesized that large, previously published COVID-19 GWAS datasets could identify SNPs associated with long COVID. Using an integrative workflow (Fig. 1), we identified 42 prioritized SNPs (see Table 1), categorized into two groups: severe COVID-19-specific SNPs (n=27) and SNPs associated with both severe and mild COVID-19 (n=15). The first category includes SNPs linked predominantly to SARS-CoV-2 susceptibility and severe disease outcomes, such as hospitalization, and are more prevalent in hospitalized cases than in non-hospitalized cases. The second category consists of SNPs associated with both severe and mild COVID-19, showing consistent associations with SARS-CoV-2 susceptibility and hospitalization, but no significant differentiation between hospitalized and non-hospitalized cases, i.e., P >0.05 in HGI-B1, P <0.05 in HGI-B2 and HGI-C2. Of the 42 SNPs, except two SNPs, rs150345524 ( SF3B1 , P= 5.6E-8 in HGI-B2) and rs11766643 ( SLC22A5 , P =7.5E-8 in HGI-C2), all SNPs passed the conventional genome-wide significance threshold ( P <5E-8) in at least one HGI COVID-19 GWAS. This underscores the robustness of the identified associations. These 42 independent variants, derived by leveraging acute-phase COVID-19 GWAS data, highlight the potential genetic contributors to long COVID while addressing the challenges of cohort size limitations and phenotype heterogeneity in long COVID studies. To reveal biological insights from SNP-gene associations, each severe COVID-19-associated SNP was linked with its corresponding nearest gene to create a SNP-gene pair, providing an intuitive framework for assessing their biological relevance in long COVID. However, proximity-based assignments may overlook distal regulatory effects, which will be further addressed in subsequent analyses. Notable SNPs in the severe COVID-19-specific category include rs10774679 ( OAS3 ), rs17169628 ( SLC22A31 ), rs12329760 ( TMPRSS2 ), rs12660421 ( FOXP4 ), rs17078348 ( SLC6A20 ), rs190509934 ( ACE2 ), rs2834164 ( IFNAR2 ), rs3785884 ( MAPT ), rs7251000 ( DPP9 ), and rs76608815 ( SLC5A3 ), among others. These SNPs show stronger associations with severe COVID-19 than with mild cases (n=27). In the common SNP category, five out 15 SNP-gene pairs, including rs2290859 ( NXPE3 ), rs505922 ( ABO ), rs149533170 ( IFNA21 ), rs34536443 ( TYK2 ), and rs9916158 ( GSDMA/B ), demonstrates consistent associations across GWAS datasets without differential effects between hospitalized and non-hospitalized groups. Notably, ABO has been reported as a genetic locus associated with long COVID 3 . Several immune-related genes were found close to these SNPs, including ABO (rs505922), IFNA21 (rs149533170), TYK2 (rs34536443), GSDMA/B (rs9916158), BCL11A (rs1123573), ELF5 (rs2924480), and JAK1 (rs11208552), highlighting the role of immune dysregulation in long COVID. Additionally, FUT2 is found close to rs492602, and the protein of FUT2 encodes fucosyltransferase 2, a key enzyme in the ABO antigen synthesis pathway, further underscores the involvement of ABO biology in COVID-19 outcomes 10 . Additionally, four genes— TAC4 , NXPE3 , NSF , and FOXG1 —linked to neuronal functions were close to rs75432325, rs2290859, rs1378358, and rs150497803, respectively, while three genes— ZKSCAN1 (rs2897075), BCL11A (rs1123573), and FOXG1 (rs150497803)—identified as transcription factors. Evaluation of Odds Ratios for 42 Prioritized Long COVID Risk SNPs To characterize the 42 candidate long COVID risk SNPs, we evaluated their odds ratios (ORs) across three GWAS datasets: SARS-CoV-2 susceptibility (HGI-C2), COVID-19 hospitalization (HGI-B1 and HGI-B2; the first used mild COVID-19 and the second used general population as controls). The forest plot (Fig. 1B) illustrates the ORs and 95% confidence intervals (CIs) of these SNPs, stratified by their classification as severe COVID-19-specific SNPs or SNPs associated with both severe and mild COVID-19. This stratification enabled detailed comparisons of effect sizes across the SNP categories and datasets. Distinct differences in OR distributions were observed between the two SNP categories (Fig. 1B). SNPs in the severe COVID-19-specific category exhibited greater deviation in ORs from 1 compared to those associated with both severe and mild COVID-19. To quantify these differences, absolute effect sizes (β) corresponding to each OR were used for statistical comparisons, as β values account for both protective (OR1) effects in a unified framework. As shown in Supplementary Fig. S2, SNPs classified as severe COVID-19-specific displayed a higher frequency of statistical significance ( P <0.05) in absolute effect sizes across GWAS datasets. These SNPs also demonstrated significantly larger absolute effect sizes in comparisons between HGI-B1/HGI-B2 and HGI-C2, underscoring their stronger association with COVID-19 hospitalization. Notably, only in HGI-B1 did the absolute effect sizes differ significantly between the two SNP categories, a result reflecting the selection criteria: severe COVID-19-specific SNPs required P <0.05 in both HGI-B1 and HGI-B2, whereas SNPs associated with both severe and mild COVID-19 needed P <0.05 in HGI-B2 but not HGI-B1. These findings emphasize the expected differences stemming from category-specific filtering criteria while highlighting the relevance of severe COVID-19-specific SNPs to hospitalization outcomes and their potential role in long COVID. To further dissect the associations, SNPs were classified into detailed sub-categories based on their association p-values, as reflected by the color-coded ORs in Fig. 1B for 4 categories, including category 1 (Hosp-specific, P <5E-8): SNPs achieving genome-wide significance specifically for hospitalization, suggesting these loci are risk factors for severe COVID-19 and long COVID, category 2 (Common, P <5E-8): SNPs achieving genome-wide significance for general susceptibility or hospitalization without significant allele frequency differences between hospitalized and non-hospitalized cases, indicating broader applicability, category 3 (Hosp-specific, suggestive/non-significant): SNPs significant only at a suggestive and non-significant associations in hospitalized cases, and category 4 (Common, suggestive/non-significant): SNPs with suggestive or non-significant associations across both case types. Among the 42 SNPs, three—including rs17078348 ( SLC6A20 ), rs12660421 ( FOXP4 ), and rs2834164 ( IFNAR2 )—achieved genome-wide significance ( P <5E-8) across all three GWAS datasets. Thirty-six SNPs met this threshold in HGI-B2 alone. SNPs such as rs17078348 ( SLC6A20 ), rs12660421 ( FOXP4 ), rs1405655 ( NR1H2 ), rs61860402 ( SFTPD ), rs10774679 ( OAS3 ), and rs7251000 ( DPP9 ), with significant ORs confined to hospitalization (Category 1), are particularly relevant to severe COVID-19 outcomes potentially predisposing individuals to long COVID. Conversely, SNPs such as rs505922 ( ABO ), rs492602 ( FUT2 ), rs2290859 ( NXPE3 ), and rs1123573 ( BCL11A ), which exhibit non-specific associations across patient groups (Category 2), may indicate general infection susceptibility factors exacerbating long COVID pathology. Notably, ABO and FUT2 loci have established roles in immune function and host susceptibility 10 . In conclusion, these results provide a comprehensive evaluation of SNPs associated with long COVID, leveraging COVID-19 GWAS data to elucidate their contributions to disease susceptibility and severity while identifying biologically relevant loci for further long COVID investigation. Expanded Screening of Potential Causal Genes Linked to 42 Prioritized Long COVID Risk SNPs To identify potential causal genes associated with 42 prioritized long COVID risk SNPs, we employed local Manhattan plots, centering each candidate SNP within a 500-kb genomic window (Fig. 2 & 3). Given that 41 of these SNPs, except rs12329760 ( TMPRSS2 ), are located in non-coding regions, manual evaluation was required to prioritize genes proximal to each SNP with robust association signals. The first category of long COVID SNPs (n=27) includes variants associated with severe COVID-19 hospitalization but not non-hospitalized cases. Based on the evaluation of association patterns, nine SNPs were prioritized (Fig. 2): rs17078348 ( SLC6A20 ), rs12660421 ( FOXP4 ), rs12329760 ( TMPRSS2 ), rs190509934 ( ACE2 ), rs61860402 ( SFTPD ), rs10774679 ( OAS3 ), rs7251000 ( DPP9 ), rs3785884 ( MAPT ), and rs2834164 ( IFNAR2 ). Strong association signals ( P <1E-30) were observed for rs17078348 ( SLCA20 ), with the locus encompassing several genes, including LZTFL1 , CCR9 , FYCO1 , CCR1 and XCR1 , previously implicated in COVID-19 severity and susceptibility. Similarly, rs12660421 ( FOXP4 ) showed significant association signals near FOXP4 , a known long COVID risk gene, with the SNP residing within its promoter region and relatively distant from other nearby genes. For rs12329760 ( TMPRSS2 ), a missense variant, nominally significant associations with COVID-19 hospitalization (HGI-B2) and SARS-CoV-2 susceptibility (HGI-C2) were detected. Notably, nearby SNPs exhibited weaker signals ( P <0.05) in HGI-B1 and differential effect sizes between HGI-B1 and HGI-B2, suggesting a complex genetic architecture. Several antiviral genes, including MX1 and MX2 , were identified near this locus. Other prioritized loci included rs61860402 ( SFTPD ) and rs7251000 ( DPP9 ), genes relevant to lung function, and rs10774679 ( OAS3 ) and rs2834164 ( IFNAR2 ), which are located within interferon-related gene clusters. While rs3785884 ( MAPT ) was initially implicated, its association may be driven by CRHR1 , a gene associated with age-related neurodegenerative processes 11 , aligning with the increased risk of severe COVID-19 and long COVID in older populations. The second category of SNPs (n=15), associated with both severe and mild COVID-19 cases, yielded nine prioritized loci (Fig. 3): rs149533170 ( IFNA21 ), rs34536443 ( TYK2 ), rs75432325 ( TAC4 ), rs505922 ( ABO ), rs1120591 ( RAB2A ), rs3897075 ( ZKSCAN1 ), rs9916158 ( GSDMA/B ), rs1378358 ( NSF ), and rs2924480 ( ELF5 ). Among these, rs505922 ( ABO ) and rs34536443 ( TYK2 ) displayed genome-wide significant associations with both hospitalized and non-hospitalized COVID-19, as evidenced by consistent signals in HGI-B2 and HGI-C2 datasets. The remaining SNPs were located in gene-dense regions, complicating the identification of specific causal genes. For instance, rs149533170 ( IFNA21 ) is located near a cluster of interferon genes, including IFNB1 and multiple IFNAs , which are central to interferon-mediated antiviral responses 12 . Similarly, rs9916158 ( GSDMA/B ) is proximal to genes implicated in asthma, while rs75432325 ( TAC4 ) resides near DLX3 and DLX4 , which are involved in developmental pathways 13 . Less complex loci, such as rs1120591 ( RAB2A ) and rs2924480 ( ELF5 ), contained fewer neighboring genes, facilitating clearer prioritization. RAB2A is an insulin regulator 14 , while ELF5 encodes a transcription factor relevant to epithelial differentiation and innate immune response 15 . In total, 18 SNPs were prioritized based on their local Manhattan plots, representing two categories of long COVID risk loci. These SNPs are associated with biologically plausible candidate genes involved in pathways related to COVID-19 severity, immune response, and tissue-specific functions. Further experimental validation is required to determine their roles in long COVID. Sex-Biased Associations with COVID-19 Hospitalization among Top Candidate SNPs Epidemiological evidence indicates that women are more likely to experience long COVID but less likely to develop severe COVID-19 16 , 17 , potentially due to stronger immune responses to SARS-CoV-2 infection in women. To investigate whether the top 42 candidate SNPs associated with long COVID exhibit sex-biased associations with COVID-19 hospitalization, we analyzed publicly available sex-stratified GWAS summary statistics from the GRASP COVID database 18 generated based on UK Biobank samples. We selected the largest sex-stratified COVID-19 hospitalization GWAS (cases=1,343 and controls=262,886 for females; cases=1,917 and controls=221,174 for males), which includes mixed ancestry populations comparable to the four HGI COVID-19 GWAS datasets used in our study. Using the delta-z-score method to compare male and female GWAS results, we identified SNPs with differential effect sizes for COVID-19 hospitalization and calculated significance levels (Fig. 4A). Genomic inflation factors for both sex-stratified GWASs and the sex-biased differential GWAS were close to 1, indicating no evidence of inflation (Fig. 4B). Compact local Manhattan plots (Fig. 4C) and forest plots (Fig. 4D) were generated for the 42 candidate SNPs. Due to the smaller sample sizes in the sex-stratified GWASs, only 15 SNPs reached nominal significance ( P <0.05) in at least one of the three analyses. Of these, rs17078348 ( SLC6A20 ), a well-established severe COVID-19 risk SNP, showed nominal significance in both male and female cohorts. Five SNPs displayed nominal significance exclusively in females, including rs75432325 ( TAC4 ), rs505922 ( ABO ), rs12660421 ( FOXP4 ), rs7251000 ( DPP9 ), and rs11766643 ( TFR2 ). Eight SNPs were nominally significant only in males, including rs897075 ( ZKSCAN1 ), rs117169628 ( SLC22A31 ), rs7515509 ( AK5 ), rs76608815 ( SLC5A3 ), rs12585036 ( ATP11A ), rs5023077 ( FBRSL1 ), rs10774679 ( IFNAR21 ), and rs12329760 ( HLA-C ). None of the SNPs passed the stringent multiple testing correction threshold ( P <6E-4), yet local Manhattan plots for 12 of the 42 SNPs revealed relatively weaker association signals in the sex-stratified GWAS compared to the stronger signals observed in HGI-B2 (Fig. 5). This reduction in signal strength likely reflects the smaller sample sizes in the sex-stratified datasets, which reduced statistical power to detect sex-biased associations. Finally, aggregating the absolute effect sizes of SNPs across the four groups (female-SNP-category 1, female-SNP-category 2, male-SNP-category 1, and male-SNP-category 2) based on the nominal association P <0.05 revealed no significant differences (ANOVA P =0.15). In summary, while the sex-biased GWAS analysis did not identify SNPs with statistically significant sex-biased associations with COVID-19 hospitalization, it highlighted 15 SNPs with nominal significance, including rs505922 ( ABO ) and rs12660421 ( FOXP4 ), which have been previously implicated as risk variants for both severe COVID-19 and long COVID. These findings warrant further investigation in larger sex-stratified COVID-19 and long COVID GWAS. Evaluation of Previously Reported Long COVID-Associated SNPs To assess whether the 42 prioritized long COVID SNPs identified in our study overlap with or are proximal to those reported in two independent long COVID GWASs by Taylor et al. 2 and Chaudhary et al. 3 , a set of 83 SNPs derived from SNP network analyses and 3 SNPs identified via traditional GWAS methods from these studies was created. These SNPs were analyzed for their association signals in three HGI GWAS datasets (supplementary Table S1). Notably, rs12660421 ( FOXP4 ), identified by the HGI long COVID GWAS, was excluded from this evaluation as it displayed stronger associations with general COVID-19 phenotypes in the HGI datasets analyzed here. This SNP was already included among the 42 prioritized long COVID risk SNPs. Our evaluation was guided by the hypothesis that true long COVID associations reported by Taylor et al. 2 and Chaudhary et al. 3 . would exhibit at least nominal significance in one of the HGI GWAS datasets. The larger sample sizes and higher statistical power of the HGI datasets enhance their ability to detect associations with traits closely related to long COVID, such as severe COVID-19 or susceptibility to SARS-CoV-2 infection. Moreover, overlap between our prioritized SNPs and those reported in previous studies would strengthen the hypothesis that the loci in question are implicated in long COVID pathogenesis. Among the three SNPs reported by Chaudhary et al. 3 (Fig. 6), rs643434 ( ABO ) and rs2080090 ( BPTF ) demonstrated nominally significant associations ( P <0.05) with COVID-19 hospitalization (HGI-B2) and susceptibility to COVID-19 (HGI-C2). rs643434 ( ABO ) surpassed genome-wide significance thresholds in both HGI-B2 ( P = 6.65E-24) and HGI-C2 ( P =8.69E-85) and showed non-significant association in HGI-B2 ( P =0.6) but demonstrated significantly differential effect sizes between HGI-B1 (hospitalized vs. non-hospitalized COVID-19) and HGI-B2 ( P =6E-5). This supported its classification as a "category 2" long COVID SNP (associated with both severe and mild COVID-19 phenotypes). Similarly, rs2080090 ( BPTF ) showed stronger associations in HGI-B2 ( P = 1.9E-4) than in HGI-C2 ( P =2.5E-2), with no significant association in HGI-B1. This SNP was also categorized as a suggestive "category 2" long COVID SNP. In contrast, rs9273363 ( HLA-DQB1 ) did not exhibit significant associations across any of the HGI GWAS datasets analyzed ( P >0.2). Nevertheless, among these 42 candidate SNPs, rs17219281 ( HLA-DQA1 ), a “category 1” long COVID SNP that tends to be more associated with hospitalized rather than mild COVID-19 (see Table 1), is ~50kb downstream of rs9273363 ( HLA-DQB1 ), strongly implicating that rs17219281 may be the real SNP associated with long COVID. Taken together, the three recently published long COVID loci, including ABO , BPTF , and HLA-DQB1 , were completely covered by these 42 prioritized long COVID SNPs. Meanwhile, of the 80 SNPs reported by Taylor et al. 2 , 12 displayed nominally significant associations in at least one HGI GWAS dataset (Supplementary Table S2 and Fig. 6). These included four SNPs associated with HGI-B2, five with HGI-B1, and seven with HGI-C2. Notably, rs4303401 ( FRMD6 ) surpassed the multiple-testing correction threshold ( P <1.5E-4), representing a strong candidate SNP. Expanding the analysis to include a ±500kb genomic window around the reported SNPs identified additional suggestive genome-wide associations in the HGI datasets. For example, in Fig. 6, rs62401842 (proximal to KCTD16 , which shows brain-specific expression) and rs7671107 and rs1017716 (adjacent to ANAPC4 , a gene implicated in aging and strongly supported as eQTLs accordingly to GTEx eQTL database) emerged as promising candidates. Other noteworthy SNPs in Fig. 6 included rs12602210 ( BPTF ) and rs17412601 and rs2290859 (both mapped to NXPE3 ), which demonstrated suggestive or genome-wide significance in at least one HGI GWAS dataset. In summary, four loci, including KCTD16 , ANAPC4 , BPTF , and NXPE3 , revealed by Taylor et al 2 . derived from SNP network analysis are independently confirmed to be nominally associated with COVID-19 related phenotypes in the current study; among them BPTF is a loci confirmed to be associated with long COVID by Chaudhary et al. 3 , Taylor et al., and current study. Therefore, these findings underscore the potential of integrating data from diverse GWAS to elucidate the genetic architecture of long COVID and highlight candidate loci for further investigation. Potential Long COVID SNPs Specifically Identified in Non-Hospitalized COVID-19 Cases To identify potential long COVID SNPs specific to non-hospitalized COVID-19 cases, we screened SNPs that showed suggestive genome-wide association signals in HGI-B1 (hospitalized vs. non-hospitalized, P < 1E-5) and HGI-B1-vs-B2 (differential GWAS between HGI-B1 and HGI-B2, P 0.05) and were not in proximity to any genome-wide significant SNPs in HGI-B2. This selection identified 20 independent SNPs potentially associated with long COVID that specifically emerged from mild (non-hospitalized) COVID-19 cases (Table 2; local Manhattan plots accessible at GitHub link provided in the Method section; Fig. 7). All 20 SNPs showed suggestive genome-wide significant differences in effect sizes between HGI-B1 and HGI-B2 ( P <6E-5). Although none met the genome-wide significance threshold ( P <5E-8), notable findings were observed. For example, in Fig. 7, rs62401842 ( KCTD16 ) exhibited the strongest association in HGI-B1 ( P =8.53E-7), while showing no association in HGI-B2 ( P =0.38) and a significant difference between HGI-B1 and HGI-B2 ( P =3.18E-7). The gene KCTD16 , encoding potassium channel tetramerization domain-containing 16 19 , is highly expressed in brain tissues according to the GTEx database. In addition, eight SNPs illustrated in Fig. 7 were mapped to genes with brain-related functions, including rs9799354 ( NLGN1 ), rs112842080 ( CPLX2 ), rs61858037 ( NRG3 ), rs61939166 ( KIF21A ), rs367777 ( NAV3 ), rs56143829 ( WASF3 ), rs11454577 ( AKAP6 ), and rs6049828 ( SYNDIG1 ). Among these, WASF3 has been implicated in mitochondrial dysfunction and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome 20 . Additionally, among the 20 candidate SNPs identified, only three (rs62401842 [ KCTD16 ], rs9799354 [ NLGN1 ], and rs112842080 [ CPLX2 ]) exhibit male-biased associations with COVID-19 hospitalization at nominal significance levels (Fig. 8A, B). Furthermore, six candidate SNPs, including rs62401842 ( KCTD16 ), rs9799354 ( NLGN1 ), rs112842080 ( CPLX2 ), rs56143829 ( WASF3 ), rs4737438 ( PENK ), and rs6049828 ( SYNDIG1 ), demonstrate sex-biased association patterns within a genomic window ranging from 500kb to 1000kb (Fig. 8C-H). However, the limited sample sizes in sex-stratified COVID-19 GWAS preclude any candidate SNPs from achieving statistical significance after correction for multiple testing in analyses of sex-biased associations with COVID-19 hospitalization. In summary, while no genome-wide significant SNPs specific to non-hospitalized COVID-19 cases were identified, these 20 SNPs provide suggestive evidence of association with long COVID in mild COVID-19. Transcriptome-Wide Association Study Identifies Candidate Genes near Prioritized SNPs for COVID-19 and Long COVID Phenotypes To elucidate genetic mechanisms underlying COVID-19 and long COVID phenotypes, we performed a transcriptome-wide association study (TWAS) using S-MetaXcan across 48 GTEx tissues. This approach utilized COVID-19 GWAS summary statistics from HGI: HGI-B1, HGI-B2, HGI-C2, and HGI-B1-vs-B2. The analysis targeted 62 candidate SNPs previously prioritized for their potential association with long COVID and aimed to identify their adjacent genes whose expression levels are associated with COVID-19 hospitalization or susceptibility phenotypes, as well as long COVID. Of the 62 candidate SNPs, two SNPs (rs190509934 near ACE2 and rs9570861 near OR7E156P ) not included in the TWAS analysis. The former resides on chromosome X, which S-MetaXcan excludes due to its focus on autosomal genes, while the latter SNP had no protein-coding genes within a 500 kb window. The remaining 60 SNPs corresponded to 753 adjacent genes (Fig. S5) with detectable expression across the 48 GTEx tissues, which were analyzed in 192 (4 × 48) TWASs. To simplify interpretation, we retained only the strongest association (smallest p-value) for each gene across 48 GTEx tissues. This approach was justified by the hypothesis that specific tissues, rather than all tissues, are likely to drive the pathogenicity of COVID-19. Applying varying significance thresholds ( P <0.05, 0.01, 0.001, 0.0001, 0.00001), the analysis revealed 422, 152, 43, 19, and 11 adjacent genes meeting these criteria in at least one of the four HGI datasets (Fig. S3, Table S2). Using a genome-wide significance threshold ( P <3.5E-7), three loci displayed significant associations: (1) DPP9 , showing the strongest association signal ( P =4.6E-18) in adipose visceral omentum tissue; (2) CXCR6 gene cluster, which included multiple genes ( CXCR6 , CCR3 , CCR1 , and LZTFL1 ), displaying the most significant association ( P =1.3E-10) in nerve tibial tissue; (3) MUC1 gene cluster, comprising MUC1 and FAM189B , highlighted in multiple GTEx tissues, including lung and colon. Additional loci with suggestive TWAS significance ( P <3.5E-6) included FYCO1 , SLC6A20 , XCR1 , and THBS3 , with specific associations to HGI datasets B1, B2, or C2 in distinct GTEx tissues. To investigate overlap with long COVID, TWAS was extended to four long COVID GWASs using the same S-MetaXcan framework. Genome-wide evaluation of TWAS signals across the eight HGI GWAS datasets (Fig. S4) confirmed that the top hits with association P <1E-5 originated exclusively from COVID-19 hospitalization and susceptibility GWAS, with no contributions from long COVID GWAS (Fig. S4). Notably, the candidate genes proximal to these prioritized SNPs accounted for most of the top signals across the eight HGI GWAS (Fig. 9), with the exception of CCR5 , EXOSC7 , FLT1P1 , RLN1 , GBAP1 , ARHGEF2 , ZSCAN31 , ZKSCAN3 , PGMSP2 , PAQR5 , and TMIE . Further in-depth evaluation of these outlier genes in long COVID GWAS revealed most of them lack of significance (Fig. S5), reinforcing the utility of prioritizing top candidate SNPs and their adjacent genes for downstream analyses. Interestingly, although the strongest associations identified in COVID-19 hospitalization or susceptibility GWASs ( P <1E-5) were not genome-wide significant in long COVID datasets, four genes ( CCR1 , DPP9 , MUC1 , and THBS3 ; Fig. S5) showed nominally significant associations ( P <0.05) in long COVID GWASs across specific tissues, such as colon transverse, testis, and heart atrial appendage. These associations were generally weaker than those observed for acute COVID-19 phenotypes, suggesting tissue-specific contributions to long COVID pathophysiology. In details, DPP9 demonstrated strong associations in adipose-related tissues for COVID-19 hospitalization but weaker signals in colon transverse for long COVID. THBS3 showed widespread associations across 19 GTEx tissues for COVID-19 hospitalization but exhibited weaker signals in long COVID, particularly in colon sigmoid and spleen. MUC1 and CCR1 exhibited similar patterns, with weaker long COVID associations in colon transverse and heart atrial appendage, respectively. Furthermore, using a relaxed threshold ( P <0.05) identified additional adjacent genes associated with long COVID (Fig. 10 & Fig. S6-7). These included OAS3 (rs10774679), TMPRSS2 (rs12329760), HLA-A (rs4s9260038), HLA-C (rs1634761), HLA-DQA1 (rs17219281), WASF3 (rs65143829), IFNAR2 (rs2834164), SFTPD (rs61860402), and GSDMA/B (rs9916158), with associations enriched in tissues such as heart, brain, colon, adipose, and muscle. Notably, the well-established ABO locus, a COVID-19 susceptibility locus, did not show significant TWAS signals. This suggests that its effects may stem from non-expression-related mechanisms, such as genetic mutations influencing blood types. In summary, TWAS analyses identified 52 SNP-gene pairs nominally associated with long COVID across tissues. While genes such as DPP9, THBS3 , CCR1 , and MUC1 emerged as potential shared contributors to acute and long COVID phenotypes, their tissue-specific associations suggest distinct mechanisms driving long COVID. These findings highlight the importance of integrative approaches in uncovering the molecular underpinnings of long COVID. Phenome-Wide Analysis of Gene Expression Associations with Candidate Genes To investigate gene expression associations with long COVID-related phenotypes, we performed a phenome-wide analysis of gene expression associations (Pheno-TWAS) analysis targeting 918 genes located within a 1Mb window of 62 candidate SNPs previously linked to COVID-19. Recognizing the limitations of published long COVID GWASs, which often rely on imprecisely defined phenotypes, this analysis extended beyond prior long COVID studies by utilizing a manually curated TWAS database encompassing 418 phenotypes across 48 GTEx tissues. Of the 918 tested genes, 400 (43.6%), including 87 associated with HLA loci (e.g., HLA-DQA2 , HLA-A , and HLA-C ; Fig. S8-10), passed a stringent bonferroni-adjusted significance threshold ( P <2.7E-9). Genes located near HLA loci emerged as top hits, demonstrating significant associations with immune-related disorders such as asthma, rheumatoid arthritis, psoriasis, coeliac disease, and systemic lupus erythematosus—conditions that may contribute to long COVID susceptibility. In addition to HLA -associated loci, 313 genes across 44 non- HLA loci surpassed the genome-wide significance threshold in TWASs. Key findings include: (1) IFNAR2 : strongly associated with COVID-19 hospitalization ( P =2.6E-10; Fig. S11); (2) GSDMB : linked to asthma ( P =2.7E-57; Fig. S12); (3) BPTF : associated with trunk fat percentage ( P =1E-18; Fig. S13); (4) CCDC171 : associated with body fat percentage ( P =2E-35; Fig. S14). Interestingly, FOXP4 expression, which did not exhibit significant associations with long COVID phenotypes in prior TWASs, demonstrated a suggestive association with sleep duration ( P =1.1E-5; Fig. S15). This finding highlights the potential for phenome-wide approaches to uncover previously uncharacterized associations relevant to long COVID. In total, pheno-TWAS identified 45 candidate SNPs associated with genome-wide significant expression-linked phenotypes. These include three major HLA loci ( HLA-DQA2 , HLA-A , and HLA-C ) as well as non- HLA loci such as IFNAR2 , BPTF , CCDC171 , and GSDMB . The implicated phenotypes—ranging from COVID-19 hospitalization and immune disorders to body composition traits and sleep duration—provide a broad spectrum of potential biological pathways contributing to long COVID pathophysiology. Full results, including nominally significant associations, are provided in Supplementary Table S3. Discussion Our integrative analysis identifies 62 candidate loci potentially associated with long COVID, leveraging large-scale COVID-19 GWAS datasets from the HGI. This approach addresses the inherent limitations of long COVID-specific GWAS, such as limited sample sizes and phenotype heterogeneity, by utilizing proxy phenotypes like SARS-CoV-2 susceptibility and COVID-19 hospitalization. By categorizing genetic variants into three groups—those associated with hospitalized COVID-19, those linked to both hospitalized and non-hospitalized cases, and those specific to non-hospitalized cases—our findings provide a scalable framework to investigate the genetic underpinnings of long COVID. The analysis highlights 42 genome-wide or suggestive loci, including well-documented variants such as rs12660421 ( FOXP4 ) and rs505922 ( ABO ), which have been implicated in severe COVID-19 outcomes and long COVID susceptibility. Notably, additional loci like rs17219281 ( HLA-DQA1 ), rs9260038 ( HLA-A ), rs1634761 ( HLA-C ), rs7251000 ( DPP9 ), rs9916158 ( GSDMB ), rs12602210 ( BPTF ), rs17078348 ( CCR1 ), and rs41264915 ( THBS3 ), rs190509934 ( ACE2 ) and rs12329760 ( TMPRSS2 ) were identified. Furthermore, a unique category comprising 20 SNPs emerged specifically in non-hospitalized COVID-19 cases. These include rs62401842 ( KCTD16 ) and rs56143829 ( WASF3 ), mapped to genes enriched in brain-related functions, underscoring the complexity of long COVID’s etiology. While this approach provides critical insights, certain limitations warrant consideration. First, the reliance on proxy phenotypes may obscure SNPs specific to mild COVID-19 cases, given the skewed representation of hospitalized patients in HGI datasets. Indeed, non-hospitalized-specific SNPs did not reach genome-wide significance, likely reflecting this bias. Nevertheless, 20 suggestive SNPs, enriched near genes implicated in neurocognitive and age-related pathways, merit further validation in independent cohorts. Second, the TWAS conducted using S-MetaXcan are constrained by the limited tissue specificity of available GTEx datasets and COVID related GWASs. Despite identifying 7 loci— HLA-DQA1 , HLA-A , HLA-C , DPP9 , CCR1 , THBS3 , GSDMB —that emerged as promising TWAS hits for long COVID, the potential absence of COVID-19-specific tissues, such as nasopharyngeal or immune-relevant sites, may hinder the detection of tissue-specific effects pertinent to long COVID's heterogeneity. Our findings underscore the utility of integrative approaches to prioritize genetic risk loci for complex traits like long COVID. The discovery of loci such as FOXP 4, ABO , and HLA-DQA1 , as well as BPTF , suggests shared biological pathways between acute COVID-19 severity and its chronic manifestations. Additionally, the identification of brain-enriched loci in non-hospitalized cases aligns with emerging evidence linking long COVID to neuroinflammatory and neurodegenerative processes. Future studies, particularly those incorporating diverse populations and long-term follow-up data, are essential to refine these associations and unravel the intricate mechanisms underlying long COVID. Declarations Acknowledgements We are thankful to all researchers who participated and contributed to the HGI and GRASP project, as without their generosities in freely sharing the two COVID-19 hospitalization GWASs and other COVID-19 related GWASs as these were essential for the current finding. Potential Conflict of Interest All authors have no biomedical financial interests or potential conflicts of interest. References Lammi, V. et al. Genome-wide Association Study of Long COVID. medRxiv , 2023.2006.2029.23292056, doi:10.1101/2023.06.29.23292056 (2023). Taylor, K. et al. 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MAP3K19 regulatory variation in populations with African ancestry may increase COVID-19 severity. iScience 26 , 107555, doi:10.1016/j.isci.2023.107555 (2023). Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat Commun 9 , 1825, doi:10.1038/s41467-018-03621-1 (2018). GTEx-Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369 , 1318-1330, doi:10.1126/science.aaz1776 (2020). Severe-COVID-GWAS-Group et al. Genomewide Association Study of Severe Covid-19 with Respiratory Failure. N Engl J Med 383 , 1522-1534, doi:10.1056/NEJMoa2020283 (2020). Galeev, A. et al. The role of the blood group-related glycosyltransferases FUT2 and B4GALNT2 in susceptibility to infectious disease. International journal of medical microbiology : IJMM 311 , 151487, doi:10.1016/j.ijmm.2021.151487 (2021). Polanczyk, G. et al. 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WASF3 disrupts mitochondrial respiration and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome. Proc Natl Acad Sci U S A 120 , e2302738120, doi:10.1073/pnas.2302738120 (2023). Tables Table 1. Prioritized long COVID SNPs based on integrative analysis of published COVID-19 GWASs, including HGI-C2 (SASR-CoV2 susceptibility), HGI-B1 (hospitalized vs. non-hospitalized COVID-19), and HGI-B2 (hospitalized vs. general population). Note: HGI: the COVID-19 host genetics initiative; chr: chromosome; pos: hg38 genomic position; allele: alternative allele; AF: allele frequency for alternative allele; diffPval: statistical significance P for differential effect size of each common SNP by comparing the two HGI GWASs, including HGI-B1 and HGI-B2; Hosp: long COVID SNPs tend to be specifically emerged in hospitalized COVID-19 cases; Both: long COVID SNPs tend to be emerged in both hospitalized and non-hospitalized COVID-19 cases. Association Ps passed the suggestive genome-wide association significance threshold of P < 1E-7 are highlighted in light red color, and differential effect size significance P < 0.05 is colored with light green. Table 2. Potential long COVID risk SNPs emerged specifically from non-hospitalized COVID-19 cases Note: abbreviations in the table headers are similar to that in Table 1. Supplementary Tables Supplemental tables are not available with this version. Additional Declarations There is NO Competing Interest. Supplementary Files FigureS1S15.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5807438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":405146350,"identity":"4ef5bb8d-79e4-4b6a-b42b-49da1b0dde6b","order_by":0,"name":"Zhongshan Cheng","email":"data:image/png;base64,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","orcid":"","institution":"St. Jude Children's Research Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhongshan","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-01-11 05:45:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5807438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5807438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79748011,"identity":"b2ff0113-1ffd-45af-a3e5-5fac3a432f36","added_by":"auto","created_at":"2025-04-02 09:04:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1274319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow to prioritize genetic single nucleotide polymorphisms (SNPs) potentially associated with long COVID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) 42 top prioritized long COVID risk SNPs, with 27 SNPs associated specifically with hospitalization risk and 15 showing common risk patterns across both hospitalized and non-hospitalized cases. These SNPs were selected based on a significance threshold of \u003cem\u003eP\u003c/em\u003e ≤ 1 × 10⁻⁷ in at least one of the GWAS studies (HGI-C2, HGI-B2, or HGI-B1). Each SNP is centered within a 1Mb window in local Manhattan plots to highlight the genomic context, including known COVID-19 and long COVID risk loci. Key genes near these SNPs, including \u003cem\u003eFOXP4\u003c/em\u003e—a locus previously implicated in long COVID risk—and other notable COVID-19-associated genes (\u003cem\u003eACE2\u003c/em\u003e, \u003cem\u003eOAS3\u003c/em\u003e, \u003cem\u003eJAK1\u003c/em\u003e, \u003cem\u003eIFNA2\u003c/em\u003e, \u003cem\u003eDDP9\u003c/em\u003e, and \u003cem\u003eABO\u003c/em\u003e), are annotated to emphasize their potential relevance to SARS-CoV-2 pathology and long COVID risk. (B) Forest plot of 42 SNPs identified from COVID-19 GWAS on susceptibility (HGI-C2) and hospitalization (HGI-B1 and HGI-B2), showing suggestive (\u003cem\u003eP\u003c/em\u003e \u0026lt; 1 × 10⁻⁷) or genome-wide significant associations (\u003cem\u003eP\u003c/em\u003e \u0026lt; 5 × 10⁻⁸). Allele distributions are compared between hospitalized and non-hospitalized cases (HGI-B1). SNPs are grouped as follows: (1) hospitalization-specific, genome-wide significant (Hosp-specific, \u003cem\u003eP\u003c/em\u003e \u0026lt; 5 × 10⁻⁸); (2) common across cases, genome-wide significant (Common, \u003cem\u003eP\u003c/em\u003e\u0026lt; 5 × 10⁻⁸); (3) hospitalization-specific, suggestive or non-significant (hosp-specific, \u003cem\u003eP\u003c/em\u003e = (5 × 10⁻⁸, 1]); (4) common across cases, suggestive or non-significant (Common, \u003cem\u003eP\u003c/em\u003e = (5 × 10⁻⁸, 1]). Colored dots represent odds ratios (ORs) with 95% confidence intervals (CIs) as error bars. ORs with CIs overlapping the vertical line (OR = 1) are non-significant.\u003c/p\u003e","description":"","filename":"Picture1.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/2064743040a9d60eba0201b3.png"},{"id":79748012,"identity":"7657e93d-9c93-480f-ba21-8a6480d5e625","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":732748,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocal Manhattan plots for potential long COVID risk SNPs likely associated with hospitalized but not non-hospitalized COVID-19\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/7bfb58eee56dbb47beef7bf1.png"},{"id":79749723,"identity":"ce7e6ded-2e6e-4bf7-a7aa-a9c95cbff009","added_by":"auto","created_at":"2025-04-02 09:12:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":816987,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocal Manhattan plots for potential long COVID risk SNPs likely associated with both hospitalized and non-hospitalized COVID-19\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/72c6e68c57905d51ad7bf613.png"},{"id":79748014,"identity":"6fd6c264-4404-4465-80c9-022e2199ef15","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1262374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluating top potential long COVID risk SNPs in sex-biased COVID-19 hospitalization GWAS from UK Biobank\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B)\u003cstrong\u003e \u003c/strong\u003eManhattan plots and QQ plots for sex-stratified COVID-19 hospitalization GWASs, as well as the differential effect size GWAS between females and males. (C)\u003cstrong\u003e \u003c/strong\u003eLocal Manhattan plot for these 42 SNPs separated into two groups, including long COVID SNPs that tend to be specific in hospitalized cases and that more frequent in both hospitalized and non-hospitalized cases. All association signals shown in the figure are within a 100Mb genomic window for each potential long COVID risk SNP residing at the center. Genes close to these 42 SNPs are added into the upper track. (C) Forest plots for these 42 potential long COVID risk SNPs in sex-stratified COVID-19 hospitalization GWASs, as well as the differential effect size GWAS between females and males.\u003cstrong\u003e \u003c/strong\u003eOdd Ratio (OR) and the corresponding 95% confidence intervals (CI) of these SNPs are illustrated, with SNPs showing nominal significant associations among female or male COVID-19 patients are colored in dark red and tagged by ‘*’. Note that there are 8 and 6 SNPs showing sex-biased associations with females and males, respectively, meanwhile, only one SNP rs17078348 mapped to \u003cem\u003eSLC6A20\u003c/em\u003edisplaying common association signals in both sexes. The genomic position of rs190509934 is used to collect all other SNPs around it, as it is not included in the two UKB sex stratified COVID-19 GWASs.\u003c/p\u003e","description":"","filename":"Picture4.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/c442f67bf191703102086374.png"},{"id":79749725,"identity":"e6952521-398c-4f8a-97ce-36789efbeafa","added_by":"auto","created_at":"2025-04-02 09:12:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":794049,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocal Manhattan plots for potential long COVID SNPs displaying suggestively sex-biased association with COVID-19 hospitalization\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture5.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/dfa865fd020348d5198539e1.png"},{"id":79748015,"identity":"200533bd-f5a7-4c69-b456-a393fb011781","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":825721,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluating published long COVID SNPs in the current HGI COVID-19 GWASs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Forest plots for these 15 SNPs in currently studied 3 GWASs (from left to right: HGI-B1, HGI-B2, and HGI-C2), with genes closed to each of them listed on the right-y-axis. Note: beside 3 long COVID risk SNPs reported by Chaudhary et al., other 12 long COVID SNPs were obtained by screening 83 previously reported long COVID SNPs by Taylor et al. (see Table S1) that show nominal significant association in at least one of the 3 current HGI COVID-19 GWASs. (B-G) Local Manhattan plots illustrate published long COVID SNPs nearby these top candidate long COVID SNPs identified in current study with each candidate SNP is located in the center of each genomic window; GWASs labels from bottom to top in each panel are HGI-B1, HGI-B2, HGI-B1-vs-B2, and HGI-C2, respectively.\u003c/p\u003e","description":"","filename":"Picture6.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/c792d4f7f17eb7a91c20fae4.png"},{"id":79749724,"identity":"0a66acc9-95db-4ea5-9764-5cc1a4b1ae7a","added_by":"auto","created_at":"2025-04-02 09:12:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":743746,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocal Manhattan plots for potential long COVID risk SNPs specifically associated with non-hospitalized COVID-19\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture7.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/128b7480d9e76df4ca32e74c.png"},{"id":79748016,"identity":"2272f2d4-9810-4c61-99bc-b439c6ca7b74","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":856404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of potential long COVID risk SNPs associated with non-hospitalized COVID-19 in sex-biased COVID-19 hospitalization GWAS from UK Biobank\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Local Manhattan plots for candidate long COVID-associated SNPs identified exclusively in the non-hospitalized COVID-19 group from sex-stratified COVID-19 hospitalization GWAS (female GWAS shown at the top, male GWAS in the middle, and the differential effect size GWAS between females and males at the bottom). (B) Forest plots displaying the odds ratios (OR) and corresponding 95% confidence intervals (CI) for these SNPs in non-hospitalized COVID-19 patients from the sex-stratified GWAS (female results at the top and male results at the bottom). SNPs with nominally significant associations in either female or male groups are highlighted in dark red and marked with an asterisk (*). Notably, three SNPs exhibit male-biased associations with COVID-19 hospitalization. (C-H) Detailed local Manhattan plots for six prioritized SNPs identified specifically in the non-hospitalized COVID-19 group within the sex-stratified GWAS.\u003c/p\u003e","description":"","filename":"Picture8.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/3b22a8351d3d044544ef8918.png"},{"id":79748019,"identity":"4f4b03d1-949b-4d9a-9bc6-01702d31efda","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":915695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of top genes adjacent to prioritized long COVID risk SNPs (n=62) in transcriptome-wide association studies (TWAS) of COVID-19 susceptibility, hospitalization, and long COVID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bar plot showing the top genes adjacent to prioritized long COVID risk SNPs based on transcriptome-wide association studies (TWAS) for COVID-19 susceptibility and hospitalization. Genes are categorized by the GTEx tissue in which they display the most significant association. Besides rs1078348, other two SNPs have multiple genes with the smallest TWAS \u003cem\u003eP\u003c/em\u003e are highlighted. (B) Heatmap depicting the significance (\u003cem\u003eP\u003c/em\u003e) of gene-tissue associations for prioritized long COVID SNP-adjacent genes across 48 GTEx tissues. Each cell represents the \u003cem\u003eP\u003c/em\u003e for a specific gene in a given tissue, with a color gradient indicating statistical significance: gray (\u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05), yellow (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), and red (\u003cem\u003eP\u003c/em\u003e \u0026lt; 1E-6). Genes are sorted by TWAS and GTEx tissues are clustered to highlight patterns of tissue specificity.\u003c/p\u003e","description":"","filename":"Picture9.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/897a9ad1b449056996c2d7b4.png"},{"id":79748018,"identity":"d50bd315-8ea8-41e9-8c04-d93cd4579fae","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1006495,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop genes adjacent to prioritized long COVID risk SNPs (n=23) in long COVID-19 transcriptome-wide association studies (TWASs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar plots illustrating the best \u003cem\u003eP\u003c/em\u003e for genes adjacent to prioritized long COVID risk SNPs (n=23) in 4 long COVID TWASs across 48 GTEx tissues.\u003c/p\u003e","description":"","filename":"Picture10.png","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/1ec437e0dee95c3576fffdb0.png"},{"id":79751864,"identity":"c6f7ba8e-de32-40aa-b237-3fa4f3de5ce3","added_by":"auto","created_at":"2025-04-02 09:28:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11206272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/63af83d5-3076-40c0-9350-e582d0d9382f.pdf"},{"id":79748020,"identity":"58e64a83-dd74-45d6-ad92-714a0083a1d0","added_by":"auto","created_at":"2025-04-02 09:04:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9085691,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1S15.docx","url":"https://assets-eu.researchsquare.com/files/rs-5807438/v1/8c4de2b631b4edbfa079a41d.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLong COVID remains a critical and complex public health issue, with a diverse array of over 200 reported symptoms affecting multiple organ systems. These symptoms, which include fatigue, cognitive impairment, respiratory issues, and cardiovascular problems, can persist well beyond the acute phase of COVID-19 infection, revealing a unique challenge for researchers to identify genetic risk factors using genome-wide association studies (GWAS), as the wide variation in symptom clusters may arise from different biological mechanisms of long COVID and require extensive sample sizes to detect meaningful associations. To address this complexity, long COVID GWAS requires not only large and diverse patient datasets but also refined approaches to phenotype classification and statistical analyses.\u003c/p\u003e\n\u003cp\u003eAmong the most significant efforts to understand the genetic basis of long COVID is the large GWAS conducted by Lama et al.\u003ca href=\"#_ENREF_1\" title=\"Lammi, 2023 #115\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e, which analyzed approximately 3,500 long COVID cases but not powerful enough to reveal the full genetic landscape due to several key limitations. Primarily, the study drew cases from early phases of the COVID-19 pandemic based on more immediate post-acute symptoms rather than on late-onset long COVID, which can emerge years after initial infection. As a result, potential long COVID cases with delayed symptom onset may be classified as controls, resulting in dilution of statistical power needed to detect true genetic associations. Despite these limitations, Lama et al.\u003ca href=\"#_ENREF_1\" title=\"Lammi, 2023 #115\"\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/a\u003e identified a single gene, \u003cem\u003eFOXP4\u003c/em\u003e, on chromosome 17, reaching genome-wide significance for association with long COVID. \u003cem\u003eFOXP4\u003c/em\u003e variants were found to be strongly associated with both long COVID and severe COVID-19, in line with a shared genetic predisposition that may drive both conditions. However, the stronger association with severe COVID-19 suggests that \u003cem\u003eFOXP4\u003c/em\u003e may contribute primarily to risk factors influencing severe acute COVID-19 cases that later progress to long COVID. Given that long COVID can also follow mild COVID-19, additional genetic contributors associated specifically with mild cases likely remain undiscovered.\u003c/p\u003e\n\u003cp\u003eAnother long COVID GWAS conducted by Taylor et al.\u003ca href=\"#_ENREF_2\" title=\"Taylor, 2023 #116\"\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/a\u003e provided further insights into genetic factors specific to a subtype of long COVID by investigating genetic overlap between long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a condition with substantial symptom overlap, including chronic fatigue and cognitive impairments. They suggested that it is beneficial to analyze long COVID subtypes separately, as different symptom profiles could be associated with distinct genetic and biological pathways. However, their GWAS faced limitations due to its relatively small sample size (n \u0026lt; 1,000 long COVID cases) with restricted statistical power. Additionally, the study only reported SNPs derived by network analysis and did not identify any SNPs that reached genome-wide significance for long COVID, emphasizing the need for larger, more statistically robust studies. The absence of genome-wide significant SNPs underscores a major challenge in long COVID research: even well-defined subtypes require substantial sample sizes to detect genetic variants with confidence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMore recently, a multi-ancestry GWAS was deposited at the preprint server medRxiv by Chaudhary et al.\u003ca href=\"#_ENREF_3\" title=\"Chaudhary, 2024 #117\"\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/a\u003e, which expanded the scope of long COVID GWAS by including participants from diverse ancestral backgrounds. Notably, several immune-related loci, such as \u003cem\u003eABO\u003c/em\u003e, \u003cem\u003eBPTF\u003c/em\u003e, and \u003cem\u003eHLA-DQA1\u003c/em\u003e, were reported to be linked with chronic fatigue syndrome (CFS), fibromyalgia, and depression, suggesting potential immune dysregulation in long COVID. Despite this, this multi-ancestry long COVID GWAS still has limited sample size in non-European samples regarding the heterogeneity of long COVID symptoms, leading to the lack of sufficient power to detect associations for less common symptom clusters or subtle genetic variants. Furthermore, the study relied on self-reported cases, which may introduce misclassification biases, particularly given that long COVID lacks a single diagnostic marker. Self-reported symptoms, though valuable for capturing diverse experiences, can introduce noise that complicates statistical analysis and gene identification.\u003c/p\u003e\n\u003cp\u003eTo advance insights into long COVID genetics, additional large-scale GWAS or alternative analyses that utilize existing COVID-19 GWAS summary statistics are essential to uncover genetic factors masked by current sample size limitations. Integrative approaches leveraging large publicly available GWAS of COVID-19 hospitalization and SARS-CoV-2 susceptibility may provide the statistical power required to detect SNPs linked to long COVID.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we performed an integrative analysis of COVID-19 GWASs from the Host Genetics Initiative (HGI, release 7), assuming these phenotypes of COVID-19 hospitalization and susceptibility considered together to be a proxy phenotype of long COVID. Hypothesizing that long COVID may develop after either mild or severe COVID-19, this study was aimed to identify potential shared or hospitalization-specific genetic elements, as well as non-hospitalization-specific genetic elements. The current analysis prioritized 62 top independent SNPs potentially associated with long COVID into three SNP categories, containing SNPs mapped to two well-known loci involved in SARS-CoV2 entry (\u003cem\u003eACE2\u003c/em\u003e [rs190509934] and \u003cem\u003eTMPRSS2\u003c/em\u003e [rs12329760]): (1) “Severe COVID-19-Specific SNPs”, such as these previously reported long COVID risk SNPs rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), rs10774679 (\u003cem\u003eOAS3\u003c/em\u003e), and rs17219281 (\u003cem\u003eHLA-DQA1\u003c/em\u003e), which show significant associations in both GWASs of \u0026nbsp; hospitalized COVID-19 vs. general population and SARS-CoV-2 infection vs. general population with relatively weaker associations in hospitalized vs. non-hospitalized COVID-19; (2) “SNPs Associated with Both Severe and Mild COVID-19”, including the well-known long COVID risk SNPs, rs505922 (\u003cem\u003eABO\u003c/em\u003e), covering SNPs that may contribute to long COVID risk across a range of COVID-19 severities; (3) an extra SNP category specifically emerged from non-hospitalized COVID-19 patients (“Non-hospitalization-specific long COVID SNPs”) was further explored to prioritize 20 potential long COVID SNPs, such as the reported long COVID SNP rs62401842 (\u003cem\u003eKCTD16\u003c/em\u003e), as well as rs56143829 (\u003cem\u003eWASF3\u003c/em\u003e), with a relative lower association significance threshold. These prioritized SNPs were evaluated in association with long COVID based on published long COVID GWASs, and transcriptome-wide association studies (TWAS) were performed for these HGI COVID-19 hospitalization/susceptibility/long COVID GWASs to determine the expression level association of genes close to these candidate SNPs with these phenotypes, resulting in the conclusion that long COVID is likely to have different tissue etiology, particularly occurring in heart, brain, and muscle related tissues. Further phenome-wide TWASs were conducted to evaluate additional candidate genes potentially involved in long COVID. This integrative approach prioritizes SNPs as well as genes close to them with potential relevance to long COVID, offering a scalable alternative to screen long COVID risk loci by maximizing the statistical power of existing large-sample COVID-19 GWAS data from HGI.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eGenome-wide Screening of Genetic SNPs Potentially Associated with Long COVID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the challenge of identifying genetic associations with long COVID due to its diverse symptomatology and the limited cohort sizes in GWAS, proxy phenotypes such as SARS-CoV-2 susceptibility (also refer to COVID-19 susceptibility in this study) and COVID-19 hospitalization were leveraged to prioritize relevant SNPs. This strategy is supported by evidence that long COVID can develop following both mild and severe COVID-19, suggesting shared or distinct genetic risk factors may influence acute-phase outcomes of COVID-19 (e.g., hospitalization, non-hospitalization, or susceptibility to SARS-CoV2 infection) and subsequent long COVID susceptibility.\u003c/p\u003e\n\u003cp\u003ePublicly available COVID-19 GWAS datasets were downloaded from the HGI (release 7), including HGI-C2 (COVID-19 [n=159,840] vs. the general population [n=2,782,977]), HGI-B1 (hospitalized [n=16,512] vs. non-hospitalized COVID-19 [n=71,321]), and HGI-B2 (hospitalized COVID-19 [n=44,986] vs. the general population [n=2,356,386]), and analyzed using an integrative analysis pipeline to prioritize potential long COVID SNPs. Although HGI also shared the long COVID GWASs with 4 slightly different case-control designs that were published by Lammi et al\u003csup\u003e1\u003c/sup\u003e, these data were not initially utilized to prioritize long COVID SNPs due to the fact that these long COVID GWASs contain potential bias to severe COVID-19, which may lead to the missing of mild COVID-19 related long COVID risk SNPs. Again, the previously published HGI long COVID GWASs only identified one genome-wide significant locus represented by \u003cem\u003eFOXP4\u003c/em\u003e. Therefore, in the current study the SNP prioritization workflow primarily focused on two key long COVID risk SNP categories related to severe COVID-19 that are potentially relevant to long COVID. The first SNP categories is \u0026ldquo;Severe COVID-19-Specific SNPs\u0026rdquo;, which includes SNPs associated with both SARS-CoV-2 susceptibility and COVID-19 hospitalization but tends to be more prevalent within hospitalized than in non-hospitalized cases. These SNPs are hypothesized to influence long COVID risk based on the epidemiological observation that multiple SARS-CoV2 infections and COVID-19 hospitalizations elevate the likelihood of developing the long COVID condition\u003csup\u003e4\u003c/sup\u003e. The following selection criteriawas applied, including (a) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in HGI-C2, capturing SNPs linked to COVID-19 susceptibility; (b) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in HGI-B1, identifying SNPs that predispose individuals to COVID-19 hospitalization rather than mild COVID-19; (c) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in HGI-B2, identifying SNPs distinguishing hospitalized COVID-19 individuals from the general population; (d) \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05 in the differential GWAS of HGI-B1 vs. HGI-B2, excluding SNPs with differential effect sizes between these comparisons; (e) all require to display consistent effect directions across HGI-C2, HGI-B1, and HGI-B2. Meanwhile, the second SNP categories is \u0026ldquo;SNPs Associated with Both Severe and Mild COVID-19\u0026rdquo;, which influence both mild and severe COVID-19 outcomes that are plausible candidates for long COVID risk, given that non-hospitalized cases can also develop long COVID. These SNPs are unlikely to show significant differentiation between hospitalized and non-hospitalized cases but may exhibit associations with SARS-CoV-2 susceptibility and COVID-19 hospitalization when compared to the general population. The selection criteria for this group were performed as (a) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in HGI-C2, identifying COVID-19 susceptibility-related SNPs; (b) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in HGI-B2, identifying SNPs associated with COVID-19 hospitalization; (c) \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05 in HGI-B1, indicating no significant difference in SNP effects between severe and mild COVID-19; (d) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in the differential GWAS of HGI-B1 vs. HGI-B2, capturing SNPs with distinct effects in these datasets; (e) all prioritized SNPs need to be fulfill consistent effect size directions across HGI-C2 and HGI-B2. Finally, SNPs met either of the above two sets of conditions were further evaluated based on the criterion of passing the \u003cem\u003eP\u003c/em\u003e\u0026lt;1E-7 in any of the three GWAS datasets (HGI-B1, HGI-B2, or HGI-C2). This prioritization framework provides a systematic approach to identifying genetic variants potentially predisposing individuals to long COVID by leveraging large-scale COVID-19 GWAS data, resulting in 42 top independent SNPs that were subjected to further evaluation on its potential involvement in long COVID by COVID19_GWAS_Analyzer\u003csup\u003e5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e (https://github.com/chengzhongshan/COVID19_GWAS_Analyzer).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of SNPs Potentially Associated with Long COVID in Non-Hospitalized Cases\u003c/strong\u003e\u003cbr\u003eCurrent COVID-19 GWAS analyses from the HGI are predominantly biased toward hospitalized cases, potentially overlooking SNPs associated with long COVID that emerge specifically in non-hospitalized individuals. To address this gap, SNPs were filtered using the following criteria: (1) \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05 in the GWAS comparing hospitalized cases with the general population (HGI-B2), to exclude SNPs associated with COVID-19 hospitalization; (2) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in the GWAS comparing hospitalized cases with non-hospitalized cases (HGI-B1), to capture SNPs significantly differentiating these two groups; (3) \u003cem\u003eP\u003c/em\u003e\u0026le;0.05 in the differential GWAS between HGI-B1 and HGI-B2, to retain SNPs with differential association signals between these datasets. To further refine the selection, independent SNPs met a suggestive significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;1E-5) were prioritized, ensuring no other genome-wide significant SNPs were located within a 500-kb window in either HGI-B2 or HGI-C2. The use of a relaxed significance threshold aimed to account for the higher heterogeneity among non-hospitalized cases in HGI-B1 and the inherent bias of HGI GWASs toward identifying SNPs associated with hospitalization. This approach enables the identification of SNPs that might represent unique genetic contributors to long COVID risk in non-hospitalized individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparing Two COVID-19 Hospitalization GWASs Using the Differential Z-Score Method\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess differences in effect sizes of SNPs between two COVID-19 GWAS datasets, such as one GWAS comparing hospitalized cases with non-hospitalized cases (HGI-B1) and the other GWAS comparing hospitalized cases with the general population (HGI-B2), as well as comparison SNP effect size between sex-stratified COVID-19 hospitalization GWASs, differential z-score method\u003csup\u003e6\u003c/sup\u003e was applied in current study. This approach quantifies the disparity in effect sizes (\u0026beta;) of each SNP between two GWASs by calculating a differential z-score, which incorporates adjustments for potential sample overlap between studies. The corresponding p-value was derived from the normalized differential z-scores. The differential z-score is defined as:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"409\" height=\"148\"\u003e\u003c/p\u003e\n\u003cp\u003eHere, gwas1.\u0026beta; and gwas2.\u0026beta; represent the effect sizes for a given SNP from the two GWASs, while gwas1.se and gwas2.se denote the corresponding standard errors. To account for genome-wide correlation in effect sizes caused by overlapping samples, differential z-scores were normalized using the SAS procedure \u0026ldquo;proc stdize\u0026rdquo; with the \u0026ldquo;std\u0026rdquo; method. Subsequently, differential p-values were computed based on the normalized differential z-scores. The cumulative distribution function of the standard normal distribution (\u0026ldquo;pnorm\u0026rdquo;) was used to calculate p-values from the normalized scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome-Wide Association Analysis Using S-MetaXcan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranscriptome-wide association analysis (TWAS) was conducted using S-MetaXcan\u003csup\u003e7\u003c/sup\u003e, which imputes gene expression from GWAS summary statistics. Four COVID-19 GWAS datasets from the HGI were analyzed, including HGI-B1, HGI-B2, HGI-C2, and HGI-B1vs-B2, alongside four long COVID GWAS datasets with subtly distinct case-control definitions from HGI\u003csup\u003e1\u003c/sup\u003e and an additional 221 GWASs from the UK Biobank (UKB) (all sample sizes \u0026gt;5,000) shared by Benneal\u0026rsquo;s lab (https://www.nealelab.is/uk-biobank). Reference transcriptomic data from 48 tissues in GTEx v7\u003csup\u003e8\u003c/sup\u003e were utilized for the analyses.\u003c/p\u003e\n\u003cp\u003eS-MetaXcan\u003csup\u003e7\u003c/sup\u003e employs multivariate regression to integrate evidence across different GTEx tissues (n=48), enhancing detection power. Default settings were applied, with the conditional number for eigenvalue set at 30 during singular value decomposition (SVD) truncation and genes on autosomal chromosomes were included in the analysis. First, TWAS results for COVID-19 (n=8, collectively referred to as COVID TWASs) were evaluated for genes proximal to prioritized long COVID-associated SNPs (\u0026plusmn;500kb to the candidate SNP termed as tag SNP). Genes showing nominal significance (P \u0026lt; 0.05) in any of the COVID TWASs across the 48 GTEx tissues were prioritized for further investigation.\u003c/p\u003e\n\u003cp\u003eTo expand these findings, phenome-wide association analyses were conducted for the prioritized genes. Associations were examined across a TWAS database aggregating 193 previously published TWAS results\u003csup\u003e7\u003c/sup\u003e and 229 TWASs generated in this study, yielding a total of 418 TWAS datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis software and availability of codes and data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnless otherwise specified, all analyses were performed using SAS OnDemand for Academics (freely available at https://www.sas.com/en_us/software/on-demand-for-academics.html). The scripts and datasets used in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eLocal Manhattan plots for the top candidate long COVID-associated SNPs and all supplementary tables are publicly accessible at the GitHub link (https://github.com/chengzhongshan/COVID19_GWAS_Analyzer/tree/main/LongCOVID_data_and_scripts/LongCOVID_SNP_LocalManhattanPlots).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePrioritizing Genetic Variants Associated with Long COVID Using COVID-19 GWAS Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLong COVID, characterized by over 200 symptoms spanning multiple organ systems, presents significant challenges for GWAS due to difficulties in assembling large, representative cohorts. To date, only four genome-wide significant loci—\u003cem\u003eFOXP4\u003c/em\u003e, \u003cem\u003eABO\u003c/em\u003e, \u003cem\u003eHLA-DQA1\u003c/em\u003e, and \u003cem\u003eBPTF\u003c/em\u003e—have been identified for long COVID\u003csup\u003e1\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e. To uncover additional genetic long COVID risk loci, we developed an integrative workflow (Fig. 1a) leveraging publicly available COVID-19 GWAS data. Acute COVID-19 phenotypes, such as SARS-CoV-2 susceptibility, COVID-19 hospitalization, and non-hospitalization, were used as proxy traits for the long COVID phenotype to prioritize genetic variants potentially linked to long COVID. The analysis utilized data from the HGI release 7, including GWAS of hospitalized vs. non-hospitalized cases (HGI-B1), hospitalized cases vs. the general population (HGI-B2), and SARS-CoV-2 infection vs. the general population (HGI-C2). These GWASs comprise large sample sizes (16,512, 44,986, and 159,840 COVID-19 cases for HGI-B1, HGI-B2, and HGI-C2, respectively; Fig. 1a) and exhibit minimal genomic inflation (λ close to 1). As shown in Supplementary Fig. S1 and Fig. 1a, loci near \u003cem\u003eSLC6A20\u003c/em\u003e and \u003cem\u003eABO\u003c/em\u003e displayed notable significance in association signals. These regions, previously validated as COVID-19 risk loci in the literature\u003csup\u003e9\u003c/sup\u003e, are well-established contributors to COVID-19 susceptibility and severity. Taken together, the robustness of these three large COVID-19 GWAS datasets, coupled with the strong correlation between long COVID, SARS-CoV-2 susceptibility, and COVID-19 severity, underscores their utility in identifying potential genetic risk variants for long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCategorization of Prioritized Long COVID Risk SNPs \u003c/strong\u003e\u003cstrong\u003eRelated to COVID-19 Severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the genetic basis of long COVID, we hypothesized that large, previously published COVID-19 GWAS datasets could identify SNPs associated with long COVID. Using an integrative workflow (Fig. 1), we identified 42 prioritized SNPs (see Table 1), categorized into two groups: severe COVID-19-specific SNPs (n=27) and SNPs associated with both severe and mild COVID-19 (n=15). The first category includes SNPs linked predominantly to SARS-CoV-2 susceptibility and severe disease outcomes, such as hospitalization, and are more prevalent in hospitalized cases than in non-hospitalized cases. The second category consists of SNPs associated with both severe and mild COVID-19, showing consistent associations with SARS-CoV-2 susceptibility and hospitalization, but no significant differentiation between hospitalized and non-hospitalized cases, i.e., \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05 in HGI-B1, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 in HGI-B2 and HGI-C2.\u003c/p\u003e\n\u003cp\u003eOf the 42 SNPs, except two SNPs, rs150345524 (\u003cem\u003eSF3B1\u003c/em\u003e, \u003cem\u003eP=\u003c/em\u003e5.6E-8 in HGI-B2) and rs11766643 (\u003cem\u003eSLC22A5\u003c/em\u003e, \u003cem\u003eP\u003c/em\u003e=7.5E-8 in HGI-C2), all SNPs passed the conventional genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;5E-8) in at least one HGI COVID-19 GWAS. This underscores the robustness of the identified associations. These 42 independent variants, derived by leveraging acute-phase COVID-19 GWAS data, highlight the potential genetic contributors to long COVID while addressing the challenges of cohort size limitations and phenotype heterogeneity in long COVID studies.\u003c/p\u003e\n\u003cp\u003eTo reveal biological insights from SNP-gene associations, each severe COVID-19-associated SNP was linked with its corresponding nearest gene to create a SNP-gene pair, providing an intuitive framework for assessing their biological relevance in long COVID. However, proximity-based assignments may overlook distal regulatory effects, which will be further addressed in subsequent analyses. Notable SNPs in the severe COVID-19-specific category include rs10774679 (\u003cem\u003eOAS3\u003c/em\u003e), rs17169628 (\u003cem\u003eSLC22A31\u003c/em\u003e), rs12329760 (\u003cem\u003eTMPRSS2\u003c/em\u003e), rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), rs17078348 (\u003cem\u003eSLC6A20\u003c/em\u003e), rs190509934 (\u003cem\u003eACE2\u003c/em\u003e), rs2834164 (\u003cem\u003eIFNAR2\u003c/em\u003e), rs3785884 (\u003cem\u003eMAPT\u003c/em\u003e), rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), and rs76608815 (\u003cem\u003eSLC5A3\u003c/em\u003e), among others. These SNPs show stronger associations with severe COVID-19 than with mild cases (n=27). In the common SNP category, five out 15 SNP-gene pairs, including rs2290859 (\u003cem\u003eNXPE3\u003c/em\u003e), rs505922 (\u003cem\u003eABO\u003c/em\u003e), rs149533170 (\u003cem\u003eIFNA21\u003c/em\u003e), rs34536443 (\u003cem\u003eTYK2\u003c/em\u003e), and rs9916158 (\u003cem\u003eGSDMA/B\u003c/em\u003e), demonstrates consistent associations across GWAS datasets without differential effects between hospitalized and non-hospitalized groups. Notably, ABO has been reported as a genetic locus associated with long COVID\u003csup\u003e3\u003c/sup\u003e. Several immune-related genes were found close to these SNPs, including \u003cem\u003eABO\u003c/em\u003e (rs505922), \u003cem\u003eIFNA21\u003c/em\u003e (rs149533170), \u003cem\u003eTYK2\u003c/em\u003e (rs34536443), \u003cem\u003eGSDMA/B\u003c/em\u003e (rs9916158), \u003cem\u003eBCL11A\u003c/em\u003e (rs1123573), \u003cem\u003eELF5\u003c/em\u003e (rs2924480), and \u003cem\u003eJAK1\u003c/em\u003e (rs11208552), highlighting the role of immune dysregulation in long COVID. Additionally, \u003cem\u003eFUT2\u003c/em\u003e is found close to rs492602, and the protein of FUT2 encodes fucosyltransferase 2, a key enzyme in the ABO antigen synthesis pathway, further underscores the involvement of ABO biology in COVID-19 outcomes\u003csup\u003e10\u003c/sup\u003e. Additionally, four genes—\u003cem\u003eTAC4\u003c/em\u003e, \u003cem\u003eNXPE3\u003c/em\u003e, \u003cem\u003eNSF\u003c/em\u003e, and \u003cem\u003eFOXG1\u003c/em\u003e—linked to neuronal functions were close to rs75432325, rs2290859, rs1378358, and rs150497803, respectively, while three genes—\u003cem\u003eZKSCAN1 \u003c/em\u003e(rs2897075), \u003cem\u003eBCL11A \u003c/em\u003e(rs1123573), and \u003cem\u003eFOXG1 \u003c/em\u003e(rs150497803)—identified as transcription factors. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of Odds Ratios for 42 Prioritized Long COVID Risk SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo characterize the 42 candidate long COVID risk SNPs, we evaluated their odds ratios (ORs) across three GWAS datasets: SARS-CoV-2 susceptibility (HGI-C2), COVID-19 hospitalization (HGI-B1 and HGI-B2; the first used mild COVID-19 and the second used general population as controls). The forest plot (Fig. 1B) illustrates the ORs and 95% confidence intervals (CIs) of these SNPs, stratified by their classification as severe COVID-19-specific SNPs or SNPs associated with both severe and mild COVID-19. This stratification enabled detailed comparisons of effect sizes across the SNP categories and datasets.\u003c/p\u003e\n\u003cp\u003eDistinct differences in OR distributions were observed between the two SNP categories (Fig. 1B). SNPs in the severe COVID-19-specific category exhibited greater deviation in ORs from 1 compared to those associated with both severe and mild COVID-19. To quantify these differences, absolute effect sizes (β) corresponding to each OR were used for statistical comparisons, as β values account for both protective (OR\u0026lt;1) and risk (OR\u0026gt;1) effects in a unified framework. As shown in Supplementary Fig. S2, SNPs classified as severe COVID-19-specific displayed a higher frequency of statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in absolute effect sizes across GWAS datasets. These SNPs also demonstrated significantly larger absolute effect sizes in comparisons between HGI-B1/HGI-B2 and HGI-C2, underscoring their stronger association with COVID-19 hospitalization. Notably, only in HGI-B1 did the absolute effect sizes differ significantly between the two SNP categories, a result reflecting the selection criteria: severe COVID-19-specific SNPs required \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 in both HGI-B1 and HGI-B2, whereas SNPs associated with both severe and mild COVID-19 needed \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 in HGI-B2 but not HGI-B1. These findings emphasize the expected differences stemming from category-specific filtering criteria while highlighting the relevance of severe COVID-19-specific SNPs to hospitalization outcomes and their potential role in long COVID.\u003c/p\u003e\n\u003cp\u003eTo further dissect the associations, SNPs were classified into detailed sub-categories based on their association p-values, as reflected by the color-coded ORs in Fig. 1B for 4 categories, including category 1 (Hosp-specific, \u003cem\u003eP\u003c/em\u003e\u0026lt;5E-8): SNPs achieving genome-wide significance specifically for hospitalization, suggesting these loci are risk factors for severe COVID-19 and long COVID, category 2 (Common, \u003cem\u003eP\u003c/em\u003e\u0026lt;5E-8): SNPs achieving genome-wide significance for general susceptibility or hospitalization without significant allele frequency differences between hospitalized and non-hospitalized cases, indicating broader applicability, category 3 (Hosp-specific, suggestive/non-significant): SNPs significant only at a suggestive and non-significant associations in hospitalized cases, and category 4 (Common, suggestive/non-significant): SNPs with suggestive or non-significant associations across both case types. Among the 42 SNPs, three—including rs17078348 (\u003cem\u003eSLC6A20\u003c/em\u003e), rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), and rs2834164 (\u003cem\u003eIFNAR2\u003c/em\u003e)—achieved genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;5E-8) across all three GWAS datasets. Thirty-six SNPs met this threshold in HGI-B2 alone. SNPs such as rs17078348 (\u003cem\u003eSLC6A20\u003c/em\u003e), rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), rs1405655 (\u003cem\u003eNR1H2\u003c/em\u003e), rs61860402 (\u003cem\u003eSFTPD\u003c/em\u003e), rs10774679 (\u003cem\u003eOAS3\u003c/em\u003e), and rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), with significant ORs confined to hospitalization (Category 1), are particularly relevant to severe COVID-19 outcomes potentially predisposing individuals to long COVID. Conversely, SNPs such as rs505922 (\u003cem\u003eABO\u003c/em\u003e), rs492602 (\u003cem\u003eFUT2\u003c/em\u003e), rs2290859 (\u003cem\u003eNXPE3\u003c/em\u003e), and rs1123573 (\u003cem\u003eBCL11A\u003c/em\u003e), which exhibit non-specific associations across patient groups (Category 2), may indicate general infection susceptibility factors exacerbating long COVID pathology. Notably, \u003cem\u003eABO\u003c/em\u003e and \u003cem\u003eFUT2\u003c/em\u003e loci have established roles in immune function and host susceptibility\u003csup\u003e10\u003c/sup\u003e. In conclusion, these results provide a comprehensive evaluation of SNPs associated with long COVID, leveraging COVID-19 GWAS data to elucidate their contributions to disease susceptibility and severity while identifying biologically relevant loci for further long COVID investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpanded Screening of Potential Causal Genes Linked to 42 Prioritized Long COVID Risk SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential causal genes associated with 42 prioritized long COVID risk SNPs, we employed local Manhattan plots, centering each candidate SNP within a 500-kb genomic window (Fig. 2 \u0026amp; 3). Given that 41 of these SNPs, except rs12329760 (\u003cem\u003eTMPRSS2\u003c/em\u003e), are located in non-coding regions, manual evaluation was required to prioritize genes proximal to each SNP with robust association signals.\u003c/p\u003e\n\u003cp\u003eThe first category of long COVID SNPs (n=27) includes variants associated with severe COVID-19 hospitalization but not non-hospitalized cases. Based on the evaluation of association patterns, nine SNPs were prioritized (Fig. 2): rs17078348 (\u003cem\u003eSLC6A20\u003c/em\u003e), rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), rs12329760 (\u003cem\u003eTMPRSS2\u003c/em\u003e), rs190509934 (\u003cem\u003eACE2\u003c/em\u003e), rs61860402 (\u003cem\u003eSFTPD\u003c/em\u003e), rs10774679 (\u003cem\u003eOAS3\u003c/em\u003e), rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), rs3785884 (\u003cem\u003eMAPT\u003c/em\u003e), and rs2834164 (\u003cem\u003eIFNAR2\u003c/em\u003e). Strong association signals (\u003cem\u003eP\u003c/em\u003e \u0026lt;1E-30) were observed for rs17078348 (\u003cem\u003eSLCA20\u003c/em\u003e), with the locus encompassing several genes, including \u003cem\u003eLZTFL1\u003c/em\u003e, \u003cem\u003eCCR9\u003c/em\u003e, \u003cem\u003eFYCO1\u003c/em\u003e, \u003cem\u003eCCR1\u003c/em\u003e and \u003cem\u003eXCR1\u003c/em\u003e, previously implicated in COVID-19 severity and susceptibility. Similarly, rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e) showed significant association signals near \u003cem\u003eFOXP4\u003c/em\u003e, a known long COVID risk gene, with the SNP residing within its promoter region and relatively distant from other nearby genes. For rs12329760 (\u003cem\u003eTMPRSS2\u003c/em\u003e), a missense variant, nominally significant associations with COVID-19 hospitalization (HGI-B2) and SARS-CoV-2 susceptibility (HGI-C2) were detected. Notably, nearby SNPs exhibited weaker signals (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in HGI-B1 and differential effect sizes between HGI-B1 and HGI-B2, suggesting a complex genetic architecture. Several antiviral genes, including \u003cem\u003eMX1\u003c/em\u003e and \u003cem\u003eMX2\u003c/em\u003e, were identified near this locus. Other prioritized loci included rs61860402 (\u003cem\u003eSFTPD\u003c/em\u003e) and rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), genes relevant to lung function, and rs10774679 (\u003cem\u003eOAS3\u003c/em\u003e) and rs2834164 (\u003cem\u003eIFNAR2\u003c/em\u003e), which are located within interferon-related gene clusters. While rs3785884 (\u003cem\u003eMAPT\u003c/em\u003e) was initially implicated, its association may be driven by \u003cem\u003eCRHR1\u003c/em\u003e, a gene associated with age-related neurodegenerative processes\u003csup\u003e11\u003c/sup\u003e, aligning with the increased risk of severe COVID-19 and long COVID in older populations.\u003c/p\u003e\n\u003cp\u003eThe second category of SNPs (n=15), associated with both severe and mild COVID-19 cases, yielded nine prioritized loci (Fig. 3): rs149533170 (\u003cem\u003eIFNA21\u003c/em\u003e), rs34536443 (\u003cem\u003eTYK2\u003c/em\u003e), rs75432325 (\u003cem\u003eTAC4\u003c/em\u003e), rs505922 (\u003cem\u003eABO\u003c/em\u003e), rs1120591 (\u003cem\u003eRAB2A\u003c/em\u003e), rs3897075 (\u003cem\u003eZKSCAN1\u003c/em\u003e), rs9916158 (\u003cem\u003eGSDMA/B\u003c/em\u003e), rs1378358 (\u003cem\u003eNSF\u003c/em\u003e), and rs2924480 (\u003cem\u003eELF5\u003c/em\u003e). Among these, rs505922 (\u003cem\u003eABO\u003c/em\u003e) and rs34536443 (\u003cem\u003eTYK2\u003c/em\u003e) displayed genome-wide significant associations with both hospitalized and non-hospitalized COVID-19, as evidenced by consistent signals in HGI-B2 and HGI-C2 datasets. The remaining SNPs were located in gene-dense regions, complicating the identification of specific causal genes. For instance, rs149533170 (\u003cem\u003eIFNA21\u003c/em\u003e) is located near a cluster of interferon genes, including \u003cem\u003eIFNB1\u003c/em\u003e and multiple \u003cem\u003eIFNAs\u003c/em\u003e, which are central to interferon-mediated antiviral responses\u003csup\u003e12\u003c/sup\u003e. Similarly, rs9916158 (\u003cem\u003eGSDMA/B\u003c/em\u003e) is proximal to genes implicated in asthma, while rs75432325 (\u003cem\u003eTAC4\u003c/em\u003e) resides near \u003cem\u003eDLX3\u003c/em\u003e and \u003cem\u003eDLX4\u003c/em\u003e, which are involved in developmental pathways\u003csup\u003e13\u003c/sup\u003e. Less complex loci, such as rs1120591 (\u003cem\u003eRAB2A\u003c/em\u003e) and rs2924480 (\u003cem\u003eELF5\u003c/em\u003e), contained fewer neighboring genes, facilitating clearer prioritization. \u003cem\u003eRAB2A\u003c/em\u003e is an insulin regulator\u003csup\u003e14\u003c/sup\u003e, while \u003cem\u003eELF5\u003c/em\u003e encodes a transcription factor relevant to epithelial differentiation and innate immune response\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn total, 18 SNPs were prioritized based on their local Manhattan plots, representing two categories of long COVID risk loci. These SNPs are associated with biologically plausible candidate genes involved in pathways related to COVID-19 severity, immune response, and tissue-specific functions. Further experimental validation is required to determine their roles in long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSex-Biased Associations with COVID-19 Hospitalization among Top Candidate SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEpidemiological evidence indicates that women are more likely to experience long COVID but less likely to develop severe COVID-19\u003csup\u003e16\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e17\u003c/sup\u003e, potentially due to stronger immune responses to SARS-CoV-2 infection in women. To investigate whether the top 42 candidate SNPs associated with long COVID exhibit sex-biased associations with COVID-19 hospitalization, we analyzed publicly available sex-stratified GWAS summary statistics from the GRASP COVID database\u003csup\u003e18\u003c/sup\u003e generated based on UK Biobank samples.\u003c/p\u003e\n\u003cp\u003eWe selected the largest sex-stratified COVID-19 hospitalization GWAS (cases=1,343 and controls=262,886 for females; cases=1,917 and controls=221,174 for males), which includes mixed ancestry populations comparable to the four HGI COVID-19 GWAS datasets used in our study. Using the delta-z-score method to compare male and female GWAS results, we identified SNPs with differential effect sizes for COVID-19 hospitalization and calculated significance levels (Fig. 4A). Genomic inflation factors for both sex-stratified GWASs and the sex-biased differential GWAS were close to 1, indicating no evidence of inflation (Fig. 4B).\u003c/p\u003e\n\u003cp\u003eCompact local Manhattan plots (Fig. 4C) and forest plots (Fig. 4D) were generated for the 42 candidate SNPs. Due to the smaller sample sizes in the sex-stratified GWASs, only 15 SNPs reached nominal significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in at least one of the three analyses. Of these, rs17078348 (\u003cem\u003eSLC6A20\u003c/em\u003e), a well-established severe COVID-19 risk SNP, showed nominal significance in both male and female cohorts. Five SNPs displayed nominal significance exclusively in females, including rs75432325 (\u003cem\u003eTAC4\u003c/em\u003e), rs505922 (\u003cem\u003eABO\u003c/em\u003e), rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), and rs11766643 (\u003cem\u003eTFR2\u003c/em\u003e). Eight SNPs were nominally significant only in males, including rs897075 (\u003cem\u003eZKSCAN1\u003c/em\u003e), rs117169628 (\u003cem\u003eSLC22A31\u003c/em\u003e), rs7515509 (\u003cem\u003eAK5\u003c/em\u003e), rs76608815 (\u003cem\u003eSLC5A3\u003c/em\u003e), rs12585036 (\u003cem\u003eATP11A\u003c/em\u003e), rs5023077 (\u003cem\u003eFBRSL1\u003c/em\u003e), rs10774679 (\u003cem\u003eIFNAR21\u003c/em\u003e), and rs12329760 (\u003cem\u003eHLA-C\u003c/em\u003e). None of the SNPs passed the stringent multiple testing correction threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;6E-4), yet local Manhattan plots for 12 of the 42 SNPs revealed relatively weaker association signals in the sex-stratified GWAS compared to the stronger signals observed in HGI-B2 (Fig. 5). This reduction in signal strength likely reflects the smaller sample sizes in the sex-stratified datasets, which reduced statistical power to detect sex-biased associations. Finally, aggregating the absolute effect sizes of SNPs across the four groups (female-SNP-category 1, female-SNP-category 2, male-SNP-category 1, and male-SNP-category 2) based on the nominal association \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 revealed no significant differences (ANOVA \u003cem\u003eP\u003c/em\u003e=0.15). In summary, while the sex-biased GWAS analysis did not identify SNPs with statistically significant sex-biased associations with COVID-19 hospitalization, it highlighted 15 SNPs with nominal significance, including rs505922 (\u003cem\u003eABO\u003c/em\u003e) and rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), which have been previously implicated as risk variants for both severe COVID-19 and long COVID. These findings warrant further investigation in larger sex-stratified COVID-19 and long COVID GWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of Previously Reported Long COVID-Associated SNPs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether the 42 prioritized long COVID SNPs identified in our study overlap with or are proximal to those reported in two independent long COVID GWASs by Taylor et al.\u003csup\u003e2\u003c/sup\u003e and Chaudhary et al.\u003csup\u003e3\u003c/sup\u003e, a set of 83 SNPs derived from SNP network analyses and 3 SNPs identified via traditional GWAS methods from these studies was created. These SNPs were analyzed for their association signals in three HGI GWAS datasets (supplementary Table S1). Notably, rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e), identified by the HGI long COVID GWAS, was excluded from this evaluation as it displayed stronger associations with general COVID-19 phenotypes in the HGI datasets analyzed here. This SNP was already included among the 42 prioritized long COVID risk SNPs.\u003c/p\u003e\n\u003cp\u003eOur evaluation was guided by the hypothesis that true long COVID associations reported by Taylor et al.\u003csup\u003e2\u003c/sup\u003e and Chaudhary et al.\u003csup\u003e3\u003c/sup\u003e. would exhibit at least nominal significance in one of the HGI GWAS datasets. The larger sample sizes and higher statistical power of the HGI datasets enhance their ability to detect associations with traits closely related to long COVID, such as severe COVID-19 or susceptibility to SARS-CoV-2 infection. Moreover, overlap between our prioritized SNPs and those reported in previous studies would strengthen the hypothesis that the loci in question are implicated in long COVID pathogenesis.\u003c/p\u003e\n\u003cp\u003eAmong the three SNPs reported by Chaudhary et al.\u003csup\u003e3\u003c/sup\u003e (Fig. 6), rs643434 (\u003cem\u003eABO\u003c/em\u003e) and rs2080090 (\u003cem\u003eBPTF\u003c/em\u003e) demonstrated nominally significant associations (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) with COVID-19 hospitalization (HGI-B2) and susceptibility to COVID-19 (HGI-C2). rs643434 (\u003cem\u003eABO\u003c/em\u003e) surpassed genome-wide significance thresholds in both HGI-B2 (\u003cem\u003eP\u003c/em\u003e= 6.65E-24) and HGI-C2 (\u003cem\u003eP\u003c/em\u003e=8.69E-85) and showed non-significant association in HGI-B2 (\u003cem\u003eP\u003c/em\u003e=0.6) but demonstrated significantly differential effect sizes between HGI-B1 (hospitalized vs. non-hospitalized COVID-19) and HGI-B2 (\u003cem\u003eP\u003c/em\u003e=6E-5). This supported its classification as a \"category 2\" long COVID SNP (associated with both severe and mild COVID-19 phenotypes). Similarly, rs2080090 (\u003cem\u003eBPTF\u003c/em\u003e) showed stronger associations in HGI-B2 (\u003cem\u003eP\u003c/em\u003e= 1.9E-4) than in HGI-C2 (\u003cem\u003eP\u003c/em\u003e=2.5E-2), with no significant association in HGI-B1. This SNP was also categorized as a suggestive \"category 2\" long COVID SNP. In contrast, rs9273363 (\u003cem\u003eHLA-DQB1\u003c/em\u003e) did not exhibit significant associations across any of the HGI GWAS datasets analyzed (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.2). Nevertheless, among these 42 candidate SNPs, rs17219281 (\u003cem\u003eHLA-DQA1\u003c/em\u003e), a “category 1” long COVID SNP that tends to be more associated with hospitalized rather than mild COVID-19 (see Table 1), is ~50kb downstream of rs9273363 (\u003cem\u003eHLA-DQB1\u003c/em\u003e), strongly implicating that rs17219281 may be the real SNP associated with long COVID. Taken together, the three recently published long COVID loci, including \u003cem\u003eABO\u003c/em\u003e, \u003cem\u003eBPTF\u003c/em\u003e, and \u003cem\u003eHLA-DQB1\u003c/em\u003e, were completely covered by these 42 prioritized long COVID SNPs.\u003c/p\u003e\n\u003cp\u003eMeanwhile, of the 80 SNPs reported by Taylor et al.\u003csup\u003e2\u003c/sup\u003e, 12 displayed nominally significant associations in at least one HGI GWAS dataset (Supplementary Table S2 and Fig. 6). These included four SNPs associated with HGI-B2, five with HGI-B1, and seven with HGI-C2. Notably, rs4303401 (\u003cem\u003eFRMD6\u003c/em\u003e) surpassed the multiple-testing correction threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;1.5E-4), representing a strong candidate SNP. Expanding the analysis to include a ±500kb genomic window around the reported SNPs identified additional suggestive genome-wide associations in the HGI datasets. For example, in Fig. 6, rs62401842 (proximal to \u003cem\u003eKCTD16\u003c/em\u003e, which shows brain-specific expression) and rs7671107 and rs1017716 (adjacent to \u003cem\u003eANAPC4\u003c/em\u003e, a gene implicated in aging and strongly supported as eQTLs accordingly to GTEx eQTL database) emerged as promising candidates. Other noteworthy SNPs in Fig. 6 included rs12602210 (\u003cem\u003eBPTF\u003c/em\u003e) and rs17412601 and rs2290859 (both mapped to \u003cem\u003eNXPE3\u003c/em\u003e), which demonstrated suggestive or genome-wide significance in at least one HGI GWAS dataset. In summary, four loci, including \u003cem\u003eKCTD16\u003c/em\u003e, \u003cem\u003eANAPC4\u003c/em\u003e, \u003cem\u003eBPTF\u003c/em\u003e, and \u003cem\u003eNXPE3\u003c/em\u003e, revealed by Taylor et al\u003csup\u003e2\u003c/sup\u003e. derived from SNP network analysis are independently confirmed to be nominally associated with COVID-19 related phenotypes in the current study; among them \u003cem\u003eBPTF\u003c/em\u003e is a loci confirmed to be associated with long COVID by Chaudhary et al.\u003csup\u003e3\u003c/sup\u003e, Taylor et al., and current study. Therefore, these findings underscore the potential of integrating data from diverse GWAS to elucidate the genetic architecture of long COVID and highlight candidate loci for further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Long COVID SNPs Specifically Identified in Non-Hospitalized COVID-19 Cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify potential long COVID SNPs specific to non-hospitalized COVID-19 cases, we screened SNPs that showed suggestive genome-wide association signals in HGI-B1 (hospitalized vs. non-hospitalized, \u003cem\u003eP\u003c/em\u003e\u0026lt; 1E-5) and HGI-B1-vs-B2 (differential GWAS between HGI-B1 and HGI-B2, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) but were not significant in HGI-B2 (hospitalized vs. general population, \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) and were not in proximity to any genome-wide significant SNPs in HGI-B2. This selection identified 20 independent SNPs potentially associated with long COVID that specifically emerged from mild (non-hospitalized) COVID-19 cases (Table 2; local Manhattan plots accessible at GitHub link provided in the Method section; Fig. 7). All 20 SNPs showed suggestive genome-wide significant differences in effect sizes between HGI-B1 and HGI-B2 (\u003cem\u003eP\u003c/em\u003e\u0026lt;6E-5). Although none met the genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;5E-8), notable findings were observed. For example, in Fig. 7, rs62401842 (\u003cem\u003eKCTD16\u003c/em\u003e) exhibited the strongest association in HGI-B1 (\u003cem\u003eP\u003c/em\u003e=8.53E-7), while showing no association in HGI-B2 (\u003cem\u003eP\u003c/em\u003e=0.38) and a significant difference between HGI-B1 and HGI-B2 (\u003cem\u003eP\u003c/em\u003e=3.18E-7). The gene \u003cem\u003eKCTD16\u003c/em\u003e, encoding potassium channel tetramerization domain-containing 16\u003csup\u003e19\u003c/sup\u003e, is highly expressed in brain tissues according to the GTEx database. In addition, eight SNPs illustrated in Fig. 7 were mapped to genes with brain-related functions, including rs9799354 (\u003cem\u003eNLGN1\u003c/em\u003e), rs112842080 (\u003cem\u003eCPLX2\u003c/em\u003e), rs61858037 (\u003cem\u003eNRG3\u003c/em\u003e), rs61939166 (\u003cem\u003eKIF21A\u003c/em\u003e), rs367777 (\u003cem\u003eNAV3\u003c/em\u003e), rs56143829 (\u003cem\u003eWASF3\u003c/em\u003e), rs11454577 (\u003cem\u003eAKAP6\u003c/em\u003e), and rs6049828 (\u003cem\u003eSYNDIG1\u003c/em\u003e). Among these, \u003cem\u003eWASF3\u003c/em\u003e has been implicated in mitochondrial dysfunction and may mediate exercise intolerance in myalgic encephalomyelitis/chronic fatigue syndrome\u003csup\u003e20\u003c/sup\u003e. Additionally, among the 20 candidate SNPs identified, only three (rs62401842 [\u003cem\u003eKCTD16\u003c/em\u003e], rs9799354 [\u003cem\u003eNLGN1\u003c/em\u003e], and rs112842080 [\u003cem\u003eCPLX2\u003c/em\u003e]) exhibit male-biased associations with COVID-19 hospitalization at nominal significance levels (Fig. 8A, B). Furthermore, six candidate SNPs, including rs62401842 (\u003cem\u003eKCTD16\u003c/em\u003e), rs9799354 (\u003cem\u003eNLGN1\u003c/em\u003e), rs112842080 (\u003cem\u003eCPLX2\u003c/em\u003e), rs56143829 (\u003cem\u003eWASF3\u003c/em\u003e), rs4737438 (\u003cem\u003ePENK\u003c/em\u003e), and rs6049828 (\u003cem\u003eSYNDIG1\u003c/em\u003e), demonstrate sex-biased association patterns within a genomic window ranging from 500kb to 1000kb (Fig. 8C-H). However, the limited sample sizes in sex-stratified COVID-19 GWAS preclude any candidate SNPs from achieving statistical significance after correction for multiple testing in analyses of sex-biased associations with COVID-19 hospitalization. In summary, while no genome-wide significant SNPs specific to non-hospitalized COVID-19 cases were identified, these 20 SNPs provide suggestive evidence of association with long COVID in mild COVID-19. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptome-Wide Association Study Identifies Candidate Genes near Prioritized SNPs for COVID-19 and Long COVID Phenotypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate genetic mechanisms underlying COVID-19 and long COVID phenotypes, we performed a transcriptome-wide association study (TWAS) using S-MetaXcan across 48 GTEx tissues. This approach utilized COVID-19 GWAS summary statistics from HGI: HGI-B1, HGI-B2, HGI-C2, and HGI-B1-vs-B2. The analysis targeted 62 candidate SNPs previously prioritized for their potential association with long COVID and aimed to identify their adjacent genes whose expression levels are associated with COVID-19 hospitalization or susceptibility phenotypes, as well as long COVID.\u003c/p\u003e\n\u003cp\u003eOf the 62 candidate SNPs, two SNPs (rs190509934 near \u003cem\u003eACE2\u003c/em\u003e and rs9570861 near \u003cem\u003eOR7E156P\u003c/em\u003e) not included in the TWAS analysis. The former resides on chromosome X, which S-MetaXcan excludes due to its focus on autosomal genes, while the latter SNP had no protein-coding genes within a 500 kb window. The remaining 60 SNPs corresponded to 753 adjacent genes (Fig. S5) with detectable expression across the 48 GTEx tissues, which were analyzed in 192 (4 × 48) TWASs. \u003c/p\u003e\n\u003cp\u003eTo simplify interpretation, we retained only the strongest association (smallest p-value) for each gene across 48 GTEx tissues. This approach was justified by the hypothesis that specific tissues, rather than all tissues, are likely to drive the pathogenicity of COVID-19. Applying varying significance thresholds (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, 0.01, 0.001, 0.0001, 0.00001), the analysis revealed 422, 152, 43, 19, and 11 adjacent genes meeting these criteria in at least one of the four HGI datasets (Fig. S3, Table S2). Using a genome-wide significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;3.5E-7), three loci displayed significant associations: (1) \u003cem\u003eDPP9\u003c/em\u003e, showing the strongest association signal (\u003cem\u003eP\u003c/em\u003e=4.6E-18) in adipose visceral omentum tissue; (2) \u003cem\u003eCXCR6\u003c/em\u003e gene cluster, which included multiple genes (\u003cem\u003eCXCR6\u003c/em\u003e, \u003cem\u003eCCR3\u003c/em\u003e, \u003cem\u003eCCR1\u003c/em\u003e, and \u003cem\u003eLZTFL1\u003c/em\u003e), displaying the most significant association (\u003cem\u003eP\u003c/em\u003e=1.3E-10) in nerve tibial tissue; (3) \u003cem\u003eMUC1\u003c/em\u003e gene cluster, comprising \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eFAM189B\u003c/em\u003e, highlighted in multiple GTEx tissues, including lung and colon. Additional loci with suggestive TWAS significance (\u003cem\u003eP\u003c/em\u003e\u0026lt;3.5E-6) included \u003cem\u003eFYCO1\u003c/em\u003e, \u003cem\u003eSLC6A20\u003c/em\u003e, \u003cem\u003eXCR1\u003c/em\u003e, and \u003cem\u003eTHBS3\u003c/em\u003e, with specific associations to HGI datasets B1, B2, or C2 in distinct GTEx tissues.\u003c/p\u003e\n\u003cp\u003eTo investigate overlap with long COVID, TWAS was extended to four long COVID GWASs using the same S-MetaXcan framework. Genome-wide evaluation of TWAS signals across the eight HGI GWAS datasets (Fig. S4) confirmed that the top hits with association \u003cem\u003eP\u003c/em\u003e\u0026lt;1E-5 originated exclusively from COVID-19 hospitalization and susceptibility GWAS, with no contributions from long COVID GWAS (Fig. S4). Notably, the candidate genes proximal to these prioritized SNPs accounted for most of the top signals across the eight HGI GWAS (Fig. 9), with the exception of \u003cem\u003eCCR5\u003c/em\u003e, \u003cem\u003eEXOSC7\u003c/em\u003e, \u003cem\u003eFLT1P1\u003c/em\u003e, \u003cem\u003eRLN1\u003c/em\u003e, \u003cem\u003eGBAP1\u003c/em\u003e, \u003cem\u003eARHGEF2\u003c/em\u003e, \u003cem\u003eZSCAN31\u003c/em\u003e, \u003cem\u003eZKSCAN3\u003c/em\u003e, \u003cem\u003ePGMSP2\u003c/em\u003e, \u003cem\u003ePAQR5\u003c/em\u003e, and \u003cem\u003eTMIE\u003c/em\u003e. Further in-depth evaluation of these outlier genes in long COVID GWAS revealed most of them lack of significance (Fig. S5), reinforcing the utility of prioritizing top candidate SNPs and their adjacent genes for downstream analyses. Interestingly, although the strongest associations identified in COVID-19 hospitalization or susceptibility GWASs (\u003cem\u003eP\u003c/em\u003e\u0026lt;1E-5) were not genome-wide significant in long COVID datasets, four genes (\u003cem\u003eCCR1\u003c/em\u003e, \u003cem\u003eDPP9\u003c/em\u003e, \u003cem\u003eMUC1\u003c/em\u003e, and \u003cem\u003eTHBS3\u003c/em\u003e; Fig. S5) showed nominally significant associations (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in long COVID GWASs across specific tissues, such as colon transverse, testis, and heart atrial appendage. These associations were generally weaker than those observed for acute COVID-19 phenotypes, suggesting tissue-specific contributions to long COVID pathophysiology. In details, \u003cem\u003eDPP9\u003c/em\u003e demonstrated strong associations in adipose-related tissues for COVID-19 hospitalization but weaker signals in colon transverse for long COVID. \u003cem\u003eTHBS3\u003c/em\u003e showed widespread associations across 19 GTEx tissues for COVID-19 hospitalization but exhibited weaker signals in long COVID, particularly in colon sigmoid and spleen. \u003cem\u003eMUC1\u003c/em\u003e and \u003cem\u003eCCR1\u003c/em\u003e exhibited similar patterns, with weaker long COVID associations in colon transverse and heart atrial appendage, respectively. Furthermore, using a relaxed threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) identified additional adjacent genes associated with long COVID (Fig. 10 \u0026amp; Fig. S6-7). These included \u003cem\u003eOAS3\u003c/em\u003e (rs10774679), \u003cem\u003eTMPRSS2\u003c/em\u003e (rs12329760), \u003cem\u003eHLA-A\u003c/em\u003e (rs4s9260038), \u003cem\u003eHLA-C\u003c/em\u003e (rs1634761), \u003cem\u003eHLA-DQA1\u003c/em\u003e (rs17219281), \u003cem\u003eWASF3\u003c/em\u003e (rs65143829), \u003cem\u003eIFNAR2\u003c/em\u003e (rs2834164), \u003cem\u003eSFTPD\u003c/em\u003e (rs61860402), and \u003cem\u003eGSDMA/B\u003c/em\u003e (rs9916158), with associations enriched in tissues such as heart, brain, colon, adipose, and muscle. Notably, the well-established \u003cem\u003eABO\u003c/em\u003e locus, a COVID-19 susceptibility locus, did not show significant TWAS signals. This suggests that its effects may stem from non-expression-related mechanisms, such as genetic mutations influencing blood types.\u003c/p\u003e\n\u003cp\u003eIn summary, TWAS analyses identified 52 SNP-gene pairs nominally associated with long COVID across tissues. While genes such as \u003cem\u003eDPP9,\u003c/em\u003e \u003cem\u003eTHBS3\u003c/em\u003e, \u003cem\u003eCCR1\u003c/em\u003e, and \u003cem\u003eMUC1\u003c/em\u003e emerged as potential shared contributors to acute and long COVID phenotypes, their tissue-specific associations suggest distinct mechanisms driving long COVID. These findings highlight the importance of integrative approaches in uncovering the molecular underpinnings of long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenome-Wide Analysis of Gene Expression Associations with Candidate Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate gene expression associations with long COVID-related phenotypes, we performed a phenome-wide analysis of gene expression associations (Pheno-TWAS) analysis targeting 918 genes located within a 1Mb window of 62 candidate SNPs previously linked to COVID-19. Recognizing the limitations of published long COVID GWASs, which often rely on imprecisely defined phenotypes, this analysis extended beyond prior long COVID studies by utilizing a manually curated TWAS database encompassing 418 phenotypes across 48 GTEx tissues. Of the 918 tested genes, 400 (43.6%), including 87 associated with \u003cem\u003eHLA\u003c/em\u003e loci (e.g., \u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eHLA-A\u003c/em\u003e, and \u003cem\u003eHLA-C\u003c/em\u003e; Fig. S8-10), passed a stringent bonferroni-adjusted significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026lt;2.7E-9). Genes located near \u003cem\u003eHLA\u003c/em\u003e loci emerged as top hits, demonstrating significant associations with immune-related disorders such as asthma, rheumatoid arthritis, psoriasis, coeliac disease, and systemic lupus erythematosus—conditions that may contribute to long COVID susceptibility. In addition to \u003cem\u003eHLA\u003c/em\u003e-associated loci, 313 genes across 44 non-\u003cem\u003eHLA\u003c/em\u003e loci surpassed the genome-wide significance threshold in TWASs. Key findings include: (1) \u003cem\u003eIFNAR2\u003c/em\u003e: strongly associated with COVID-19 hospitalization (\u003cem\u003eP\u003c/em\u003e=2.6E-10; Fig. S11); (2) \u003cem\u003eGSDMB\u003c/em\u003e: linked to asthma (\u003cem\u003eP\u003c/em\u003e=2.7E-57; Fig. S12); (3) \u003cem\u003eBPTF\u003c/em\u003e: associated with trunk fat percentage (\u003cem\u003eP\u003c/em\u003e=1E-18; Fig. S13); (4) \u003cem\u003eCCDC171\u003c/em\u003e: associated with body fat percentage (\u003cem\u003eP\u003c/em\u003e=2E-35; Fig. S14). Interestingly, \u003cem\u003eFOXP4\u003c/em\u003e expression, which did not exhibit significant associations with long COVID phenotypes in prior TWASs, demonstrated a suggestive association with sleep duration (\u003cem\u003eP\u003c/em\u003e=1.1E-5; Fig. S15). This finding highlights the potential for phenome-wide approaches to uncover previously uncharacterized associations relevant to long COVID. In total, pheno-TWAS identified 45 candidate SNPs associated with genome-wide significant expression-linked phenotypes. These include three major \u003cem\u003eHLA\u003c/em\u003e loci (\u003cem\u003eHLA-DQA2\u003c/em\u003e, \u003cem\u003eHLA-A\u003c/em\u003e, and \u003cem\u003eHLA-C\u003c/em\u003e) as well as non-\u003cem\u003eHLA\u003c/em\u003e loci such as \u003cem\u003eIFNAR2\u003c/em\u003e, \u003cem\u003eBPTF\u003c/em\u003e, \u003cem\u003eCCDC171\u003c/em\u003e, and \u003cem\u003eGSDMB\u003c/em\u003e. The implicated phenotypes—ranging from COVID-19 hospitalization and immune disorders to body composition traits and sleep duration—provide a broad spectrum of potential biological pathways contributing to long COVID pathophysiology. Full results, including nominally significant associations, are provided in Supplementary Table S3.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur integrative analysis identifies 62 candidate loci potentially associated with long COVID, leveraging large-scale COVID-19 GWAS datasets from the HGI. This approach addresses the inherent limitations of long COVID-specific GWAS, such as limited sample sizes and phenotype heterogeneity, by utilizing proxy phenotypes like SARS-CoV-2 susceptibility and COVID-19 hospitalization. By categorizing genetic variants into three groups—those associated with hospitalized COVID-19, those linked to both hospitalized and non-hospitalized cases, and those specific to non-hospitalized cases—our findings provide a scalable framework to investigate the genetic underpinnings of long COVID.\u003c/p\u003e\n\u003cp\u003eThe analysis highlights 42 genome-wide or suggestive loci, including well-documented variants such as rs12660421 (\u003cem\u003eFOXP4\u003c/em\u003e) and rs505922 (\u003cem\u003eABO\u003c/em\u003e), which have been implicated in severe COVID-19 outcomes and long COVID susceptibility. Notably, additional loci like rs17219281 (\u003cem\u003eHLA-DQA1\u003c/em\u003e), rs9260038 (\u003cem\u003eHLA-A\u003c/em\u003e), rs1634761 (\u003cem\u003eHLA-C\u003c/em\u003e), rs7251000 (\u003cem\u003eDPP9\u003c/em\u003e), rs9916158 (\u003cem\u003eGSDMB\u003c/em\u003e), rs12602210 (\u003cem\u003eBPTF\u003c/em\u003e), rs17078348 (\u003cem\u003eCCR1\u003c/em\u003e), and rs41264915 (\u003cem\u003eTHBS3\u003c/em\u003e), rs190509934 (\u003cem\u003eACE2\u003c/em\u003e) and rs12329760 (\u003cem\u003eTMPRSS2\u003c/em\u003e) were identified. Furthermore, a unique category comprising 20 SNPs emerged specifically in non-hospitalized COVID-19 cases. These include rs62401842 (\u003cem\u003eKCTD16\u003c/em\u003e) and rs56143829 (\u003cem\u003eWASF3\u003c/em\u003e), mapped to genes enriched in brain-related functions, underscoring the complexity of long COVID’s etiology.\u003c/p\u003e\n\u003cp\u003eWhile this approach provides critical insights, certain limitations warrant consideration. First, the reliance on proxy phenotypes may obscure SNPs specific to mild COVID-19 cases, given the skewed representation of hospitalized patients in HGI datasets. Indeed, non-hospitalized-specific SNPs did not reach genome-wide significance, likely reflecting this bias. Nevertheless, 20 suggestive SNPs, enriched near genes implicated in neurocognitive and age-related pathways, merit further validation in independent cohorts. Second, the TWAS conducted using S-MetaXcan are constrained by the limited tissue specificity of available GTEx datasets and COVID related GWASs. Despite identifying 7 loci—\u003cem\u003eHLA-DQA1\u003c/em\u003e, \u003cem\u003eHLA-A\u003c/em\u003e, \u003cem\u003eHLA-C\u003c/em\u003e, \u003cem\u003eDPP9\u003c/em\u003e, \u003cem\u003eCCR1\u003c/em\u003e, \u003cem\u003eTHBS3\u003c/em\u003e, \u003cem\u003eGSDMB\u003c/em\u003e—that emerged as promising TWAS hits for long COVID, the potential absence of COVID-19-specific tissues, such as nasopharyngeal or immune-relevant sites, may hinder the detection of tissue-specific effects pertinent to long COVID's heterogeneity.\u003c/p\u003e\n\u003cp\u003eOur findings underscore the utility of integrative approaches to prioritize genetic risk loci for complex traits like long COVID. The discovery of loci such as \u003cem\u003eFOXP\u003c/em\u003e4, \u003cem\u003eABO\u003c/em\u003e, and \u003cem\u003eHLA-DQA1\u003c/em\u003e, as well as \u003cem\u003eBPTF\u003c/em\u003e, suggests shared biological pathways between acute COVID-19 severity and its chronic manifestations. Additionally, the identification of brain-enriched loci in non-hospitalized cases aligns with emerging evidence linking long COVID to neuroinflammatory and neurodegenerative processes. Future studies, particularly those incorporating diverse populations and long-term follow-up data, are essential to refine these associations and unravel the intricate mechanisms underlying long COVID.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to all researchers who participated and contributed to the HGI and GRASP project, as without their generosities in freely sharing the two COVID-19 hospitalization GWASs and other COVID-19 related GWASs as these were essential for the current finding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Conflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no biomedical financial interests or potential conflicts of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLammi, V.\u003cem\u003e et al.\u003c/em\u003e Genome-wide Association Study of Long COVID. \u003cem\u003emedRxiv\u003c/em\u003e, 2023.2006.2029.23292056, doi:10.1101/2023.06.29.23292056 (2023).\u003c/li\u003e\n\u003cli\u003eTaylor, K.\u003cem\u003e et al.\u003c/em\u003e Genetic risk factors for severe and fatigue dominant long COVID and commonalities with ME/CFS identified by combinatorial analysis. \u003cem\u003eJournal of translational medicine\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 775, doi:10.1186/s12967-023-04588-4 (2023).\u003c/li\u003e\n\u003cli\u003eChaudhary, N. 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Prioritized long COVID SNPs based on integrative analysis of published COVID-19 GWASs, including HGI-C2 (SASR-CoV2 susceptibility), HGI-B1 (hospitalized vs. non-hospitalized COVID-19), and HGI-B2 (hospitalized vs. general population).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"948\" height=\"894\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eNote: HGI: the COVID-19 host genetics initiative; chr: chromosome; pos: hg38 genomic position; allele: alternative allele; AF: allele frequency for alternative allele; diffPval: statistical significance \u003cem\u003eP\u003c/em\u003e for differential effect size of each common SNP by comparing the two HGI GWASs, including HGI-B1 and HGI-B2; Hosp: long COVID SNPs tend to be specifically emerged in hospitalized COVID-19 cases; Both: long COVID SNPs tend to be emerged in both hospitalized and non-hospitalized COVID-19 cases. Association Ps passed the suggestive genome-wide association significance threshold of P \u0026lt; 1E-7 are highlighted in light red color, and differential effect size significance P \u0026lt; 0.05 is colored with light green.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Potential long COVID risk SNPs emerged specifically from non-hospitalized COVID-19 cases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg 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\" width=\"946\" height=\"472\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote: abbreviations in the table headers are similar to that in Table 1.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Supplementary Tables","content":"\u003cp\u003eSupplemental tables are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, SARS-CoV2, Long COVID, GWAS, TWAS, Hospitalization, Susceptibility","lastPublishedDoi":"10.21203/rs.3.rs-5807438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5807438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLong COVID presents a significant public health challenge, characterized by over 200 reported symptoms spanning multiple organ systems. Despite its complexity, genome-wide association studies (GWAS) offer a pathway to uncovering genetic risk factors, though progress has been hindered by the disorder's symptom heterogeneity and the limited power of available datasets. Recent long COVID GWASs have highlighted key genetic associations, such as variants close to \u003cem\u003eFOXP4\u003c/em\u003e, \u003cem\u003eABO\u003c/em\u003e, and \u003cem\u003eHLA-DQA1\u003c/em\u003e, while underscoring the limitations posed by small sample sizes, restricted diversity, and misclassification biases. Here, we performed an integrative analysis using COVID-19 GWAS data from the Host Genetics Initiative (HGI, release 7) by leveraging proxy phenotypes to overcome the challenges of limited sample size or heterogeneity of long COVID-specific cohorts. 62 independent single nucleotide polymorphisms (SNPs) were prioritized as potentially associated with long COVID and categorized into three groups: (1) Severe COVID-19-Specific SNPs, such as SNPs mapped to two well-known loci involved in SARS-CoV2 entry (\u003cem\u003eACE2\u003c/em\u003e [rs190509934] and \u0026nbsp;\u003cem\u003eTMPRSS2\u003c/em\u003e[rs12329760]), as well as variants of \u003cem\u003eDPP9\u003c/em\u003e (rs7251000), \u003cem\u003eFOXP4\u003c/em\u003e(rs12660421) and \u003cem\u003eHLA-DQA2\u003c/em\u003e (rs17219281), exhibiting associations with severe COVID-19 but displaying weaker signals in non-hospitalized COVID-19 cases; (2) SNPs Associated with Both Severe and Mild COVID-19, including the SNP close to \u003cem\u003eABO\u003c/em\u003e(rs505922), representing a catalog of SNPs predispose to both acute COVID-19 and chronic long COVID; and (3) Non-Hospitalization-Specific SNPs, such as variants in \u003cem\u003eKCTD16\u003c/em\u003e (rs62401842) and \u003cem\u003eWASF3\u003c/em\u003e (rs56143829), highlighting genetic contributors specific to mild COVID-19 cases that might also contribute to long COVID. Further transcriptome-wide association studies (TWASs) across 48 GTEx tissues, leveraging GWAS data on COVID-19 hospitalization, susceptibility, and long COVID from the HGI consortium, revealed distinct tissue-specific patterns of association. Compared to the acute COVID-19 phenotypes, long COVID exhibited weaker association signals across heart, brain, and muscle-related tissues, as determined by correlations between gene expression of adjacent genes of candidate SNPs (43 of 62 SNPs) and different COVID phenotypes. Notable TWAS hits included \u003cem\u003eDPP9\u003c/em\u003e (rs7251000), \u003cem\u003eCCR1\u003c/em\u003e (rs17078348), and \u003cem\u003eTHBS3\u003c/em\u003e (rs41264915). Phenome-wide TWASs also identified additional significant associations with long COVID related phenotypes, such as \u003cem\u003eHLA-DQA1\u003c/em\u003e(rs17219281), \u003cem\u003eHLA-A\u003c/em\u003e (rs9260038), and \u003cem\u003eHLA-C\u003c/em\u003e (rs1634761) associated with immune-related diseases, \u003cem\u003eGSDMB\u003c/em\u003e(rs9916158) associated with asthma, \u003cem\u003eFOXP4\u003c/em\u003e(rs12660421) linked with sleep duration, highlighting their potential roles in long COVID pathophysiology. Therefore, current integrative approach offers a scalable framework for long COVID research by maximizing the statistical power of existing large-scale COVID-19 GWASs and provides novel insights into the genetic underpinnings of long COVID.\u003c/p\u003e","manuscriptTitle":"Integrative Genome-Wide Association Studies of COVID-19 Susceptibility and Hospitalization Reveal Risk Loci for Long COVID","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 09:04:20","doi":"10.21203/rs.3.rs-5807438/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c912ba4-ba58-413b-a5ce-e27847ff54ef","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43196154,"name":"Biological sciences/Genetics/Genetic association study/Genome-wide association studies"},{"id":43196155,"name":"Health sciences/Diseases/Infectious diseases/Viral infection"}],"tags":[],"updatedAt":"2025-04-02T09:04:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 09:04:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5807438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5807438","identity":"rs-5807438","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}