Genome-wide association studies of Long COVID and post-acute complications of SARS-CoV-2 in the UK Biobank Data

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We performed two genome-wide association studies (GWAS) in UK Biobank COVID-19 positive individuals to identify the genetic variants associated with Long COVID (LC) and post-acute cardiovascular complications of SARS-CoV-2 (PACS-CVD). The LC cohort comprised 8,469 participants (68% cases). The PACS-CVD cohort included 105,175 individuals (2% cases). LC GWAS identified 15 independent signals at suggestive significance (p-value<5×10⁻⁶), with 73.3% validated. The fully validated variant, rs12335232 (ADCY8), has been linked to memory decline, COVID-19 infection and severity. Other loci were near CHRNA7 (neuroinflammation, COVID-19 severity) and RNU7-126P (COVID-19 hospitalization). These findings consistently demonstrate shared biological pathways between acute infection and persistent symptoms. PACS-CVD GWAS identified 14 suggestive loci, mainly near genes linked to cardiovascular and metabolic functions (SAYSD1/KCNK5, FLT1) or COVID-19 severity (ROR2). These results enhance the genetic understanding of Long COVID and PACS-CVD pathophysiology and highlight several potential therapeutic targets for both conditions. Health sciences/Medical research/Genetics research Health sciences/Medical research/Epidemiology Figures Figure 1 Figure 2 Figure 3 Introduction Post-COVID-19 conditions (PCC) are defined as long-term post-acute health consequences caused by a COVID-19 infection 1 . As of August 2024, the global cumulative incidence of PCC was estimated to be 400 million individuals 2 , resulting in a substantial burden on healthcare systems worldwide. PCC can be distinguished between Long COVID (LC) and post-acute cardiovascular complications of SARS-CoV-2 infection (PACS-CVD) 3 , 4 . While LC is characterised by the persistence or onset of COVID-19 related symptoms beyond the COVID-19 infection acute phase, PACS-CVD typically refer to more severe thromboembolic or cardiovascular complications within the same time frame. While clinical and epidemiological studies have identified the risk factors and symptoms of Long COVID and PACS-CVD 5 , we still lack a clear understanding of the biological mechanisms behind these conditions. Genetic predisposition is likely to play a role in individual susceptibility, as recent studies have shown 6 . In this context, genome-wide association studies (GWAS) 7 can be a valuable approach to uncover common genetic variants linked to LC and PACS-CVD, potentially revealing the pathophysiological pathways involved. In this study, we used UK Biobank (UKBB) data to conduct two separate GWAS: one focusing on Long COVID and another on PACS-CVD. By investigating these two outcomes in parallel, we aimed to identify genetic variants associated with the susceptibility of each condition, and to explore whether they share common or distinct genetic determinants. Results Study cohorts Long COVID discovery cohort Of the 275,234 UK Biobank participants with valid linkage to COVID-19 surveillance data, 46,793 individuals had both a confirmed positive test and complete responses to the Health and Well-being online questionnaire. Out of those, 8,469 participants fulfilled the selection criteria, with 5,768 (68%) reporting at least one symptom persisting beyond 30 days after infection (cases), and 2,701 (32%) without reporting any symptom beyond 30 days after infection (controls) (Supplementary Figure 1). Long COVID validation cohort Out of the 8,723 that participated in the COVID-19 serology study (waves 1-6) and had all the antibody tests performed, 704 had reported at least one positive result and 682 fulfilled our selection criteria. Of those, 244 (36%) were classified as cases, whereas 438 (64%) were classified as controls. PACS-CVD cohort 115,007 UK Biobank participants had a valid linkage to hospital episode statistics (HES) data and to COVID-19 surveillance data, and a positive PCR test. Of these, 105,174 fulfilled the selection criteria to be part of the PACS-CVD base cohort. A total of 1,885 (2%) had at least one PACS-CVD diagnosis beyond 30 days after infection and were therefore classified as PACS-CVD cases, whereas 103,289 (98%) were classified as PACS-CVD controls (Supplementary Figure 2). Baseline characteristics of the study cohorts are presented in Table 1. The mean age was 66 for LC participants, 65 for LC validation cohort, and 67 years for PACS-CVD participants. In all cohorts, female participants outnumbered male participants. Indexes of multiple deprivation (IMD) were 14, 20, and 17, respectively. Genome-wide association (GWAS) analyses From the 784,256 variants loaded from genotype calls, 529,562 and 472,043 passed the quality control in the LC and PACS-CVD GWAS, respectively, and were used to build a whole-genome regression model in REGENIE Step 1. From the 93,095,623 imputed variants, 92,775,302 passed the initial quality control (kept the first instance when there are duplicated variants). Post-quality control included up to 8.5 million variants in both analyses. Although no SNPs reached genome-wide significance ( p -value ≤ 5 × 10 −8 ) in the LC GWAS, 15 genomic loci achieved suggestive significance threshold ( p- value ≤ 5 × 10 −6 ) (Figure 1A, Table 2). Of these, one variant -rs12335232 in the ADCY8 gene- was fully validated (Figure 2), with the G allele associated with an increased risk of LC (OR = 1.32, 95%CI = 1.17–1.49). Ten additional variants were partially validated. These included rs375087201 (between VENTXP4 and AC099754.1 ), with allele G associated with an increased risk of LC (OR = 1.26, 95%CI = 1.15–1.39); rs34746824 (between RP11 - 206P5.2 and RNU7-126P ), with allele C associated with a protective effect (OR = 0.83, 95%CI = 0.77–0.90); rs147065000 (within ZNF454 ), with its allele G associated also protective (OR = 0.79, 95%CI = 0.72–0.87); rs10966605 (between RMRPP5 and RN7SKP120 ), with its allele T associated with a decreased risk (OR = 0.62, 95%CI = 0.51–0.75); rs147950355 (within TMEM246 ), with its allele G linked with an increased risk of LC (OR = 2.24, 95%CI = 1.62–3.11); rs9415008 (between PPA1 and NPFFR1 ), with its allele A associated with an increased risk (OR = 1.25, 95%CI = 1.14–1.37); rs4551739, within gene NAV2 , with its allele C associated with a protective effect (OR = 0.78, 95%CI = 0.70–0.86); rs2702217 (gene GLT1D1 ), with its allele G linked to decreased risk (OR = 0.83, 95%CI = 0.77–0.89); rs11621087 (between PTGR2 and RP5-1021I20.4 ), with its allele C associated with a decreased risk (OR = 0.84, 95%CI = 0.79–0.90); rs78215228 ( CHRNA7 ), with its allele G associated with a decreased risk of LC (OR = 0.59, 95%CI = 0.47–0.73). The remaining 4 genomic loci were not validated. Regional plots of all the genomic loci are shown in Figure 3. Fourteen SNPs were identified with the suggestive significance threshold to be associated with PACS-CVD susceptibility (Figure 1B, Table 3). In chromosome 1, rs11582898 (intergenic, between RP11-115A15.4 and RP11-115A15.2 ) had the T allele associated with an increased risk of LC (OR = 1.66, 95%CI = 1.35–2.03). In chromosome 3, rs7643274 (intergenic, between TRIM42 and RP11-691G17.1 ) showed a protective association, with the A allele linked to reduced risk (OR = 0.84, 95%CI =0.79–0.90). On chromosome 4, rs10212904 (ncRNA_intronic in AC108142.1 ) also showed a protective effect (G allele, OR = 0.80, 95%CI = 0.73–0.88). On chromosome 6, rs147544694 (intergenic, between SAYSD1 and KCNK5 ) showed a strong association with increased risk (C allele, OR = 1.84, 1.46–2.32). On chromosome 7, two variants reached the threshold: rs147084175 (intronic in CCDC126 ), with the C allele associated with a protective effect (OR = 0.56, 95%CI: 0.44–0.72), and rs818468 (intronic in PUS7), where the T allele increased risk (OR = 1.19, 95% CI: 1.11–1.28). On chromosome 9, rs117559490 (intergenic, between RP11-440G5.2 and ROR2 ) showed a protective association (G allele, OR = 0.62, 95%CI: 0.51–0.75). On chromosome 10, rs2486033 (intergenic, between RP11-282I1.1 and RP11-282I1.2 ) had the A allele associated with increased risk (OR = 1.21, 95% CI: 1.11–1.31). On chromosome 11, rs7120231 (intergenic, between NELL1 and CTD-2019O4.1 ) showed a protective effect (C allele, OR = 0.55, 95% CI: 0.43–0.71). On chromosome 13, rs17554072 (intergenic, between FLT1 and EIF4A1P7 ) showed an increased risk (A allele, OR = 1.51, 95% CI: 1.30–1.76), while rs61947186 (ncRNA_intronic in RP11-16D22.2 ) showed a protective effect (T allele, OR = 0.69, 95%CI = 0.59–0.80). On chromosome 19, rs1558138 (intronic in PTPRS ) was protective (C allele, OR = 0.84, 95% CI: 0.79–0.90). Finally, two variants were located on chromosome 21: rs8126522 (intronic in BACH1 ), where the T allele was associated with a protective effect (OR = 0.60, 95% CI: 0.48–0.74), and rs56067353 (intronic in RUNX1 ), where the T allele was associated with increased risk (OR = 1.68, 95% CI: 1.35–2.08). Long COVID subtypes analysis The overall Long COVID cohort for the subtype analysis had 20,472 participants. For the respiratory and chest symptoms, there were 477 (2%) cases and 19,996 (98%) controls. 10 genomic loci reached the suggestive significance threshold (Supplementary Figure 3A, Supplementary Table 4). For the ENT (ear, nose and throat) subtype, we found 633 (3%) cases and 19,839 (97%) controls. 12 genomic loci reached the suggestive significance threshold (Supplementary Figure 3B, Supplementary Table 5). For the neurological subtype, we found 1,078 (5%) cases and 19,394 (95%) controls. 10 genomic loci reached the suggestive significance threshold (Supplementary Figure 3C, Supplementary Table 6). For the fatigue subtype, we found 3,161 (15%) cases and 17,311 (85%) controls. 14 genomic loci reached the suggestive significance threshold (Supplementary Figure 3D, Supplementary Table 7). Discussion Summary of k ey study f indings Our genome-wide association study of Long COVID in the UK Biobank identified 15 lead genetic variants that reached suggestive statistical significance ( p -value < 5×10⁻⁶) in the discovery cohort. Among these, eleven variants (73.3%) demonstrated robustness during validation analysis, with one variant (rs12335232 in the ADCY8 gene) achieving full validation and 10 variants showing partial validation in an independent UK Biobank sub-cohort, which used an alternative Long COVID phenotype definition. The high replication rate across different Long COVID phenotype definitions within the UK Biobank population adds confidence to our findings. However, most variants were in genetic positions that do not directly code for proteins (non-coding regions), with seven located between genes, 7 within genes (although intronic), and one in a 3' UTR region (rs147065000 in ZNF454 ). Importantly, the suggestive genetic variants identified in our study are predominantly located within or adjacent to genes whose established functions correspond with hypothesized pathogenic mechanisms of Long COVID. For example, three genes containing these variants have been previously linked with COVID-19 outcomes: ADCY8 (rs12335232) linked to COVID-19 infection and severity, CHRNA7 (rs78215228) to COVID-19 infection, and the RNU7-126P (rs34746824) to COVID-19 hospitalization and severity. These findings provide evidence that acute COVID-19 disease, and its persistent symptoms share common biological pathways 8-10 . In addition, the result of ADCY8 gene as a fully validated Long COVID risk locus is important. ADCY8 encodes adenylyl cyclase 8, a key enzyme in cAMP signalling that plays crucial roles in brain function, synaptic plasticity, and memory formation 11,12 . Its links to COVID-19 severity and memory decline support the epidemiological studies that show high prevalence of cognitive dysfunction in patients with Long COVID 13-15 . Similarly, CHRNA7 , which is involved in neuroinflammation and immune response 16,17 , suggests a possible connection between severe COVID-19 and lasting neurological symptoms 18,19 . Moreover, the enrichment of variants near genes associated with neuropsychiatric traits is also consistent with the high prevalence of cognitive impairment, depression, and anxiety in Long COVID patients 20 . This overlap hints that people with a genetic background for mental health conditions might be more likely to experience neurological issues from Long COVID. Of note, the identification of metabolic trait-associated genes ( ADH5P4, LIN7A, TMEM246 ) aligns with emerging evidence of metabolic dysfunction in Long COVID pathophysiology 21,22 . Separately, we found 14 lead variants with suggestive significance for PACS-CVD. To our best knowledge, this is the first GWAS specifically focused on PACS-CVD. Most of them were near genes that have established roles in cardiovascular disease or its risk factors. For example, SAYSD1 and KCNK5 (rs147544694) linked to coronary artery disease 23-26 , myocardial infarction 27-29 , and blood pressure regulation 30-32 , and FLT1 (rs17554072), associated with coronary artery disease 23,24,26,33,34 and triglycerides 28 . This cardiovascular gene enrichment corroborates observational evidence that SARS-CoV-2 infection may trigger persistent cardiovascular complications and metabolic dysfunction. Similarly, the identification of ROR2 (rs117559490), previously associated with COVID-19 infection and severity, also suggests shared pathogenic mechanisms between acute infection and post-acute complications. The presence of variants near neuropsychiatric-associated genes including TRIM42 (bipolar disorder 35 ) and PUS7 (ADHD, autism, bipolar disorder 36 ) indicates potential overlap with neurological sequelae, with an immune-related gene RUNX1 linking to inflammatory disease such as rheumatoid arthritis 37 and asthma 38 . Our results in broader context A few GWAS have been conducted to identify common genetic variants associated with Long COVID and have revealed several loci of interest. The most significant and consistently highlighted finding in Long COVID genetics is the association with variants near or within the FOXP4 gene (rs9367106-C, P = 1.76×10 -10 ). This locus was first identified in a meta-GWAS analysis by the COVID-19 Host Genetics Initiative (HGI) 6 . FOXP4 encodes a transcription factor predominantly found in lung tissue and immune cells, and has previously been connected to lung function, susceptibility to severe acute COVID-19 39 , and lung development 40 . Notably, conditional analyses in this HGI study found that the FOXP4 locus’s association with Long COVID remains significant after adjusting for acute COVID-19 hospitalization status, which implies that even people who experience a mild initial infection could be at risk for persistent pulmonary issues if this FOXP4 -related pathway is dysregulated. Another important genomic region tied to Long COVID is the Human Leukocyte Antigen (HLA) complex. A significant study, which looked at diverse populations and used data from 23andMe with over 53,000 cases and 120,000 controls, found a noteworthy locus at HLA-DQA1 to HLA-DQB1 region 41 . This supports an immune-mediated component in Long COVID pathogenesis, such as autoimmunity or an altered or prolonged immune response to persistent viral antigens or viral debris 42 . This research also showed a connection between the ABO blood group and Long COVID. The ABO locus has previously been implicated in susceptibility to and severity of acute COVID-19 infection, with non-O blood groups often carrying a higher risk for severe outcomes or thromboembolic complications 43,44 . The extended association of the ABO locus with Long COVID suggests that biological mechanisms influenced by blood group antigens may play significant roles in symptom persistence. A recent GWAS in a German cohort reported several genetic regions linked to Long COVID symptoms 45 . The most notable finding was an association with SNP rs10893121 ( P = 2.5×10 -6 ) close to genes (specifically olfactory receptor families 4, 6, and 10) responsible for our sense of smell. Given that impairment of smell and taste represents a pathognomonic feature of both acute COVID-19 and Long COVID, this finding suggests a potential genetic basis for specific sensory dysfunction. Separately, studies focused on acute COVID-19-related anosmia have implicated olfactory-related genes UGT2A1 and UGT2A2 46 . These genes, expressed in the olfactory epithelium and involved in odorant metabolism, showed genome-wide significant associations with COVID-19-related loss of smell or taste in acute infection. Recent single-cell analysis shown that UGT2A1 is strongly expressed in sustentacular cells, the primary target of SARS-CoV-2 infection in olfactory tissue 47 . Although these associations focused on acute COVID-19 symptoms rather than Long COVID specifically, the overlapping mechanisms of olfactory dysfunction appeared to be potential shared pathways 48 . A critical limitation in Long COVID genetics research is the lack of consistent replication across studies 49 . None of the genetic loci identified in prior studies, including those from our analysis, have achieved genome-wide replication across independent cohorts. For example, even the strongest FOXP4 association in the HGI did not pass statistical significance in the subsequent 23andMe analysis, despite the latter study having over eight times more cases. This pattern may indicate several underlying issues. First, there appears to be substantial heterogeneity in Long COVID phenotypes across different populations (e.g. self-reported symptoms vs clinically validated scores), and different recruitment strategies (e.g. 23andMe's direct-to-consumer model vs. clinically ascertained cohorts in some HGI studies vs a sub-cohort nested in the UK Biobank population in our study). Second, the underlying definition of LC requires to use patient-reported data, which likely introduces potential misclassification bias. Individual perception of symptoms, willingness to report symptoms, and understanding of "Long COVID" as a diagnostic entity can vary significantly across populations and geographic regions 50 . It hinders the detection of genetic signals unless they exert very strong or universal effects across different Long COVID manifestations. Implications for future research Overall, the absence of a clear, widely recognised, and uniformly implemented definition for Long COVID presence a major challenge in genetic research 51 . Current evidence indicated that Long COVID represents a heterogeneous syndrome rather than a single disease entity. Future progress will heavily depend on efforts to harmonize phenotyping, potentially through stratifying patients into more homogeneous subgroups based on detailed symptom clusters and objective clinical measures. This approach will allow for a more robust and replicable identification of genetic variants that contribute to the diverse manifestations of Long COVID. Strengths and limitations Our study has several strengths that enhance the reliability of our findings. First, we use a study specifically designed to detect LC symptoms within the UK Biobank discovery cohort, providing a robust phenotype for LC. Second, we separately examine LC and PACS-CVD, being, to our knowledge, the first study to date to explore the key genetic variants associated with these two outcomes independently. Third, we validate the LC results using a cohort created independently from the discovery cohort. There are some limitations that must be considered. First, patient reported outcomes (PROs) are inherently subjective, which may result in misclassification of LC cases due to variability in symptom reporting. Additionally, the self-reported nature of the questionnaire can introduce recall bias, as participants may not accurately remember the symptoms or their length. Second, PACS-CVD definition relies on electronic medical records captured during hospitalisation, likely leading to underestimation of patients with milder complications. Third, the suggestive significance threshold (P < 5×10⁻⁶) is less stringent than the conventional genome-wide significance level, and hence more likely to identify spurious associations. Further validation of our findings is therefore warranted. Lastly, the predominance of variants in non-coding regions underscores the need for functional studies to determine how these variants regulate gene expression. Conclusion Our study offers new genetics insights into the pathophysiology of both Long COVID and PACS-CVD highlighting several potential therapeutic pathways for these two recently identified medical conditions. Methods Study design We used UKBB data to perform two GWAS on LC and PACS-CVD, separately. Additionally, separate GWAS for different LC subtypes were performed (Supplementary Figure 4). Data sources UK Biobank The UKBB data is a large-scale, population-based cohort study of over 500,000 participants aged 40-69 years at recruitment (2006-2010) 52 . The UKBB contains detailed information on sociodemographic, lifestyle, and measurements, including genotyping data 53 . Follow-up is conducted via linkage to Hospital Episodes Statistics (HES) and primary care data. For this study, we used diagnostic data from HES (1998-October 2022) and confirmed PCR COVID-19 test results from Public Health England’s Second-Generation Surveillance System 54 . This linkage included data from England (2020-September 2022), Scotland (2020-November 2022), and Wales (2020-December 2022). Health and well-being online questionnaire The UKBB conducted an online survey to collect patient-reported data on LC related symptoms during the pandemic. Over 200,000 participants completed the questionnaire between June 2022 to May 2023, which included 45 questions. More details about the survey design can be found in the online document: https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2500. COVID-19 serology study waves 1-6 Between May and November 2020, approximately 10,000 UKBB participants were enrolled in a longitudinal serology study to assess the extent of SARS-CoV-2 infection through monthly antibody IgG testing and symptom reporting. More details can be found in the online document: https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=4400. Genotyping and imputation Genotyping in UKBB was performed using custom Affymetrix arrays (UK BiLEVE and UK Biobank Axiom), covering over 700,000 autosomal SNPs. Rigorous quality control procedures were applied and are detailed explained in the original publication 53 . Imputation was conducted using a combined reference panel from the Haplotype Reference Consortium (HR), UK10K, and 1000 Genomes Phase 2, resulting in ~93 million imputed autosomal variants. Phenotype definitions Participants with sex chromosome aneuploidy, heterozygosity, and different sex and genetic sex registered, were excluded from all cohorts to avoid confounding effects. Long COVID discovery cohort Our LC discovery cohort included participants who had completed the Health and Well-being online questionnaire (~200,000 individuals), had a valid linkage to COVID-19 surveillance data, and tested positive for SARS-CoV-2 between one year to 30 days prior to completing the survey. For individuals with multiple infections, only the most recent infection prior to survey completion was used in the analysis. LC symptoms were identified by mapping symptoms reported in the survey to the World Health Organisation (WHO) Delphi consensus list (Supplementary Table 1) 55 . Participants who did not answer symptom-related questions or reported pre-existing symptoms were excluded. See Supplementary Note 1 for more details. LC cases were defined as individuals experiencing at least one WHO-listed symptom persisting beyond 30 days to one year after infection. PACS-CVD cohort All UKBB participants (~500,000) with a positive PCR-confirmed SARS-CoV-2 infection and no PACS-CVD-related diagnosis within one year prior to or 30 days following infection were included. Cases were defined as participants with a PACS-CVD diagnosis between 30 days and one-year post-infection. Controls had no such diagnosis during this period. For individuals with multiple infections, only the earliest was considered for the analysis. PACS-CVD associated diagnoses were selected based on clinical knowledge and prior literature 56 . Supplementary Table 2 provides a list of ICD-10 codes used in this study. Genome-wide association analysis GWAS analysis for LC and PACS-CVD were conducted using REGENIE (v3.3) 57 , which is a machine learning model that employs a two-step regression framework: first, a whole-genome regression model is fitted using a subset of the total available genetic markers, and then a larger set of markers are tested for association conditioned on the step 1 regression model. All models were adjusted for age at COVID-19 infection, age 2 , sex, age*sex, genetic batch, and the first ten genetic principal components. Case-control imbalance was addressed using Firth correction. Genotype calls pre-analysis quality control was done using PLINK2 58 . Variants in regions with extended linkage disequilibrium (Supplementary Table 3) 59 , with missing genotype data, with Hardy-Weinberg equilibrium ( P < 1 × 10 −6 ) and with a minor allele frequency smaller than 1% were excluded. Pre-analysis quality control for imputed variants included removing duplicated SNPs, keeping only the first instance. Post-analysis quality control was applied to the GWAS results. Only biallelic alleles were kept, as well as those variants with an INFO score higher than 0.8 and with minor allele frequency higher than 1%. Statistically significant associations were defined using two p -value thresholds: a genome-wide significance (a p -value ≤ 5 × 10 −8 ) and a suggestive significance ( p -value ≤ 5 × 10 −6 ) when no genome-wide significant variant was found. Odds Ratios (ORs) were calculated for effect size estimates. Genomic risk loci were annotated using FUMA 60 and defined based on those SNPs that surpass the given p -value threshold, that are not in linkage disequilibrium (r 2 ≤ 0.1), and that are separated by at least 250kb from each other. We used positional mapping (window 10 kb) to map SNPs to genes. Cohort curation was conducted in R software (version 4.1). Main packages used included dplyr 61 (version 1.1.4), and ggplot2 (version 3.5.1). UK Biobank RAP platform was used to run the GWAS, using REGENIE 57 (version 3.3) and PLINK2 58 (version 1.1.1). FUMA 60 (version 1.7.0) was used to detect independent SNPs. Validation of Long COVID associations To validate the findings from the LC discovery cohort, we tested the top lead independent SNPs in an independent LC validation cohort drawn from the UK Biobank COVID-19 serology study (waves 1-6). This cohort consisted of participants that had participated in the COVID-19 serology study (waves 1-6), with a complete antibody test result and date data across the six waves, and with at least a positive serology test result. Cases were defined as those participants that reported any symptom beyond 30 days after testing positive, whereas those that did not report any symptom were defined as controls. We used genotype calls data for the 1st step of the REGENIE method. Subsequently, in the 2nd step, we exclusively tested the top lead SNPs of each genomic loci obtained in the discovery analysis. Variants with the OR from the main analysis and the validated OR(OR v ) pointing to the same direction (OR and OR v , both being >1 or 0.05 were partially validated. Variants with OR in opposite directions were not validated. Long COVID subtypes exploration We performed an additional analysis exploring four different subtypes of LC, defined based on our previous research 62 : ENT, respiratory and chest, neurological, and fatigue symptoms. These cohorts were applied with a similar inclusion criterion as in the discovery cohort (see Supplementary Note 2). GWAS was performed the same way as for the discovery cohort, with additionally adjusting for the first 20 principal components instead of the first 10, to better capture potential population stratification. Declarations Data availability UK Biobank patient-level data can be accessed by applying for access at http://ukbiobank.ac.uk/register-apply/. All participants provided informed written consent to take part in the study. Ethics approval for the UK Biobank was granted by the North West Multi-Centre Research Ethics Committee in 2006 and was updated regularly after that (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics). This study was conducted after approval by UK Biobank under application reference 151425. Code availability All analytic code is publicly available at oxford-pharmacoepi/GenomeWideAssociationStudies_LC_PACS: GenomeWideAssociationStudies_LC_PACS. Competing interests DPA research group from the University of Oxford has received research grants from the European Medicines Agency, from the Innovative Medicines Initiative, from Gilead Science, and from UCB Biopharma. S.I., J.J.W., and Y.L. are employees of Gilead Sciences and may own stock in the company. K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The remaining authors declare no competing interests. Acknowledgements This study was conducted after approval by the UK Biobank under application reference 151425. This work uses data provided by patients and collected by the NHS as part of their care and support. This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (research which commenced between 1st October 2020–31st March 2021 grant ref MC_PC_20029; 1st April 2021–30th September 2022 grant ref MC_PC_20058). The research was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC) and by Gilead Sciences, Inc. DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health and Care Research or the Department of Health. References Al-Aly Z, Topol E. Solving the puzzle of Long Covid. Science. 2024;383(6685):830-832. Al-Aly Z, Davis H, McCorkell L, et al. Long COVID science, research and policy. Nat Med. 2024;30(8):2148-2164. Peluso MJ, Deeks SG. Mechanisms of long COVID and the path toward therapeutics. Cell. 2024;187(20):5500-5529. Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601-615. Tsampasian V, Elghazaly H, Chattopadhyay R, et al. Risk Factors Associated With Post-COVID-19 Condition: A Systematic Review and Meta-analysis. JAMA Intern Med. 2023;183(6):566-580. Lammi V, Nakanishi T, Jones SE, et al. 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Long-range LD can confound genome scans in admixed populations. Am J Hum Genet. 2008;83(1):132-135; author reply 135-139. Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. Wickham H FR, Henry L, Müller K, Vaughan D. dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://github.com/tidyverse/dplyr, https://dplyr.tidyverse.org. . 2023. Wang Y, Alcalde-Herraiz M, Guell KL, et al. Refinement of post-COVID condition core symptoms, subtypes, determinants, and health impacts: a cohort study integrating real-world data and patient-reported outcomes. EBioMedicine. 2025;111:105493. Tables Table 1. Baseline characteristics of the long COVID (discovery) and PACS-CVD cohorts. Variables Level Long COVID discovery cohort Long COVID validation cohort PACS-CVD N - 8,469 682 105,174 Overall (%) Cases 2,701 (31.9) 244 (35.8) 1,885 (1.8) Controls 5,768 (68.1) 438 (64.2) 103,289 (98.2) Age (mean (SD)) - 66.49 (7.34) 64.82 (7.81) 67.37 (8.20) Sex (%) Female 4,451 (52.6) 352 (51.6) 58,401 (55.5) Male 4,018 (47.4) 330 (48.4) 46,773 (44.5) Body mass index (mean (SD)) - 26.03 (4.02) 26.81 (4.41) 27.34 (4.73) Index of multiple deprivation (mean (SD)) - 13.85 (11.09) 19.60 (14.40) 16.83 (13.49) Genetic ethnic background (%) Caucasian 7,217 (85.2) 409 (60.0) 88,610 (84.3) Other 1,252 (14.8) 273 (40) 16,564 (15.7) Table 2. Long COVID GWAS results. Note: CHR = Chromosome; SNP = Single Nucleotide Polymorphism; EA = Effect Allele; EAF = Effect Allele Frequency; OR = Odds Ratio; SE = Standard Error. CHR SNP EA EAF OR SE p- value Function Gene Gene Type/Details Reported traits Validation results 2 rs7590539 G 0.08 0.73 0.07 3.6e-06 intergenic RN7SKP93 - MGAT5 RN7SKP93: Pseudogene MGAT5: Protein coding gene RN7SKP93: Height, schizophrenia and cholesterol levels MGAT5: Height, schizophrenia, alkaline phosphatase levels Not validated 3 rs375087201 G 0.83 1.26 0.05 1.9e-06 intergenic VENTXP4 - AC099754.1 VENTXP4: Pseudogene AC099754.1: Information not specified VENTXP4: Creatinine change after HIV infection AC099754.1: None reported Partially validated 4 rs34746824 C 0.29 0.83 0.04 1.9e-06 intergenic RP11-206P5.2 - RNU7-126P RP11-206P5.2: Information not specified RNU7-126P: snRNA RP11-206P5.2: None reported RNU7-126P: COVID-19 hospitalisation and severity Partially validated 5 rs147065000 G 0.85 0.79 0.05 1.5e-06 UTR3 ZNF454 Protein coding gene Cognitive function and major depressive disorder interaction Partially validated 6 rs74874798 A 0.97 1.80 0.12 5.2e-07 intergenic ADH5P4 - NUFIP1P ADH5P4: Pseudogene NUFIP1P: Pseudogene ADH5P4: Depression or major depressive disorder, type 2 diabetes, psychotic disorders NUFIP1P: None reported Not validated 7 rs2106435 A 0.74 1.21 0.04 1.8e-06 intergenic ZNF804B - AC002383.2 ZNF804B: Protein coding gene AC002383.2: Information not specified ZNF804B: Smoking, IgG glycosylation, and ovarian cancer AC002383.2: None reported Not validated 8 rs12335232 G 0.09 1.32 0.06 4.9e-06 intronic ADCY8 Protein coding gene Memory decline, COVID-19 infection, COVID-19 severity Fully validated 9 rs10966605 T 0.97 0.62 0.10 1.3e-06 intergenic RMRPP5 - RN7SKP120 RMRPP5: Ribozyme RN7SKP120: misc_RNA RMRPP5: Testosterone levels RN7SKP120: Depression, testosterone levels, bronchodilator Partially validated 9 rs147950355 G 0.99 2.24 0.17 1.2e-06 intronic TMEM246 lncRNA Alkaline phosphatase levels, liver enzyme levels Partially validated 10 rs9415008 A 0.15 1.25 0.05 3.2e-06 intergenic PPA1 - NPFFR1 PPA1: Protein coding gene NPFFR1: Protein coding gene PPA1: Protein phosphatase levels NPFFR1: Inorganic pyrophosphatase levels, decline rate in late mild cognitive impairment Partially validated 11 rs4551739 C 0.87 0.78 0.05 1.4e-06 intronic NAV2 Protein coding gene Atrial fibrillation, brain shape, obesity, smoking initiation Partially validated 12 rs77158180 T 0.95 0.68 0.08 2.8e-06 intronic LIN7A Protein coding gene Creatinine change after HIV infection, type 2 diabetes Not validated 12 rs2702217 G 0.7 0.83 0.04 5.6e-07 intronic GLT1D1 Protein coding gene Aging, response to zileuton treatment in asthma Partially validated 14 rs11621087 C 0.57 0.84 0.03 1.0e-06 intronic PTGR2 – RP5-1021I20.4 PTGR2: Protein coding gene RP5-1021I20.4: Information not specified PTGR2: Ectonucleoside triphosphate diphosphohydrolase 5 levels RP5-1021I20.4: None reported Partially validated 15 rs78215228 G 0.98 0.59 0.11 1.5e-06 intronic CHRNA7 Protein coding gene Acute kidney injury, COVID-19 infection Partially validated Table 3. PACS-CVD GWAS results. Note: CHR = Chromosome; SNP = Single Nucleotide Polymorphism; EA = Effect Allele; EAF = Effect Allele Frequency; OR = Odds Ratio; SE = Standard Error. CHR SNP EA EAF OR SE p -value Function Gene Gene Type/Details Reported traits 1 rs11582898 T 0.96 1.66 0.10 1.0e-06 intergenic RP11-115A15.4 - RP11-115A15.2 RP11-115A15.4: Information not specified RP11-115A15.2: Information not specified RP11-115A15.4: None reported RP11-115A15.2: None reported 3 rs7643274 A 0.53 0.84 0.03 4.0e-07 intergenic TRIM42 – RP11-691G17.1 TRIM42: Protein coding gene RP11-691G17.1: Information not specified TRIM42: Bone mineral density levels, bipolar disorder RP11-691G17.1: None reported 4 rs10212904 G 0.86 0.80 0.05 2.4e-06 ncRNA_intronic AC108142.1 Information not specified None reported 6 rs147544694 C 0.97 1.84 0.12 2.3e-07 intergenic SAYSD1 – KCNK5 SAYSD1: Protein coding gene KCNK5: Protein coding gene SAYSD1: BMI, coronary artery disease, myocardial infarction, diastolic blood pressure, type 2 diabetes KCNK5: Coronary artery disease, myocardial infarction, diastolic blood pressure, urate levels, LDL cholesterol levels 7 rs147084175 C 0.99 0.56 0.13 4.8e-06 intronic CCDC126 Protein coding gene Serum alkaline phosphatase levels 7 rs818468 T 0.72 1.19 0.04 3.6e-06 intronic PUS7 Protein coding gene ADHD, autism, bipolar disorder 9 rs117559490 G 0.98 0.62 0.10 1.9e-06 intergenic RP11-440G5.2 - ROR2 RP11-440G5.2: Information not specified ROR2: Protein coding gene RP11-440G5.2: None reported ROR2: Height, body mass index, glaucoma, tyrosine-protein kinase transmembrane receptor ROR2 levels, aspartate aminotransferase levels, COVID-19 infection and severity 10 rs2486033 A 0.2 1.21 0.04 3.6e-06 intergenic RP11-282I1.1 - RP11-282I1.2 RP11-282I1.1: Information not specified RP11-282I1.2: Information not specified RP11-282I1.1: None reported RP11-282I1.2: None reported 11 rs7120231 C 0.99 0.55 0.13 4.3e-06 intergenic NELL1 – CTD-2019O4.1 NELL1: Protein coding gene CTD-2019O4.1: Information not specified NELL1: Protein kinase C, type 2 diabetes, opioid addiction CTD-2019O4.1: None reported 13 rs17554072 A 0.94 1.51 0.08 1.1e-07 intergenic FLT1 – EIF4A1P7 FLT1: Protein coding gene EIF4A1P7: Pseudogene FLT1: Coronary artery disease, triglycerides, myocardial infarction EIF4A1P7: None reported 13 rs61947186 T 0.96 0.69 0.08 1.4e-06 ncRNA_intronic RP11-16D22.2 Information not specified None reported 19 rs1558138 C 0.51 0.84 0.03 2.0e-07 intronic PTPRS Protein coding gene Thyroid stimulating hormone levels, type 2 diabetes 21 rs8126522 T 0.98 0.60 0.11 1.9e-06 intronic BACH1 Protein coding gene Height, alanine aminotransferase levels, liver enzyme levels, body mass index 21 rs56067353 T 0.97 1.68 0.11 2.1e-06 intronic RUNX1 Protein coding gene Rheumatoid arthritis, asthma, attempted suicide, haemoglobin concentration Additional Declarations Yes there is potential Competing Interest. DPA research group from the University of Oxford has received research grants from the European Medicines Agency, from the Innovative Medicines Initiative, from Gilead Science, and from UCB Biopharma. S.I., J.J.W., and Y.L. are employees of Gilead Sciences and may own stock in the company. K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The remaining authors declare no competing interests. Supplementary Files SupplementaryInformation.docx Supplementary Information 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. We do this by developing innovative software and high quality services for the global research community. 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11:32:06","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":171560,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/d15ad73ad228b8a651761c3d.html"},{"id":92256110,"identity":"eaab0bbd-bc35-4bad-917a-7200c7619a24","added_by":"auto","created_at":"2025-09-26 11:32:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":727508,"visible":true,"origin":"","legend":"\u003cp\u003eGenome wide association Manhattan plots. (A) Long COVID discovery cohort findings. (B) PACS-CVD cohort findings.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/dc639064178bd32dca808cec.png"},{"id":92256124,"identity":"fb178b9f-ec88-4863-bf94-e086e99a5a5b","added_by":"auto","created_at":"2025-09-26 11:32:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":447310,"visible":true,"origin":"","legend":"\u003cp\u003eValidation results.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/7ed02685befaf7b770fea920.png"},{"id":92256116,"identity":"8d3b23ea-557f-4553-b647-04486e8a4e97","added_by":"auto","created_at":"2025-09-26 11:32:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":617462,"visible":true,"origin":"","legend":"\u003cp\u003eRegional plots of the fully/partially validated loci for Long COVID.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/9114a364d9787727d27788b1.png"},{"id":94472972,"identity":"45dab0f5-60c7-4ea6-860b-10357a96a637","added_by":"auto","created_at":"2025-10-27 15:42:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2700204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/f4053ce8-1dda-4af0-aa3e-12f5fe6edc7a.pdf"},{"id":92256120,"identity":"23f4ee33-516c-477b-acaa-ec75fa8df0b5","added_by":"auto","created_at":"2025-09-26 11:32:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1494775,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7676837/v1/77abebc9f5ba5a70d8993262.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential Competing Interest.\nDPA research group from the University of Oxford has received research grants from the European Medicines Agency, from the Innovative Medicines Initiative, from Gilead Science, and from UCB Biopharma. S.I., J.J.W., and Y.L. are employees of Gilead Sciences and may own stock in the company. K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The remaining authors declare no competing interests.","formattedTitle":"Genome-wide association studies of Long COVID and post-acute complications of SARS-CoV-2 in the UK Biobank Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePost-COVID-19 conditions (PCC) are defined as long-term post-acute health consequences caused by a COVID-19 infection\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As of August 2024, the global cumulative incidence of PCC was estimated to be 400\u0026nbsp;million individuals\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, resulting in a substantial burden on healthcare systems worldwide.\u003c/p\u003e\u003cp\u003ePCC can be distinguished between Long COVID (LC) and post-acute cardiovascular complications of SARS-CoV-2 infection (PACS-CVD)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While LC is characterised by the persistence or onset of COVID-19 related symptoms beyond the COVID-19 infection acute phase, PACS-CVD typically refer to more severe thromboembolic or cardiovascular complications within the same time frame.\u003c/p\u003e\u003cp\u003eWhile clinical and epidemiological studies have identified the risk factors and symptoms of Long COVID and PACS-CVD\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, we still lack a clear understanding of the biological mechanisms behind these conditions. Genetic predisposition is likely to play a role in individual susceptibility, as recent studies have shown\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In this context, genome-wide association studies (GWAS)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e can be a valuable approach to uncover common genetic variants linked to LC and PACS-CVD, potentially revealing the pathophysiological pathways involved.\u003c/p\u003e\u003cp\u003eIn this study, we used UK Biobank (UKBB) data to conduct two separate GWAS: one focusing on Long COVID and another on PACS-CVD. By investigating these two outcomes in parallel, we aimed to identify genetic variants associated with the susceptibility of each condition, and to explore whether they share common or distinct genetic determinants.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLong COVID discovery cohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOf the 275,234 UK Biobank participants with valid linkage to COVID-19 surveillance data, 46,793 individuals had both a confirmed positive test and complete responses to the Health and Well-being online questionnaire. Out of those, 8,469 participants fulfilled the selection criteria, with 5,768 (68%) reporting at least one symptom persisting beyond 30 days after infection (cases), and 2,701 (32%) without reporting any symptom beyond 30 days after infection (controls) (Supplementary Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLong COVID validation cohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOut of the 8,723 that participated in the COVID-19 serology study (waves 1-6) and had all the antibody tests performed, 704 had reported at least one positive result and 682 fulfilled our selection criteria. Of those, 244 (36%) were classified as cases, whereas 438 (64%) were classified as controls.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePACS-CVD cohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e115,007 UK Biobank participants had a valid linkage to hospital episode statistics (HES) data and to COVID-19 surveillance data, and a positive PCR test. Of these, 105,174 fulfilled the selection criteria to be part of the PACS-CVD base cohort. A total of 1,885 (2%) had at least one PACS-CVD diagnosis beyond 30 days after infection and were therefore classified as PACS-CVD cases, whereas 103,289 (98%) were classified as PACS-CVD controls (Supplementary Figure 2).\u003c/p\u003e\n\u003cp\u003eBaseline characteristics of the study cohorts are presented in Table 1. The mean age was 66 for LC participants, 65 for LC validation cohort, and 67 years for PACS-CVD participants. In all cohorts, female participants outnumbered male participants. Indexes of multiple deprivation (IMD) were 14, 20, and 17, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association (GWAS) analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the 784,256 variants loaded from genotype calls, 529,562 and 472,043 passed the quality control in the LC and PACS-CVD GWAS, respectively, and were used to build a whole-genome regression model in REGENIE Step 1. From the 93,095,623 imputed variants, 92,775,302\u0026nbsp;passed the initial quality control (kept the first instance when there are duplicated variants). Post-quality control included up to 8.5 million variants in both analyses.\u003c/p\u003e\n\u003cp\u003eAlthough no SNPs reached genome-wide significance\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e) in the LC GWAS, 15 genomic loci achieved suggestive significance threshold (\u003cem\u003ep-\u003c/em\u003evalue\u0026thinsp;\u0026le;\u0026thinsp;5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) (Figure 1A, Table 2). Of these, one variant -rs12335232 in the \u003cem\u003eADCY8\u003c/em\u003e gene- was fully validated\u0026nbsp;(Figure 2), with the G allele associated with an increased risk of LC\u0026nbsp;(OR = 1.32, 95%CI = 1.17\u0026ndash;1.49). Ten additional variants were partially validated. These included rs375087201 (between \u003cem\u003eVENTXP4\u003c/em\u003e and \u003cem\u003eAC099754.1\u003c/em\u003e), with allele G associated with an increased risk of LC (OR = 1.26, 95%CI = 1.15\u0026ndash;1.39);\u0026nbsp;rs34746824 (between \u003cem\u003eRP11\u003c/em\u003e-\u003cem\u003e206P5.2\u003c/em\u003e and \u003cem\u003eRNU7-126P\u003c/em\u003e), with allele C associated with a protective effect (OR = 0.83, 95%CI = 0.77\u0026ndash;0.90); rs147065000 (within \u003cem\u003eZNF454\u003c/em\u003e), with its allele G associated also protective (OR = 0.79, 95%CI = 0.72\u0026ndash;0.87); rs10966605 (between \u003cem\u003eRMRPP5\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRN7SKP120\u003c/em\u003e), with its allele T associated with a decreased risk (OR = 0.62, 95%CI = 0.51\u0026ndash;0.75); rs147950355 (within \u003cem\u003eTMEM246\u003c/em\u003e), with its allele G linked with an increased risk of LC (OR = 2.24, 95%CI = 1.62\u0026ndash;3.11); rs9415008 (between \u003cem\u003ePPA1\u003c/em\u003e and \u003cem\u003eNPFFR1\u003c/em\u003e), with its allele A associated with an increased risk (OR = 1.25, 95%CI = 1.14\u0026ndash;1.37); rs4551739, within gene \u003cem\u003eNAV2\u003c/em\u003e, with its allele C associated with a protective effect (OR = 0.78, 95%CI = 0.70\u0026ndash;0.86); rs2702217 (gene \u003cem\u003eGLT1D1\u003c/em\u003e), with its allele G linked to decreased risk (OR = 0.83, 95%CI = 0.77\u0026ndash;0.89); rs11621087 (between \u003cem\u003ePTGR2\u003c/em\u003e and \u003cem\u003eRP5-1021I20.4\u003c/em\u003e), with its allele C associated with a decreased risk (OR = 0.84, 95%CI = 0.79\u0026ndash;0.90); rs78215228 (\u003cem\u003eCHRNA7\u003c/em\u003e), with its allele G associated with a decreased risk of LC (OR = 0.59, 95%CI = 0.47\u0026ndash;0.73).\u0026nbsp;The remaining 4 genomic loci were not validated. Regional plots of all the genomic loci are shown in Figure 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFourteen SNPs were identified with the suggestive significance threshold to be associated with PACS-CVD susceptibility (Figure 1B, Table 3). In chromosome 1, rs11582898 (intergenic, between \u003cem\u003eRP11-115A15.4\u003c/em\u003e and \u003cem\u003eRP11-115A15.2\u003c/em\u003e) had the T allele associated with an increased risk of LC (OR = 1.66, 95%CI = 1.35\u0026ndash;2.03). In chromosome 3, rs7643274 (intergenic, between \u003cem\u003eTRIM42\u003c/em\u003e and \u003cem\u003eRP11-691G17.1\u003c/em\u003e) showed a protective association, with the A allele linked to reduced risk (OR = 0.84, 95%CI =0.79\u0026ndash;0.90). On chromosome 4, rs10212904 (ncRNA_intronic in \u003cem\u003eAC108142.1\u003c/em\u003e) also showed a protective effect (G allele, OR = 0.80, 95%CI = 0.73\u0026ndash;0.88). On chromosome 6, rs147544694 (intergenic, between \u003cem\u003eSAYSD1\u003c/em\u003e and \u003cem\u003eKCNK5\u003c/em\u003e) showed a strong association with increased risk (C allele, OR = 1.84, 1.46\u0026ndash;2.32). On chromosome 7, two variants reached the threshold: rs147084175 (intronic in \u003cem\u003eCCDC126\u003c/em\u003e), with the C allele associated with a protective effect (OR = 0.56, 95%CI: 0.44\u0026ndash;0.72), and rs818468 (intronic in PUS7), where the T allele increased risk (OR = 1.19, 95% CI: 1.11\u0026ndash;1.28). On chromosome 9, rs117559490 (intergenic, between \u003cem\u003eRP11-440G5.2\u003c/em\u003e and \u003cem\u003eROR2\u003c/em\u003e) showed a protective association (G allele, OR = 0.62, 95%CI: 0.51\u0026ndash;0.75). On chromosome 10, rs2486033 (intergenic, between \u003cem\u003eRP11-282I1.1\u003c/em\u003e and \u003cem\u003eRP11-282I1.2\u003c/em\u003e) had the A allele associated with increased risk (OR = 1.21, 95% CI: 1.11\u0026ndash;1.31). On chromosome 11, rs7120231 (intergenic, between \u003cem\u003eNELL1\u003c/em\u003e and \u003cem\u003eCTD-2019O4.1\u003c/em\u003e) showed a protective effect (C allele, OR = 0.55, 95% CI: 0.43\u0026ndash;0.71). On chromosome 13, rs17554072 (intergenic, between \u003cem\u003eFLT1\u003c/em\u003e and \u003cem\u003eEIF4A1P7\u003c/em\u003e) showed an increased risk (A allele, OR = 1.51, 95% CI: 1.30\u0026ndash;1.76), while rs61947186 (ncRNA_intronic in \u003cem\u003eRP11-16D22.2\u003c/em\u003e) showed a protective effect (T allele, OR = 0.69, 95%CI = 0.59\u0026ndash;0.80). On chromosome 19, rs1558138 (intronic in \u003cem\u003ePTPRS\u003c/em\u003e) was protective (C allele, OR = 0.84, 95% CI: 0.79\u0026ndash;0.90). Finally, two variants were located on chromosome 21: rs8126522 (intronic in \u003cem\u003eBACH1\u003c/em\u003e), where the T allele was associated with a protective effect (OR = 0.60, 95% CI: 0.48\u0026ndash;0.74), and rs56067353 (intronic in \u003cem\u003eRUNX1\u003c/em\u003e), where the T allele was associated with increased risk (OR = 1.68, 95% CI: 1.35\u0026ndash;2.08).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong COVID subtypes analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall Long COVID cohort for the subtype analysis had 20,472 participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the respiratory and chest symptoms, there were 477 (2%) cases and 19,996 (98%) controls. 10 genomic loci reached the suggestive significance threshold (Supplementary Figure 3A, Supplementary Table 4). For the ENT (ear, nose and throat) subtype, we found 633 (3%) cases and 19,839 (97%) controls. 12 genomic loci reached the suggestive significance threshold (Supplementary Figure 3B, Supplementary Table 5). For the neurological subtype, we found 1,078 (5%) cases and 19,394 (95%) controls. 10 genomic loci reached the suggestive significance threshold (Supplementary Figure 3C, Supplementary Table 6). For the fatigue subtype, we found 3,161 (15%) cases and 17,311 (85%) controls. 14 genomic loci reached the suggestive significance threshold (Supplementary Figure 3D, Supplementary Table 7).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of k\u003c/strong\u003e\u003cstrong\u003eey\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estudy f\u003c/strong\u003e\u003cstrong\u003eindings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur genome-wide association study of Long COVID in the UK Biobank identified 15 lead genetic variants that reached suggestive statistical significance (\u003cem\u003ep\u003c/em\u003e-value \u0026lt; 5\u0026times;10⁻⁶) in the discovery cohort. Among these, eleven variants (73.3%) demonstrated robustness during validation analysis, with one variant (rs12335232 in the \u003cem\u003eADCY8\u003c/em\u003e gene) achieving full validation and 10 variants showing partial validation in an independent UK Biobank sub-cohort, which used an alternative Long COVID phenotype definition. The high replication rate across different Long COVID phenotype definitions within the UK Biobank population adds confidence to our findings. However, most variants were in genetic positions that do not directly code for proteins (non-coding regions), with seven located between genes, 7 within genes (although intronic), and one in a 3\u0026apos; UTR region (rs147065000 in \u003cem\u003eZNF454\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eImportantly, the suggestive genetic variants identified in our study are predominantly located within or adjacent to genes whose established functions correspond with hypothesized pathogenic mechanisms of Long COVID. For example, three genes containing these variants have been previously linked with COVID-19 outcomes: \u003cem\u003eADCY8\u003c/em\u003e (rs12335232) linked to COVID-19 infection and severity, \u003cem\u003eCHRNA7\u003c/em\u003e (rs78215228) to COVID-19 infection, and the \u003cem\u003eRNU7-126P\u003c/em\u003e (rs34746824) to COVID-19 hospitalization and severity. These findings provide evidence that acute COVID-19 disease, and its persistent symptoms share common biological pathways\u003csup\u003e8-10\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the result of \u003cem\u003eADCY8\u003c/em\u003e gene as a fully validated Long COVID risk locus is important. \u003cem\u003eADCY8\u0026nbsp;\u003c/em\u003eencodes adenylyl cyclase 8, a key enzyme in cAMP signalling that plays crucial roles in brain function, synaptic plasticity, and memory formation\u003csup\u003e11,12\u003c/sup\u003e. Its links to COVID-19 severity and memory decline support the epidemiological studies that show high prevalence of cognitive dysfunction in patients with Long COVID\u003csup\u003e13-15\u003c/sup\u003e. Similarly, \u003cem\u003eCHRNA7\u003c/em\u003e, which is involved in neuroinflammation and immune response\u003csup\u003e16,17\u003c/sup\u003e, suggests a possible connection between severe COVID-19 and lasting neurological symptoms\u003csup\u003e18,19\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, the enrichment of variants near genes associated with neuropsychiatric traits is also consistent with the high prevalence of cognitive impairment, depression, and anxiety in Long COVID patients\u003csup\u003e20\u003c/sup\u003e. This overlap hints that people with a genetic background for mental health conditions might be more likely to experience neurological issues from Long COVID. Of note, the identification of metabolic trait-associated genes (\u003cem\u003eADH5P4, LIN7A, TMEM246\u003c/em\u003e) aligns with emerging evidence of metabolic dysfunction in Long COVID pathophysiology\u003csup\u003e21,22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeparately, we found 14 lead variants with suggestive significance for PACS-CVD. To our best knowledge, this is the first GWAS specifically focused on PACS-CVD. Most of them were near genes that have established roles in cardiovascular disease or its risk factors. For example, \u003cem\u003eSAYSD1\u003c/em\u003e and \u003cem\u003eKCNK5\u003c/em\u003e (rs147544694) linked to coronary artery disease\u003csup\u003e23-26\u003c/sup\u003e, myocardial infarction\u003csup\u003e27-29\u003c/sup\u003e, and blood pressure regulation\u003csup\u003e30-32\u003c/sup\u003e, and \u003cem\u003eFLT1\u003c/em\u003e (rs17554072), associated with coronary artery disease\u003csup\u003e23,24,26,33,34\u003c/sup\u003e and triglycerides\u003csup\u003e28\u003c/sup\u003e. This cardiovascular gene enrichment corroborates observational evidence that SARS-CoV-2 infection may trigger persistent cardiovascular complications and metabolic dysfunction. Similarly, the identification of \u003cem\u003eROR2\u003c/em\u003e (rs117559490), previously associated with COVID-19 infection and severity, also suggests shared pathogenic mechanisms between acute infection and post-acute complications. The presence of variants near neuropsychiatric-associated genes including \u003cem\u003eTRIM42\u003c/em\u003e (bipolar disorder\u003csup\u003e35\u003c/sup\u003e) and \u003cem\u003ePUS7\u003c/em\u003e (ADHD, autism, bipolar disorder\u003csup\u003e36\u003c/sup\u003e) indicates potential overlap with neurological sequelae, with an immune-related gene \u003cem\u003eRUNX1\u003c/em\u003e linking to inflammatory disease such as rheumatoid arthritis\u003csup\u003e37\u003c/sup\u003e and asthma\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOur results in broader context\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA few GWAS have been conducted to identify common genetic variants associated with Long COVID and have revealed several loci of interest. The most significant and consistently highlighted finding in Long COVID genetics is the association with variants near or within the \u003cem\u003eFOXP4\u003c/em\u003e gene (rs9367106-C, \u003cem\u003eP\u003c/em\u003e = 1.76\u0026times;10\u003csup\u003e-10\u003c/sup\u003e). This locus was first identified in a meta-GWAS analysis by the COVID-19 Host Genetics Initiative (HGI)\u003csup\u003e6\u003c/sup\u003e. \u003cem\u003eFOXP4\u003c/em\u003e encodes a transcription factor predominantly found in lung tissue and immune cells, and has previously been connected to lung function, susceptibility to severe acute COVID-19\u003csup\u003e39\u003c/sup\u003e, and lung development\u003csup\u003e40\u003c/sup\u003e. Notably, conditional analyses in this HGI study found that the \u003cem\u003eFOXP4\u003c/em\u003e locus\u0026rsquo;s association with Long COVID remains significant after adjusting for acute COVID-19 hospitalization status, which implies that even people who experience a mild initial infection could be at risk for persistent pulmonary issues if this \u003cem\u003eFOXP4\u003c/em\u003e-related pathway is dysregulated.\u003c/p\u003e\n\u003cp\u003eAnother important genomic region tied to Long COVID is the Human Leukocyte Antigen (HLA) complex. A significant study, which looked at diverse populations and used data from 23andMe with over 53,000 cases and 120,000 controls, found a noteworthy locus at \u003cem\u003eHLA-DQA1\u003c/em\u003e to \u003cem\u003eHLA-DQB1\u003c/em\u003e region\u003csup\u003e41\u003c/sup\u003e. This supports an immune-mediated component in Long COVID pathogenesis, such as autoimmunity or an altered or prolonged immune response to persistent viral antigens or viral debris\u003csup\u003e42\u003c/sup\u003e. This research also showed a connection between the ABO blood group and Long COVID. The ABO locus has previously been implicated in susceptibility to and severity of acute COVID-19 infection, with non-O blood groups often carrying a higher risk for severe outcomes or thromboembolic complications\u003csup\u003e43,44\u003c/sup\u003e. The extended association of the ABO locus with Long COVID suggests that biological mechanisms influenced by blood group antigens may play significant roles in symptom persistence.\u003c/p\u003e\n\u003cp\u003eA recent GWAS in a German cohort reported several genetic regions linked to Long COVID symptoms\u003csup\u003e45\u003c/sup\u003e. The most notable finding was an association with SNP rs10893121 (\u003cem\u003eP\u003c/em\u003e = 2.5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) close to genes (specifically olfactory receptor families 4, 6, and 10) responsible for our sense of smell. Given that impairment of smell and taste represents a pathognomonic feature of both acute COVID-19 and Long COVID, this finding suggests a potential genetic basis for specific sensory dysfunction. Separately, studies focused on acute COVID-19-related anosmia have implicated olfactory-related genes \u003cem\u003eUGT2A1\u003c/em\u003e and \u003cem\u003eUGT2A2\u003c/em\u003e\u003cem\u003e\u003csup\u003e46\u003c/sup\u003e\u003c/em\u003e. These genes, expressed in the olfactory epithelium and involved in odorant metabolism, showed genome-wide significant associations with COVID-19-related loss of smell or taste in acute infection. Recent single-cell analysis shown that \u003cem\u003eUGT2A1\u003c/em\u003e is strongly expressed in sustentacular cells, the primary target of SARS-CoV-2 infection in olfactory tissue\u003csup\u003e47\u003c/sup\u003e. Although these associations focused on acute COVID-19 symptoms rather than Long COVID specifically, the overlapping mechanisms of olfactory dysfunction appeared to be potential shared pathways\u003csup\u003e48\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA critical limitation in Long COVID genetics research is the lack of consistent replication across studies\u003csup\u003e49\u003c/sup\u003e. None of the genetic loci identified in prior studies, including those from our analysis, have achieved genome-wide replication across independent cohorts. For example, even the strongest FOXP4 association in the HGI did not pass statistical significance in the subsequent 23andMe analysis, despite the latter study having over eight times more cases. This pattern may indicate several underlying issues. First, there appears to be substantial heterogeneity in Long COVID phenotypes across different populations (e.g. self-reported symptoms vs clinically validated scores), and different recruitment strategies (e.g. 23andMe\u0026apos;s direct-to-consumer model vs. clinically ascertained cohorts in some HGI studies vs a sub-cohort nested in the UK Biobank population in our study). Second, the underlying definition of LC requires to use patient-reported data, which likely introduces potential misclassification bias. Individual perception of symptoms, willingness to report symptoms, and understanding of \u0026quot;Long COVID\u0026quot; as a diagnostic entity can vary significantly across populations and geographic regions\u003csup\u003e50\u003c/sup\u003e. It hinders the detection of genetic signals unless they exert very strong or universal effects across different Long COVID manifestations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications for future research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, the absence of a clear, widely recognised, and uniformly implemented definition for Long COVID presence a major challenge in genetic research\u003csup\u003e51\u003c/sup\u003e. Current evidence indicated that Long COVID represents a heterogeneous syndrome rather than a single disease entity. Future progress will heavily depend on efforts to harmonize phenotyping, potentially through stratifying patients into more homogeneous subgroups based on detailed symptom clusters and objective clinical measures. This approach will allow for a more robust and replicable identification of genetic variants that contribute to the diverse manifestations of Long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several strengths that enhance the reliability of our findings. First, we use a study specifically designed to detect LC symptoms within the UK Biobank discovery cohort, providing a robust phenotype for LC. Second, we separately examine LC and PACS-CVD, being, to our knowledge, the first study to date to explore the key genetic variants associated with these two outcomes independently. Third, we validate the LC results using a cohort created independently from the discovery cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are some limitations that must be considered. First, patient reported outcomes (PROs) are inherently subjective, which may result in misclassification of LC cases due to variability in symptom reporting. Additionally, the self-reported nature of the questionnaire can introduce recall bias, as participants may not accurately remember the symptoms or their length. Second, PACS-CVD definition relies on electronic medical records captured during hospitalisation, likely leading to underestimation of patients with milder complications. Third, the suggestive significance threshold (P \u0026lt; 5\u0026times;10⁻⁶) is less stringent than the conventional genome-wide significance level, and hence more likely to identify spurious associations. Further validation of our findings is therefore warranted. Lastly, the predominance of variants in non-coding regions underscores the need for functional studies to determine how these variants regulate gene expression.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study offers new genetics insights into the pathophysiology of both Long COVID and PACS-CVD highlighting several potential therapeutic pathways for these two recently identified medical conditions.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used UKBB data to perform two GWAS on LC and PACS-CVD, separately. Additionally, separate GWAS for different LC subtypes were performed (Supplementary Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUK Biobank\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe UKBB data is a large-scale, population-based cohort study of over 500,000 participants aged 40-69 years at recruitment (2006-2010)\u003csup\u003e52\u003c/sup\u003e. The UKBB contains detailed information on sociodemographic, lifestyle, and measurements, including genotyping data\u003csup\u003e53\u003c/sup\u003e. Follow-up is conducted via linkage to Hospital Episodes Statistics (HES) and primary care data. For this study, we used diagnostic data from HES (1998-October 2022) and confirmed PCR COVID-19 test results from Public Health England\u0026rsquo;s Second-Generation Surveillance System\u003csup\u003e54\u003c/sup\u003e. This linkage included data from England (2020-September 2022), Scotland (2020-November 2022), and Wales (2020-December 2022).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHealth and well-being online questionnaire\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe UKBB conducted an online survey to collect patient-reported data on LC related symptoms during the pandemic. Over 200,000 participants completed the questionnaire between June 2022 to May 2023, which included 45 questions. More details about the survey design can be found in the online document: https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=2500.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOVID-19 serology study waves 1-6\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween May and November 2020, approximately 10,000 UKBB participants were enrolled in a longitudinal serology study to assess the extent of SARS-CoV-2 infection through monthly antibody IgG testing and symptom reporting. More details can be found in the online document: https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=4400. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGenotyping and imputation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGenotyping in UKBB was performed using custom Affymetrix arrays (UK BiLEVE and UK Biobank Axiom), covering over 700,000 autosomal SNPs. Rigorous quality control procedures were applied and are detailed explained in the original publication\u003csup\u003e53\u003c/sup\u003e. Imputation was conducted using a combined reference panel from the Haplotype Reference Consortium (HR), UK10K, and 1000 Genomes Phase 2, resulting in ~93 million imputed autosomal variants. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhenotype definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants with sex chromosome aneuploidy, heterozygosity, and different sex and genetic sex registered, were excluded from all cohorts to avoid confounding effects.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLong COVID discovery cohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur LC discovery cohort included participants who had completed the Health and Well-being online questionnaire (~200,000 individuals), had a valid linkage to COVID-19 surveillance data, and tested positive for SARS-CoV-2 between one year to 30 days prior to completing the survey. For individuals with multiple infections, only the most recent infection prior to survey completion was used in the analysis. \u003c/p\u003e\n\u003cp\u003eLC symptoms were identified by mapping symptoms reported in the survey to the World Health Organisation (WHO) Delphi consensus list (Supplementary Table 1)\u003csup\u003e55\u003c/sup\u003e. Participants who did not answer symptom-related questions or reported pre-existing symptoms were excluded. See Supplementary Note 1 for more details. LC cases were defined as individuals experiencing at least one WHO-listed symptom persisting beyond 30 days to one year after infection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePACS-CVD cohort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll UKBB participants (~500,000) with a positive PCR-confirmed SARS-CoV-2 infection and no PACS-CVD-related diagnosis within one year prior to or 30 days following infection were included. Cases were defined as participants with a PACS-CVD diagnosis between 30 days and one-year post-infection. Controls had no such diagnosis during this period. For individuals with multiple infections, only the earliest was considered for the analysis. PACS-CVD associated diagnoses were selected based on clinical knowledge and prior literature\u003csup\u003e56\u003c/sup\u003e. Supplementary Table 2 provides a list of ICD-10 codes used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide association analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS analysis for LC and PACS-CVD were conducted using REGENIE (v3.3)\u003csup\u003e57\u003c/sup\u003e, which is a machine learning model that employs a two-step regression framework: first, a whole-genome regression model is fitted using a subset of the total available genetic markers, and then a larger set of markers are tested for association conditioned on the step 1 regression model.\u003c/p\u003e\n\u003cp\u003eAll models were adjusted for age at COVID-19 infection, age\u003csup\u003e2\u003c/sup\u003e, sex, age*sex, genetic batch, and the first ten genetic principal components. Case-control imbalance was addressed using Firth correction. Genotype calls pre-analysis quality control was done using PLINK2\u003csup\u003e58\u003c/sup\u003e. Variants in regions with extended linkage disequilibrium (Supplementary Table 3)\u003csup\u003e59\u003c/sup\u003e, with missing genotype data, with Hardy-Weinberg equilibrium (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) and with a minor allele frequency smaller than 1% were excluded. Pre-analysis quality control for imputed variants included removing duplicated SNPs, keeping only the first instance. Post-analysis quality control was applied to the GWAS results. Only biallelic alleles were kept, as well as those variants with an INFO score higher than 0.8 and with minor allele frequency higher than 1%.\u003c/p\u003e\n\u003cp\u003eStatistically significant associations were defined using two \u003cem\u003ep\u003c/em\u003e-value thresholds: a genome-wide significance (a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;8\u003c/sup\u003e) and a suggestive significance (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;5\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;6\u003c/sup\u003e) when no genome-wide significant variant was found. Odds Ratios (ORs) were calculated for effect size estimates. Genomic risk loci were annotated using FUMA\u003csup\u003e60\u003c/sup\u003e and defined based on those SNPs that surpass the given \u003cem\u003ep\u003c/em\u003e-value threshold, that are not in linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026le;\u0026thinsp;0.1), and that are separated by at least 250kb from each other. We used positional mapping (window 10\u0026thinsp;kb) to map SNPs to genes. \u003c/p\u003e\n\u003cp\u003eCohort curation was conducted in R software (version 4.1). Main packages used included dplyr\u003csup\u003e61\u003c/sup\u003e (version 1.1.4), and ggplot2 (version 3.5.1). UK Biobank RAP platform was used to run the GWAS, using REGENIE\u003csup\u003e57\u003c/sup\u003e (version 3.3) and PLINK2\u003csup\u003e58\u003c/sup\u003e (version 1.1.1). FUMA\u003csup\u003e60\u003c/sup\u003e (version 1.7.0) was used to detect independent SNPs. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Long COVID associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate the findings from the LC discovery cohort, we tested the top lead independent SNPs in an independent LC validation cohort drawn from the UK Biobank COVID-19 serology study (waves 1-6). This cohort consisted of participants that had participated in the COVID-19 serology study (waves 1-6), with a complete antibody test result and date data across the six waves, and with at least a positive serology test result. Cases were defined as those participants that reported any symptom beyond 30 days after testing positive, whereas those that did not report any symptom were defined as controls. \u003c/p\u003e\n\u003cp\u003eWe used genotype calls data for the 1st step of the REGENIE method. Subsequently, in the 2nd step, we exclusively tested the top lead SNPs of each genomic loci obtained in the discovery analysis.\u003c/p\u003e\n\u003cp\u003eVariants with the OR from the main analysis and the validated OR(OR\u003csub\u003ev\u003c/sub\u003e) pointing to the same direction (OR and OR\u003csub\u003ev\u003c/sub\u003e, both being \u0026gt;1 or \u0026lt;1) and with a validated p-value (\u003cem\u003eP\u003c/em\u003e\u003csub\u003ev\u003c/sub\u003e)\u0026thinsp;\u0026le;\u0026thinsp;0.05 were fully validated. Variants with both OR having the same direction but with a validated \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 were partially validated. Variants with OR in opposite directions were not validated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong COVID subtypes exploration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed an additional analysis exploring four different subtypes of LC, defined based on our previous research\u003csup\u003e62\u003c/sup\u003e: ENT, respiratory and chest, neurological, and fatigue symptoms. These cohorts were applied with a similar inclusion criterion as in the discovery cohort (see Supplementary Note 2). GWAS was performed the same way as for the discovery cohort, with additionally adjusting for the first 20 principal components instead of the first 10, to better capture potential population stratification.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUK Biobank patient-level data can be accessed by applying for access at http://ukbiobank.ac.uk/register-apply/. All participants provided informed written consent to take part in the study. Ethics approval for the UK Biobank was granted by the North West Multi-Centre Research Ethics Committee in 2006 and was updated regularly after that (https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/about-us/ethics). This study was conducted after approval by UK Biobank under application reference 151425.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analytic code is publicly available at oxford-pharmacoepi/GenomeWideAssociationStudies_LC_PACS: GenomeWideAssociationStudies_LC_PACS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDPA research group from the University of Oxford has received research grants from the European Medicines Agency, from the Innovative Medicines Initiative, from Gilead Science, and from UCB Biopharma. S.I., J.J.W., and Y.L. are employees of Gilead Sciences and may own stock in the company. K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The remaining authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was conducted after approval by the UK Biobank under application reference 151425. This work uses data provided by patients and collected by the NHS as part of their care and support. This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (research which commenced between 1st October 2020\u0026ndash;31st March 2021 grant ref MC_PC_20029; 1st April 2021\u0026ndash;30th September 2022 grant ref MC_PC_20058). The research was supported by the National Institute for Health and Care Research (NIHR) Oxford Biomedical Research Centre (BRC) and by Gilead Sciences, Inc. DPA is funded through a NIHR Senior Research Fellowship (Grant number SRF-2018-11-ST2-004). K.L.G is funded through an MRC scholarship with Bayer AG as an industrial partner. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health and Care Research or the Department of Health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAl-Aly Z, Topol E. Solving the puzzle of Long Covid. \u003cem\u003eScience. \u003c/em\u003e2024;383(6685):830-832.\u003c/li\u003e\n\u003cli\u003eAl-Aly Z, Davis H, McCorkell L, et al. Long COVID science, research and policy. \u003cem\u003eNat Med. \u003c/em\u003e2024;30(8):2148-2164.\u003c/li\u003e\n\u003cli\u003ePeluso MJ, Deeks SG. 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Refinement of post-COVID condition core symptoms, subtypes, determinants, and health impacts: a cohort study integrating real-world data and patient-reported outcomes. \u003cem\u003eEBioMedicine. \u003c/em\u003e2025;111:105493.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Baseline characteristics of the long COVID (discovery) and PACS-CVD cohorts.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"635\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong COVID discovery cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLong COVID validation cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePACS-CVD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e8,469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e105,174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eOverall (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2,701 (31.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e244 (35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e1,885 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e5,768 (68.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e438 (64.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e103,289 (98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eAge (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e66.49 (7.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e64.82 (7.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e67.37 (8.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eSex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4,451 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e352 (51.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e58,401 (55.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4,018 (47.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e330 (48.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e46,773 (44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eBody mass index (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e26.03 (4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e26.81 (4.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e27.34 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eIndex of multiple deprivation (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13.85 (11.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e19.60 (14.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e16.83 (13.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 227px;\"\u003e\n \u003cp\u003eGenetic ethnic background (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e7,217 (85.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e409 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e88,610 (84.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1,252 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 110px;\"\u003e\n \u003cp\u003e273 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e16,564 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eLong COVID GWAS results. Note: CHR = Chromosome; SNP = Single Nucleotide Polymorphism; EA = Effect Allele; EAF = Effect Allele Frequency; OR = Odds Ratio; SE = Standard Error.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"941\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Type/Details\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReported traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValidation results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers7590539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.6e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eRN7SKP93 - MGAT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eRN7SKP93: Pseudogene\u003c/p\u003e\n \u003cp\u003eMGAT5: Protein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eRN7SKP93: Height, schizophrenia and cholesterol levels\u003c/p\u003e\n \u003cp\u003eMGAT5: Height, schizophrenia, alkaline phosphatase levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNot validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers375087201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eVENTXP4 - AC099754.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eVENTXP4: Pseudogene\u003c/p\u003e\n \u003cp\u003eAC099754.1: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eVENTXP4: Creatinine change after HIV infection\u003c/p\u003e\n \u003cp\u003eAC099754.1: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers34746824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eRP11-206P5.2 - RNU7-126P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eRP11-206P5.2: Information not specified RNU7-126P: snRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eRP11-206P5.2: None reported RNU7-126P: COVID-19 hospitalisation and severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers147065000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.5e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eUTR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eZNF454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eCognitive function and major depressive disorder interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers74874798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.2e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eADH5P4 - NUFIP1P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eADH5P4: Pseudogene\u003c/p\u003e\n \u003cp\u003eNUFIP1P: Pseudogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eADH5P4: Depression or major depressive disorder, type 2 diabetes, psychotic disorders\u003c/p\u003e\n \u003cp\u003eNUFIP1P: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNot validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers2106435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.8e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eZNF804B - AC002383.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eZNF804B: Protein coding gene\u003c/p\u003e\n \u003cp\u003eAC002383.2: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eZNF804B: Smoking, IgG glycosylation, and ovarian cancer\u003c/p\u003e\n \u003cp\u003eAC002383.2: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNot validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers12335232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.9e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eADCY8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eMemory decline, COVID-19 infection, COVID-19 severity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eFully validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers10966605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.3e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eRMRPP5 - RN7SKP120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eRMRPP5: Ribozyme\u003c/p\u003e\n \u003cp\u003eRN7SKP120: misc_RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eRMRPP5: Testosterone levels\u003c/p\u003e\n \u003cp\u003eRN7SKP120: Depression, testosterone levels, bronchodilator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers147950355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.2e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eTMEM246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eAlkaline phosphatase levels, liver enzyme levels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers9415008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.2e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003ePPA1 - NPFFR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePPA1: Protein coding gene\u003c/p\u003e\n \u003cp\u003eNPFFR1: Protein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ePPA1: Protein phosphatase levels\u003c/p\u003e\n \u003cp\u003eNPFFR1: Inorganic pyrophosphatase levels, decline rate in late mild cognitive impairment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers4551739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.4e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eNAV2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eAtrial fibrillation, brain shape, obesity, smoking initiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers77158180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.8e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eLIN7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eCreatinine change after HIV infection, type 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eNot validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers2702217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5.6e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eGLT1D1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eAging, response to zileuton treatment in asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers11621087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.0e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003ePTGR2 \u0026ndash;\u003c/p\u003e\n \u003cp\u003eRP5-1021I20.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003ePTGR2: Protein coding gene\u003c/p\u003e\n \u003cp\u003eRP5-1021I20.4: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003ePTGR2: Ectonucleoside triphosphate diphosphohydrolase 5 levels\u003c/p\u003e\n \u003cp\u003eRP5-1021I20.4: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers78215228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.5e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eCHRNA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 159px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 266px;\"\u003e\n \u003cp\u003eAcute kidney injury, COVID-19 infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003ePartially validated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e PACS-CVD GWAS results. Note: CHR = Chromosome; SNP = Single Nucleotide Polymorphism; EA = Effect Allele; EAF = Effect Allele Frequency; OR = Odds Ratio; SE = Standard Error.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"960\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCHR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Type/Details\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReported traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers11582898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.0e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRP11-115A15.4 - RP11-115A15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eRP11-115A15.4: Information not specified\u003c/p\u003e\n \u003cp\u003eRP11-115A15.2: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRP11-115A15.4: None reported\u003c/p\u003e\n \u003cp\u003eRP11-115A15.2: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers7643274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.0e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTRIM42 \u0026ndash;\u003c/p\u003e\n \u003cp\u003eRP11-691G17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eTRIM42: Protein coding gene\u003c/p\u003e\n \u003cp\u003eRP11-691G17.1: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eTRIM42: Bone mineral density levels, bipolar disorder\u003c/p\u003e\n \u003cp\u003eRP11-691G17.1: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers10212904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.80\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.4e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003encRNA_intronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eAC108142.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eInformation not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eNone reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers147544694\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.12\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.3e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSAYSD1 \u0026ndash;\u003c/p\u003e\n \u003cp\u003eKCNK5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eSAYSD1: Protein coding gene\u003c/p\u003e\n \u003cp\u003eKCNK5: Protein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eSAYSD1: BMI, coronary artery disease, myocardial infarction, diastolic blood pressure, type 2 diabetes\u003c/p\u003e\n \u003cp\u003eKCNK5: Coronary artery disease, myocardial infarction, diastolic blood pressure, urate levels, LDL cholesterol levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers147084175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.8e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eCCDC126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eSerum alkaline phosphatase levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers818468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.19\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.6e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePUS7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eADHD, autism, bipolar disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers117559490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.10\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRP11-440G5.2 - ROR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eRP11-440G5.2: Information not specified\u003c/p\u003e\n \u003cp\u003eROR2: Protein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRP11-440G5.2: None reported\u003c/p\u003e\n \u003cp\u003eROR2: Height, body mass index, glaucoma, tyrosine-protein kinase transmembrane receptor ROR2 levels, aspartate aminotransferase levels, COVID-19 infection and severity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers2486033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.04\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e3.6e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRP11-282I1.1 - RP11-282I1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eRP11-282I1.1: Information not specified\u003c/p\u003e\n \u003cp\u003eRP11-282I1.2: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRP11-282I1.1: None reported\u003c/p\u003e\n \u003cp\u003eRP11-282I1.2: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers7120231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.55\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e4.3e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eNELL1 \u0026ndash;\u003c/p\u003e\n \u003cp\u003eCTD-2019O4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eNELL1: Protein coding gene\u003c/p\u003e\n \u003cp\u003eCTD-2019O4.1: Information not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eNELL1: Protein kinase C, type 2 diabetes, opioid addiction\u003c/p\u003e\n \u003cp\u003eCTD-2019O4.1: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers17554072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.51\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.1e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eFLT1 \u0026ndash;\u003c/p\u003e\n \u003cp\u003eEIF4A1P7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eFLT1: Protein coding gene\u003c/p\u003e\n \u003cp\u003eEIF4A1P7: Pseudogene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eFLT1: Coronary artery disease, triglycerides, myocardial infarction\u003c/p\u003e\n \u003cp\u003eEIF4A1P7: None reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers61947186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.08\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.4e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003encRNA_intronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRP11-16D22.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eInformation not specified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eNone reported\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers1558138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.03\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.0e-07\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003ePTPRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eThyroid stimulating hormone levels, type 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers8126522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.60\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.9e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eBACH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eHeight, alanine aminotransferase levels, liver enzyme levels, body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003ers56067353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 28px;\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e1.68\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.11\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.1e-06\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eRUNX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003eProtein coding gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 317px;\"\u003e\n \u003cp\u003eRheumatoid arthritis, asthma, attempted suicide, haemoglobin concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\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":"","lastPublishedDoi":"10.21203/rs.3.rs-7676837/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7676837/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The genetic foundations of post-COVID-19 conditions remains unclear. We performed two genome-wide association studies (GWAS) in UK Biobank COVID-19 positive individuals to identify the genetic variants associated with Long COVID (LC) and post-acute cardiovascular complications of SARS-CoV-2 (PACS-CVD). The LC cohort comprised 8,469 participants (68% cases). The PACS-CVD cohort included 105,175 individuals (2% cases). LC GWAS identified 15 independent signals at suggestive significance (p-value\u003c5×10⁻⁶), with 73.3% validated. The fully validated variant, rs12335232 (ADCY8), has been linked to memory decline, COVID-19 infection and severity. Other loci were near CHRNA7 (neuroinflammation, COVID-19 severity) and RNU7-126P (COVID-19 hospitalization). These findings consistently demonstrate shared biological pathways between acute infection and persistent symptoms. PACS-CVD GWAS identified 14 suggestive loci, mainly near genes linked to cardiovascular and metabolic functions (SAYSD1/KCNK5, FLT1) or COVID-19 severity (ROR2). These results enhance the genetic understanding of Long COVID and PACS-CVD pathophysiology and highlight several potential therapeutic targets for both conditions.","manuscriptTitle":"Genome-wide association studies of Long COVID and post-acute complications of SARS-CoV-2 in the UK Biobank Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 11:31:59","doi":"10.21203/rs.3.rs-7676837/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":"0c55817f-eb0f-4036-964b-106f27a5d5de","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55281227,"name":"Health sciences/Medical research/Genetics research"},{"id":55281228,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2025-10-27T14:21:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 11:31:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7676837","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7676837","identity":"rs-7676837","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}