BACKGROUND:Vast amounts of genome sequencing data generated from large-scale research studies like HostSeq provide an opportunity to summarise the spectrum of pathogenic variation in a subset of the Canadian population. Sharing variant-level data with public databases, like ClinVar, is crucial for advancing our understanding of genomic variants related to Mendelian diseases. However, such entries are often incomplete or contain discrepancies which render interpretation and classifications less useful. METHODS:The GENCOV and HostSeq cohorts were annotated and summarised using custom workflows to identify variants with a pathogenic and likely pathogenic (P/LP) classification in ClinVar. These variants were further filtered using custom gene panels, gnomAD frequency, variant type, gene-disease relationship and the number of reputable ClinVar laboratory submissions. Manually assessed variants from the GENCOV study were compared with ClinVar classifications to identify discrepancies. RESULTS:A total of 1956 unique variants were manually classified as P/LP through the GENCOV study. Of these, 65% (1276) were also identified in ClinVar, and among those, 69% (889) had concordant P/LP classifications. The manual assessment of unique exonic variants in GENCOV yielded a higher number of putative P/LP variants (1595), including truncating and missense variants compared with the 127 unique exonic P/LP variants in HostSeq. CONCLUSION:These results highlight that a large proportion of P/LP variation is either absent or has conflicting evidence for pathogenicity in ClinVar, emphasising the importance of periodic reassessment, discrepancy resolution and updates to ensure completeness and accuracy of public databases.

Purpose:Opportunistic genome sequencing (GS) allows for the return of findings to clinical and research cohorts. We report on comprehensive GS results from the GENCOV study in Ontario, Canada. Methods:GS data were analyzed for clinically significant variants associated with monogenic disease and carrier status for autosomal recessive and X-linked conditions, pharmacogenomic variation, polygenic risk scores for common conditions, human leukocyte antigen and blood group genotypes, and genetic ancestry. GS results were summarized using descriptive statistics. Results:GS was completed on 1292 participants; 53% were female, 53% were 18 to 39 years old, and 816 (63%) were estimated to have European genetic ancestry. All (100%) had a variant associated with drug metabolism, 845 (65%) with increased polygenic risk scores, 735 (57%) with a risk-associated human leukocyte antigen genotype, and 857 (69%) and 91 (7%) with a rare red blood cell and/or platelet antigen, respectively. Of 851 who received reports, 261 (31%) had a variant associated with monogenic disease (178 or 21% were considered medically actionable) and 782 (92%) had at least one variant associated with carrier status. Conclusion:Opportunistic GS demonstrated that many individuals harbor GS findings impacting their health, illustrating the potential of GS to inform personalized and proactive health care for Canadians.

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

Introduction: The GENCOV study sought to evaluate serological differences between individuals with differing COVID-19 severity and outcomes. We assessed the SARS-CoV-2 antibody response of GENCOV participants cross- sectionally 1-, 6-, and 12-months following COVID-19 diagnosis to identify patient factors associated with more robust and durable humoral immune responses. Materials and Methods: COVID-19 patients and a control cohort of vaccinated infection-na & iuml;ve participants were recruited at hospital sites across the Greater Toronto Area in Ontario, Canada. Commercially available and laboratory-developed serological assays were used to characterize features of participants' antibody responses, including both binding and neutralizing antibodies. Regression analyses were performed to identify associations between participant characteristics and features of the SARS-CoV-2 antibody response. Results: Samples were obtained from participants 1- (n = 938), 6- (n = 842), and 12-months (n = 662) post- infection or vaccination. At all time points, vaccinees, and to a greater extent those who were both infected and vaccinated, had significantly elevated anti-spike antibody levels compared to unvaccinated participants. Increasing age and/or illness severity were associated with significantly higher antibody levels among unvaccinated participants. Among vaccines, those who were vaccinated after infection (i.e., hybrid immunity) had consistently higher antibody levels compared to participants who were infection-naive or vaccinated before their infection (i.e., breakthrough infections). Additionally, receiving more vaccine doses and having a more recent vaccination were strongly associated with higher antibody levels across all time points. Conclusions: Our findings highlight various patient factors, including vaccination, which contribute to robust, durable SARS-CoV-2 antibody responses. Overall, the findings presented here may inform future vaccine development and rollout plans.

Objectives Patient characteristics related to an increased risk of severe coronavirus disease 2019 (COVID-19) have been thoroughly studied since the beginning of the pandemic; however, clinical tools offering rapid and automated predictions of patients’ acute reactions to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection remain limited. This study explored the associations between laboratory markers and mortality in hospitalized patients with COVID-19 and developed a scoring model using laboratory data to estimate patients’ risk of mortality. Methods Participants were recruited from hospitals in the Greater Toronto area between January 2020 and February 2022. Demographics, laboratory results, and treatment outcomes were collected from patient medical charts. Admission data for 33 biochemical and hematological markers assessing complete blood cell count, coagulation, general chemistry, and inflammatory, liver, renal, and cardiac function were extracted for analyses. Results Logistic regression revealed that 6 laboratory markers, including creatinine, sodium, bicarbonate, base excess, pH, and lactate, were significantly associated with COVID-19 patient mortality. Five markers were incorporated into a multivariable model after excluding correlated analytes. Low bicarbonate levels were the only significant finding in the multivariable model associated with increased odds of mortality. Receiver operating characteristic (ROC) curves (area under the curves (AUCs)) revealed that risk scores constructed from multivariable values performed similarly to their univariable counterparts in both the training (0.82 vs 0.83) and validation (0.80 vs 0.80) cohorts. Overall, the risk score exhibited 80% accuracy in predicting mortality, with greater sensitivity than specificity. Conclusions Developed risk scores provide moderate predictions of COVID-19 mortality, which could be improved by assessing larger populations. Additionally, significant markers from our cohort indicate that at-risk patients may present with acid‒base disruptions.

The GENCOV study is a research initiative on ostensibly healthy COVID-19 positive adults in Toronto. Genomes for 1281 participants were sequenced and analyzed, and clinical characteristics were collected. GENCOV participants were surveyed on their self-reported ancestry. Race/ethnicity is known to have an impact on COVID-19 severity and susceptibility, as well as eligibility for genetic testing. However self-reported ancestry may not always accurately reflect individuals on a genetic scale if they are admixed with multiple ethnically distinct populations.

DNA variant databases play a pivotal role in evaluating the clinical implications of DNA variations for disease diagnosis and treatment. However, these databases are often replete with both false positives and negatives, leading to potential misinterpretations and consequential impacts on patients and clinicians relying on this data for genetic testing. ClinVar, an expansive public database documenting genomic variations and their implications for human health, holds substantial significance in both medical research and clinical applications.

PURPOSE:Novel uses of genome sequencing (GS) present an opportunity for return of results to healthy individuals, prompting the need for scalable genetic counseling strategies. We evaluate the effectiveness of a genomic counseling model (GCM) and explore preferences for GS findings in the general population. METHODS:Participants (N = 466) completed GS and our GCM (digital genomics platform and group-based webinar) and indicated results preferences. Surveys were administered before (T0) and after (T1) GCM. Change in knowledge and decisional conflict (DC) were evaluated using paired-sample T and Wilcoxon tests. Factors influencing knowledge and results preferences were evaluated using linear and logistic regression models. RESULTS:Participants were 56% female, 58% white, and 53% ≥40 years of age. Mean knowledge scores increased (Limitations: 3.73 to 5.63; Benefits: 4.34 to 5.48, P < .0001), and DC decreased (-21.9, P < .0001) at T1 versus T0. Eighty-six percent of participants wished to learn all GS findings at T1 vs 78% at T0 (P < .0001). Older age, negative/mixed attitudes toward genetics and greater DC were associated with change in preferences after intervention. CONCLUSION:In a population-based cohort undergoing GS interested in learning GS findings, our GCM increased knowledge and reduced DC, illustrating the GCM's potential effectiveness for GS counseling in the general population.

The HostSeq database is a repository containing genome sequencing (GS) results and harmonized clinical data for ∼10,000 Canadians infected with SARS-CoV-2 over the COVID-19 pandemic. HostSeq is being used to facilitate research efforts in understanding the risks for disease and health outcomes. Phenome-wide association studies (PheWAS) are an approach used to identify associations between a large number of clinical phenotypes simultaneously and specific genetic markers. This comprehensive analysis can potentially reveal novel genetic risk factors related to diverse health conditions and inform disease prevention and treatment strategies.

Background: GENCOV is a prospective, observational cohort study of COVID-19-positive adults. Here, we characterize and compare side effects between COVID-19 vaccines and determine whether reactogenicity is exacerbated by prior SARS-CoV-2 infection. Methods: Participants were recruited across Ontario, Canada. Participant -reported demographic and COVID-19 vaccination data were collected using a questionnaire. Multivariable logistic regression was performed to assess whether vaccine manufacturer, type, and previous SARS-CoV-2 infection are associated with reactogenicity. Results: Responses were obtained from n = 554 participants. Tiredness and localized side effects were the most common reactions across vaccine doses. For most participants, side effects occurred and subsided within 1 -2 days. Recipients of Moderna mRNA and AstraZeneca vector vaccines reported reactions more frequently compared to recipients of a Pfizer-BioNTech mRNA vaccine. Previous SARS-CoV-2 infection was independently associated with developing side effects. Conclusions: We provide evidence of relatively mild and short-lived reactions reported by participants who have received approved COVID-19 vaccines.

Rapid advancements of genome sequencing (GS) technologies have enhanced our understanding of the relationship between genes and human disease. To incorporate genomic information into the practice of medicine, new processes for the analysis, reporting, and communication of GS data are needed. Blood samples were collected from adults with a PCR-confirmed SARS-CoV-2 (COVID-19) diagnosis (target N = 1500). GS was performed. Data were filtered and analyzed using custom pipelines and gene panels. We developed unique patient-facing materials, including an online intake survey, group counseling presentation, and consultation letters in addition to a comprehensive GS report. The final report includes results generated from GS data: (1) monogenic disease risks; (2) carrier status; (3) pharmacogenomic variants; (4) polygenic risk scores for common conditions; (5) HLA genotype; (6) genetic ancestry; (7) blood group; and, (8) COVID-19 viral lineage. Participants complete pre-test genetic counseling and confirm preferences for secondary findings before receiving results. Counseling and referrals are initiated for clinically significant findings. We developed a genetic counseling, reporting, and return of results framework that integrates GS information across multiple areas of human health, presenting possibilities for the clinical application of comprehensive GS data in healthy individuals.

Differences in SARS-CoV-2-specific immune responses have been observed between individuals following natural infection or vaccination. In addition to already known factors, such as age, sex, COVID-19 severity, comorbidity, vaccination status, hybrid immunity, and duration of infection, inter-individual variations in SARS-CoV-2 immune responses may, in part, be explained by structural differences brought about by genetic variation in the human leukocyte antigen (HLA) molecules responsible for the presentation of SARS-CoV-2 antigens to T effector cells. While dendritic cells present peptides with HLA class I molecules to CD8+ T cells to induce cytotoxic T lymphocyte responses (CTLs), they present peptides with HLA class II molecules to T follicular helper cells to induce B cell differentiation followed by memory B cell and plasma cell maturation. Plasma cells then produce SARS-CoV-2-specific antibodies. Here, we review published data linking HLA genetic variation or polymorphisms with differences in SARS-CoV-2-specific antibody responses. While there is evidence that heterogeneity in antibody response might be related to HLA variation, there are conflicting findings due in part to differences in study designs. We provide insight into why more research is needed in this area. Elucidating the genetic basis of variability in the SARS-CoV-2 immune response will help to optimize diagnostic tools and lead to the development of new vaccines and therapeutics against SARS-CoV-2 and other infectious diseases.

Objectives: Concepts related to SARS-CoV-2 laboratory testing and result interpretation can be challenging to understand. A cross-sectional survey of COVID-19 positive adults residing in Ontario, Canada was conducted to explore how well people understand SARS-CoV-2 laboratory tests and their associated results. Design and methods: Participants were recruited through fliers or by prospective recruitment of outpatients and hospitalized inpatients with COVID-19. Enrolled participants included consenting adults with a positive SARSCoV-2 polymerase chain reaction test result. An 11-item questionnaire was developed by researchers, nurses, and physicians in the study team and was administered online between April 2021 to May 2022 upon enrolment into the study.Results: Responses were obtained from 940 of 1106 eligible participants (85% participation rate). Most respondents understood 1) that antibody results should not influence adherence to social distancing measures (n = 602/888, 68%), 2) asymptomatic SARS-CoV-2 infection following test positivity (n = 698/888, 79%), 3) serological test sensitivity in relation to post-infection timeline (n = 540/891, 61%), and 4) limitations of experts' knowledge related to SARS-CoV-2 serology (n = 693/887, 78%). Conversely, respondents demonstrated challenges understanding 1) conflicting molecular and serological test results and their relationship with immune protection (n = 162/893, 18%) and 2) the impact of SARS-CoV-2 variants on vaccine effectiveness (n = 235/891, 26%). Analysis of responses stratified by sociodemographic variables identified that respondents who were either: 1) female, 2) more educated, 3) aged 18-44, 4) from a high-income household, or 5) healthcare workers responded expectedly more often. Conclusions: We have highlighted concepts related to SARS-CoV-2 laboratory tests and associated results which may be challenging to understand. The findings of this study enable us to identify 1) misconceptions related to various SARS-CoV-2 test results, 2) groups of individuals at risk, and 3) strategies to improve people's understanding of their test results.

The GENCOV study aims to identify patient factors which affect COVID-19 severity and outcomes. Here, we aimed to evaluate patient characteristics, acute symptoms and their persistence, and associations with hospitalization. Participants were recruited at hospital sites across the Greater Toronto Area in Ontario, Canada. Patient-reported demographics, medical history, and COVID-19 symptoms and complications were collected through an intake survey. Regression analyses were performed to identify associations with outcomes including hospitalization and COVID-19 symptoms. In total, 966 responses were obtained from 1106 eligible participants (87% response rate) between November 2020 and May 2022. Increasing continuous age (aOR: 1.05 [95%CI: 1.01–1.08]) and BMI (aOR: 1.17 [95%CI: 1.10–1.24]), non-White/European ethnicity (aOR: 2.72 [95%CI: 1.22–6.05]), hypertension (aOR: 2.78 [95%CI: 1.22–6.34]), and infection by viral variants (aOR: 5.43 [95%CI: 1.45–20.34]) were identified as risk factors for hospitalization. Several symptoms including shortness of breath and fever were found to be more common among inpatients and tended to persist for longer durations following acute illness. Sex, age, ethnicity, BMI, vaccination status, viral strain, and underlying health conditions were associated with developing and having persistent symptoms. By improving our understanding of risk factors for severe COVID-19, our findings may guide COVID-19 patient management strategies by enabling more efficient clinical decision making.

DNA biobanks developed for COVID-19 research have the potential to discover medically actionable findings that may be relevant not only for identifying individuals at high risk for COVID-19 related disease, but that also predict genetic conditions for which prevention, treatment or management strategies may be available.

Genome sequencing holds the promise for great public health benefits. It is currently being used in the context of rare disease diagnosis and novel gene identification, but also has the potential to identify genetic disease risk factors in healthy individuals. Genome sequencing technologies are currently being used to identify genetic factors that may influence variability in symptom severity and immune response among patients infected by SARS-CoV-2. The GENCOV study aims to look at the relationship between genetic, serological, and biochemical factors and variability of SARS-CoV-2 symptom severity, and to evaluate the utility of returning genome screening results to study participants. Study participants select which results they wish to receive with a decision aid. Medically actionable information for diagnosis, disease risk estimation, disease prevention, and patient management are provided in a comprehensive genome report. Using a combination of bioinformatics software and custom tools, this article describes a pipeline for the analysis and reporting of genetic results to individuals with COVID-19, including HLA genotyping, large-scale continental ancestry estimation, and pharmacogenomic analysis to determine metabolizer status and drug response. In addition, this pipeline includes reporting of medically actionable conditions from comprehensive gene panels for Cardiology, Neurology, Metabolism, Hereditary Cancer, and Hereditary Kidney, and carrier screening for reproductive planning. Incorporated into the genome report are polygenic risk scores for six diseases-coronary artery disease; atrial fibrillation; type-2 diabetes; and breast, prostate, and colon cancer-as well as blood group genotyping analysis for ABO and Rh blood types and genotyping for other antigens of clinical relevance. The genome report summarizes the findings of these analyses in a way that extensively communicates clinically relevant results to patients and their physicians. © 2022 Wiley Periodicals LLC. Basic Protocol 1: HLA genotyping and disease association Basic Protocol 2: Large-scale continental ancestry estimation Basic Protocol 3: Dosage recommendations for pharmacogenomic gene variants associated with drug response Support Protocol: System setup.