{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T05:10:04Z","timestamp":1772169004380,"version":"3.50.1"},"update-to":[{"DOI":"10.12688\/f1000research.55370.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000}},{"DOI":"10.12688\/f1000research.55370.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000}}],"reference-count":31,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T00:00:00Z","timestamp":1721952000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Australian Government National Health and Medical Research Council","award":["1195236"],"award-info":[{"award-number":["1195236"]}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>COVID-19 caused by SARS-CoV-2 has resulted in a global pandemic with a rapidly developing global health and economic crisis. Variations in the disease have been observed and have been associated with the genomic sequence of either the human host or the pathogen. Worldwide scientists scrambled initially to recruit patient cohorts to try and identify risk factors. A resource that presented itself early on was the UK Biobank (UKBB), which is investigating the respective contributions of genetic predisposition and environmental exposure to the development of disease. To enable COVID-19 studies, UKBB is now receiving COVID-19 test data for their participants every two weeks. In addition, UKBB is delivering more frequent updates of death and hospital inpatient data (including critical care admissions) on the UKBB Data Portal. This frequently changing dataset requires a tool that can rapidly process and analyse up-to-date data. We developed an R package specifically for the UKBB COVID-19 data, which summarises COVID-19 test results, performs association tests between COVID-19 susceptibility\/severity and potential risk factors such as age, sex, blood type, comorbidities and generates input files for genome-wide association studies (GWAS). By applying the R package to data released in April 2021, we found that age, body mass index, socioeconomic status and smoking are positively associated with COVID-19 susceptibility, severity, and mortality. Males are at a higher risk of COVID-19 infection than females. People staying in aged care homes have a higher chance of being exposed to SARS-CoV-2. By performing GWAS, we replicated the 3p21.31 genetic finding for COVID-19 susceptibility and severity. The ability to iteratively perform such analyses is highly relevant since the UKBB data is updated frequently. As a caveat, users must arrange their own access to the UKBB data to use the R package.<\/ns3:p>","DOI":"10.12688\/f1000research.55370.3","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T09:31:13Z","timestamp":1721986273000},"page":"830","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["UKB.COVID19: an R package for UK Biobank COVID-19 data processing and analysis"],"prefix":"10.12688","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5143-4146","authenticated-orcid":false,"given":"Longfei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victoria E","family":"Jackson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liam G","family":"Fearnley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5132-0774","authenticated-orcid":false,"given":"Melanie","family":"Bahlo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2560","published-online":{"date-parts":[[2024,7,26]]},"reference":[{"key":"ref1","first-page":"234","article-title":"\u201cHEALTH AND DEPRIVATION: Inequality and the North.\u201d","volume":"38","author":"D Black","year":"1988","journal-title":"J Royal College General Practitioners."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"e0247461","DOI":"10.1371\/journal.pone.0247461","article-title":"Population Risk Factors for Severe Disease and Mortality in COVID-19: A Global Systematic Review and Meta-Analysis.","volume":"16","author":"A Booth","year":"2021","journal-title":"PloS 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status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.169531.r307380, Thomas Michael Palmer, Population Health Sciences, University of Bristol Medical School, Bristol, 26 Aug 2024, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/10-830\/v3#referee-response-307380","order":0,"name":"referee-response-307380","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.58938.r126903, Annalisa De Silvestri, Virginia Valeria, Scientific Direction, IRCCS Policlinico San Matteo Foundation, Pavia, 22 Apr 2022, version 1, 1 approved, 1 not approved","URL":"https:\/\/f1000research.com\/articles\/10-830\/v1#referee-response-126903","order":1,"name":"referee-response-126903","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Longfei Wang<\/b>; \n<i>Posted: 02 May 2022<\/i>; \n<b>1. It is not clear how comorbidities are retrieved, classified (at which level of ICD-10), and analysed.<\/b>  We added a new table (Table 4) to show how the comorbidities are classified. We modified the following sentences:  \n<i>Comorbidity categories are generated using the block categories in the ICD10 code, which is shown in the second column in Table 4. We include ICD10 chapters 1\u201314 and 17 and exclude several chapters such as pregnancy, childbirth, and consequences of external causes etc. For instance, the first category is \u201cA00-A09\u201d, representing intestinal infectious diseases. During a period restricted by the start and end dates, cases are defined as any participants who were diagnosed as any subclasses under the block A00-A09 in the hospital inpatient diagnosis data. In this way, 164 binary variables are generated and each of them represents a comorbidity category.<\/i>  Therefore, we can test the association between each comorbidity category and the selected COVID-19 phenotype using logistic regression models. We modified the sentence as follows: \n<i>The comorbidity.asso function performs association tests between each comorbidity category and the selected phenotype using logistic regression models and adjusts the tested phenotype with covariates, which can be set using the argument \u201ccov.name\u201d.<\/i> \n<b>&nbsp;<\/b> \n<b>2. Authors should discuss how they choose to classify severity (the distinction between critical care and advanced critical care for example) and why they choose to include all Covid patients (for example severity 2-3 vs 0-1 instead of severity 2-3 vs 1). Why not consider it as an ordinal variable?<\/b>  Thanks for the suggestion. We re-wrote the following paragraph:  Based on the World Health Organization (WHO) ordinal scale for clinical improvement, we classify severity into four levels. These levels are defined as 1) hospitalisation: individuals admitted to hospital with their primary diagnosis recorded as COVID-19. 2) critical care level 2: individuals required basic treatment in a critical care unit, such as non-invasive ventilation and continuous positive airway pressure, and with their primary diagnosis recorded as COVID-19. 3) critical care level 3: individuals required advanced treatment in a critical care unit, such as invasive ventilation and temporary tracheostomy, and with their primary diagnosis recorded as COVID-19. 4) mortality: individuals died due to COVID-19. The critical care information was summarised from the HESIN_CRITICAL table and the HESIN_OPER table. The critical care level 2 cases are the COVID-19 patients who required at least one \u201cCritical care level 2 days\u201d in the HESIN_CRIRICAL table or received basic respiratory support, such as, E85.2 non-invasive ventilation NEC, in the HESIN_OPER table. The critical care level 3 cases are defined as the COVID-19 patients who required at least one \u201cCritical care level 3 days\u201d in the HESIN_CRIRICAL table or received advanced respiratory support, such as, E85.1 invasive ventilation, in the HESIN_OPER table. The commonly used GWAS tools, such as SAIGE and PLINK, do not support ordinal categorical phenotypes. Therefore, we converted this ordinal variable into four binary variables named \u201chospitalisation\u201d, \u201ccritical care\u201d, \u201cadvanced critical care\u201d and \u201cmortality\u201d (Table 2). However, users can get the ordinal variable by simply summing the four binary variables. We assume that participants who were tested COVID-19 positive but did not admit to hospital had no or mild symptoms and hence classified them as controls in severity phenotypes. \n<b>&nbsp;<\/b> \n<b>3. Authors should specify if they consider mortality due to Covid or with Covid<\/b>  Sorry for the unclearness. We defined the mortality case as mortality due to Covid. In the article, we wrote:  \n<i>For mortality, we include all individuals who received at least one positive test result and define those whose primary cause of death is recorded as being due to COVID-19 as cases.<\/i> To make it clearer, we corrected the definition of mortality in Table 2 from \u201c1 = death with COVID-19\u201d to \u201c1 = death due to COVID-19\u201d.","URL":"https:\/\/f1000research.com\/articles\/10-830\/v3#referee-comment-8179","order":2,"name":"referee-comment-8179","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.133689.r287362, Edgar Gonzalez-Kozlova, Icahn School of Medicine at Mount Sinai, New York, NY, 12 Jun 2024, version 2, 1 approved, 2 not approved","URL":"https:\/\/f1000research.com\/articles\/10-830\/v2#referee-response-287362","order":3,"name":"referee-response-287362","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"<b>Longfei Wang<\/b>; \n<i>Posted: 23 Jul 2024<\/i>; \n<b>------------------ Reviewer Comment: 1.&nbsp;Prepare make a vignette available showcasing exactly how you intended the package to be used. While the article describes well the study and package, packages without a vignette are disregarded.<\/b>  \n<b>Author Response: <\/b>We appreciate your suggestion. We have created a vignette and updated the UKB.COVID R package on CRAN (https:\/\/cran.r-project.org\/web\/packages\/UKB.COVID19\/vignettes\/Introduction_to_UKB_COVID19.html). \n<b>------------------<\/b>  \n<b>Reviewer Comment:<\/b> \n<b>2.&nbsp;There is no mention of long covid in the article. Long term effects of COVID19 cant be ignored. Please include a discussion and\/or status of long covid patients in the article.<\/b>  \n<b>Author Response: <\/b>Thank you for your suggestion. We have added a discussion of long COVID and provided relevant functions in UKB.COVID19.  Long COVID, also known as post-acute sequelae of SARS-CoV-2 infection, refers to a range of symptoms that persist for weeks or months after the acute phase of COVID-19 has resolved. These symptoms can include fatigue, shortness of breath, cognitive dysfunction, and various other systemic issues, significantly impacting the quality of life of affected individuals. The UKB.COVID19 package provides multiple functions to facilitate long COVID analysis. For instance, the \u2018comorbidity_summary\u2019 and \u2018comorbidity_asso\u2019 functions can be used to summarise potential long COVID symptoms and assess their associations with risk factors, such as age, sex and certain pre-existing conditions. Furthermore, researchers can focus on subsets of participants reporting persistent symptoms consistent with long COVID to investigate genetic risk factors using GWAS. These analyses hold promise for uncovering the biological underpinnings of long COVID and identifying potential therapeutic targets to alleviate its impact. \n<b>------------------<\/b>  \n<b>Reviewer Comment:<\/b> \n<b>3.&nbsp;A statistics section is missing from the methods section. Every test used in the article should be clearly described and justified in methods.<\/b>  \n<b>Author Response: <\/b>We added a statistics section in the methods section.  \n<b>Statistical analysis<\/b> To assess the associations between non-genetic risk factors and COVID-19 phenotypes (including susceptibility, severity, and mortality), we employed multivariable logistic regression models using the \u2018glm\u2019 function from the R package stats. Each model adjusted for covariates such as age, sex, and BMI. The tested risk factors included socioeconomic status (SES), smoking status, blood type, ethnic background, and residence in aged care facilities. The logistic regression model for each risk factor was specified as follows: logit(COVID-19 phenotype) ~ risk factor + age + sex + BMI. Comorbidity associations were analyzed using similar multivariable logistic regression models, with COVID-19 phenotypes modeled as: logit(COVID-19 phenotype) ~ comorbidity category + age + sex + BMI + SES + smoking status + aged care status. Odds ratios (ORs) with 95% confidence intervals (CIs) were reported, and p-values were calculated to determine the significance of the associations. To identify genetic variants associated with COVID-19 phenotypes, we performed GWASs using the SAIGE software. Principal component analysis (PCA) was performed to account for population stratification, and the first 20 principal components (PCs) were included as covariates in the analysis. Additionally, we adjusted for age, sex, BMI, SES, smoking status, residence in aged care facilities and genotypic array in the regression models. The association between each SNP and the phenotypes was tested using a logistic regression model, as follows: logit(COVID-19 phenotype) ~ SNP + age + sex + BMI + SES + smoking status + aged care status + genotypic array + PC1-20. To account for multiple testing, the Bonferroni correction was applied. Loci reaching the genome-wide significance threshold (p &lt; 5x10\n<sup>-8<\/sup>) were considered significant. Manhattan plots and quantile-quantile (QQ) plots were generated to visualize the results using R package ggplot2. All analyses were carried out using R (version 4.0.5). \n<b>------------------<\/b>","URL":"https:\/\/f1000research.com\/articles\/10-830\/v3#referee-comment-12063","order":4,"name":"referee-comment-12063","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This work was made possible through the Victorian State Government Operational Infrastructure Support and Australian Government National Health and Medical Research Council (NHMRC) independent research Institute Infrastructure Support Scheme (IRIISS). Melanie Bahlo was supported by an NHMRC Investigator Grant (1195236). Access to the UKBB for this project was granted through project ID 36610.","order":5,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}