Genetic array data from the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA)

Genetic array data from the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Data Note Genetic array data from the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA) Laura Smyth, Marisa Canadas Garre, Claire Hill, Claire Potter, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9075179/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Northern Ireland’s largest national population cohort over 50 years of age, with a focus on the collection of longitudinal data including computer-assisted personal interviews, self-completed questionnaires and in-person health assessments takes form as the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA). Data from 8,283 individuals over 50 years of age was collected from individuals within NICOLA’s first wave of data collection for this cohort. Together, the data collected aims to help researchers explore how social, economic, health, lifestyle, and biological factors influence healthy ageing. NICOLA places a strong emphasis on molecular biomarkers, with a subset of 3,471 participants (44%) undergoing health assessments at wave 1, including the collection of blood samples specifically for this analysis. Objectives : Using blood-derived DNA collected from individuals within NICOLA, genetic data was generated to enable research projects associated with rich cross-sectional and longitudinal data within NICOLA to explore genetic connections to the health, lifestyle, environment and financial situations of people as they grow older. Data description: Genetic data was generated using the Illumina Infinium CoreExome-24 array (Illumina, USA) and is available for 2,978 NICOLA participants with 551,839 directly genotyped and 18,148,478 imputed single nucleotide polymorphisms following initial quality control. Ageing Association Genetic Longitudinal Molecular NICOLA Northern Ireland Population SNP Objective Northern Ireland’s largest population cohort collecting longitudinal data from a sample of the over-50s population (n=8,283) was launched in 2012 as the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) [1], [2]. Throughout the recruitment, spouses or partners of invited individuals who resided in the same home at time of interview, who were under the age of 50 (n=195, n=8,478 total population) were also invited to take part. A strong focus was placed on the generation of molecular biomarkers as part of the Wave 1 data collection and analysis. The genetic data generated forms a key part of this and is described in this data note. Genetic data was generated using the Illumina Infinium CoreExome-24 array (Illumina, USA) for 3,266 individuals and is available for 2,978 NICOLA participants following initial quality control for 551,839 directly genotyped and 18,148,478 imputed single nucleotide polymorphisms (SNPs). Summary statistics for the association of these gene polymorphisms with ~30 phenotypes have been generated to date and are available as derived variables from the NICOLA Data Access Committee[3]. The genetic .bam, .bim and .fam data files are available through data access requests made to the European Genome-Phenome Archive using study ID EGAS00001007915, dataset ID EGAD00010002762. Data description NICOLA Sample Collection and Preparation NICOLA participants who consented to the in-person health assessment element of data collection provided blood samples which were collected in EDTA tubes at the clinical research facility of Queen’s University Belfast. Sample processing using aseptic technique was conducted in laboratories on the same floor of the facility; blood tubes were centrifuged at 3000 rpm for 10 minutes at 4°C before a 1 mL pipette was used to carefully remove each buffy coat before being frozen at -80°C. DNA was extracted, quantified using PicoGreen, normalised to 200 ng/μL using 0.1 TE and stored in aliquots at -80°C to minimise freeze/thaw cycles. Critically important for this data, all samples were processed in a single recruitment laboratory with genetic data generated in a single laboratory. All wet-lab typing included standard experimental controls including replicate samples (n=12), control samples of known genotype blinded to the genotyping laboratory (n=7) and no template controls (n=4). Measurement of Genetic Biomarkers Samples were genotyped by Eurofins Scientific (https://www.eurofinsgenomics.eu). Genotype data (n=551,839 markers directly typed) was generated using the Illumina Infinium CoreExome-24 on an iScan for two batches composed of 2,799 and 467 participants respectively (n=3,266). For the purposes of analysis, each batch was processed separately as they were genotyped at different timepoints. GenomeStudio® Software’s Genotyping Module was used alongside the Genome Reference Consortium Human Build 37 (GRCh37) for analysis. Each SNP was called using clustering methods with an independent visual review of any outliers to more accurately call genotypes. Samples were excluded if they had insufficient DNA quality, quantity, or poor genotype concordance with previous genotypes during careful evaluation. Quality Control and Imputation of Genetic Data The QC of genotyped data was performed in PLINK 1.90 beta[4]. QC included removal of samples with a call rate <95%, heterozygosity, principal component analysis outliers, sex mismatches and duplicates. Up to second-degree related individuals were also removed by eliminating one individual from each pair with an identity by descent value >0.1875[5], [6]. Variants with a call rate ≥98%, Hardy-Weinberg equilibrium p>10 -6 and minor allele frequency <0.0001 were removed. Files were prepared for imputation using the Haplotype Reference Consortium (HRC) / 1000 Genomes Imputation Preparation and Checking Tool , developed by Will Rayner[7], before imputation to the 1000 Genomes Phase3 v5 (1KGP3) and HRC r1.1 2016 reference panels using the Michigan Imputation Server[8]. A minor allele cut-off of 5 and imputation quality of 0.3 was applied to imputed files; monomorphic markers were removed. Upon completion of initial QC, 2,526 samples for batch 1 and 452 samples for batch 2 remained in the cohort (n=2,978 total). An overview of the datafiles is included in Table 1. Table 1 : Overview of data files/data sets. Label Name of data file/data set File types (file extension) Data repository and identifier (DOI or accession number) Data file 1 Genetic data .bed file for batch 1 of the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) .bed EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Data file 2 Genetic data .bim file for batch 1 of NICOLA .bim EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Data file 3 Genetic data .fam file for batch 1 of NICOLA .fam EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Data file 4 Genetic data .bed file for batch 2 of NICOLA .bed EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Data file 5 Genetic data .bim file for batch 2 of NICOLA .bim EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Data file 6 Genetic data .fam file for batch 2 of NICOLA .fam EGA ID: EGAD00010002762 DAC ID: EGAC00001003504 Exemplar Genetic Data GWAS analysis of the NICOLA dataset has contributed to an international GWAS examining human height[9], an international multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels[10], [11], [12] and two GWAS examining age-related macular degeneration[13], [14]. The associated summary statistics have been returned to the NICOLA bioresource and are available for researchers within our data access agreements. Limitations Genetic data is not available for all individuals who participated in NICOLA, rather is available for a sub-set of the larger cohort from Wave 1. All individuals who attended the health assessment and provided a venous blood sample with permission for genetic analysis were submitted for genotyping. Despite being a nationally representative cohort, there is a limitation in terms of the response bias which has an over-representation of individuals with higher education levels. Despite the study being designed to be representative of the over 50s population in NI using a random selector for participants based on residential address, there is potential for selection bias to have attracted more healthy individuals. Due to the small number of ethnic minorities included within NICOLA, no statistically robust estimates could be generated for this population subset. Abbreviations 1KGP3 - 1000 Genomes Phase3 v5 DNA – deoxyribose nucleic acid EDTA - ethylenediaminetetraacetic acid GRCh37 - Genome Reference Consortium Human Build 37 HRC - Haplotype Reference Consortium NICOLA - Northern Ireland COhort for the Longitudinal study of Ageing SNP – single nucleotide polymorphism QC – quality control Declarations Ethics approval and consent to participate Ethical approval for the study was obtained from the School of Medicine, Dentistry and Biomedical Sciences Ethics Committee, Queen’s University Belfast. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004). Informed consent was obtained from all participants in the study - Ethical approval for the study was obtained from the School of Medicine, Dentistry and Biomedical Sciences Ethics Committee, Queen’s University Belfast (Ref 12/23). Consent for publication All data is anonymised. Availability of data and materials The data described in this Data note can be freely and openly accessed through the European Genome-Phenome Archive under the study accession number EGAS00001007915. Competing interests Authors declare no competing interests. Funding The Atlantic Philanthropies, the Economic and Social Research Council (ES/L0084559/1), the UKCRC Centre of Excellence for Public Health Northern Ireland, the Centre for Ageing Research and Development in Ireland, the Office of the First Minister and Deputy First Minister, the Health and Social Care Research and Development Division of the Public Health Agency, the Wellcome Trust/Wolfson Foundation and Queen’s University Belfast provided core financial support for NICOLA Waves 1 and 2. The authors alone are responsible for the interpretation of the data and any views or opinions presented are solely those of the authors and do not necessarily represent those of the NICOLA Study team. We also acknowledge individual funding awards from, the Northern Ireland Kidney Research Fund, the Medical Research Council (MC_PC_20026) and the Public Health Agency R&D Division (STL/5569/19), Science Foundation Ireland (SFI15/US/B3130), NIH R01_DK105154, a Science Foundation Ireland and the Department for the Economy, Northern Ireland US partnership award 15/IA/3152, the Economic and Social Research Council (ES/L008459/1) and Welcome Trust/ Health Research Board Ireland’s Irish Clinical Academic Training (ICAT) Fellowship grant number 203930/B/16/Z. Authors’ contributions AJM, BM, FK and IY were responsible for study concept, design, and funding acquisition. LJS and MCG prepared the data. LJS, MCG, CH and CP prepared the initial drafts of the manuscript. LS, CH, CP, and AJM carried out subsequent revisions. All authors contributed to the critical revision of the manuscript, proof reading and approval of the final version. Acknowledgements We are grateful to all the participants of the NICOLA Study, and the whole NICOLA team, which includes nursing staff, research scientists, clerical staff, computer and laboratory technicians, managers and receptionists. References C. E. Neville et al. , “Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): health assessment protocol, participant profile and patterns of participation,” BMC Public Health , vol. 23, no. 1, pp. 1–19, 2023, doi: 10.1186/s12889-023-15355-x. C. Neville et al. , “Cohort Profile: The Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA),” Int J Epidemiol , pp. 1–11, 2023, doi: 10.1093/ije/dyad026. M. Canadas-Garre, L. Smyth, C. Neville, F. Kee, J. Woodside, and A. McKnight, “Objective Measures of Health and Wellbeing of Older Adults in Northern Ireland The NICOLA Study Wave 1; Chapter 8 Molecular Biomarkers ,” Belfast, Jun. 2021. C. C. Chang, C. C. Chow, L. C. A. M. Tellier, S. Vattikuti, S. M. Purcell, and J. J. Lee, “Second-generation PLINK: Rising to the challenge of larger and richer datasets,” Gigascience , vol. 4, no. 1, pp. 1–16, 2015, doi: 10.1186/s13742-015-0047-8. S. H. Slifer, “Current Protocols in Human Genetics Current Protocols in Human Genetics UNIT PLINK: Key Functions for Data Analysis,” Curr Protoc Hum Genet , vol. 97, no. 1, p. e59, 2018, doi: 10.1002/cphg.59. C. A. Anderson, F. H. Pettersson, G. M. Clarke, L. R. Cardon, A. P. Morris, and K. T. Zondervan, “Data quality control in genetic case-control association studies,” Nat Protoc , vol. 5, no. 9, pp. 1564–1573, 2010, doi: 10.1038/nprot.2010.116. W. Rayner, “McGarthy Group Tools.” Accessed: Jul. 18, 2023. [Online]. Available: https://www.well.ox.ac.uk/~wrayner/tools/ S. Das et al. , “Next-generation genotype imputation service and methods,” Nat Genet , vol. 48, no. 10, pp. 1284–1287, 2016, doi: 10.1038/ng.3656. L. Yengo et al. , “A saturated map of common genetic variants associated with human height,” Nature , vol. 610, no. 7933, pp. 704–712, 2022, doi: 10.1038/s41586-022-05275-y. S. E. Graham et al. , “The power of genetic diversity in genome-wide association studies of lipids,” Nature , vol. 600, no. 7890, pp. 675–679, 2021, doi: 10.1038/s41586-021-04064-3. S. Ramdas et al. , “A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids,” Am J Hum Genet , vol. 109, no. 8, pp. 1366–1387, 2022, doi: 10.1016/j.ajhg.2022.06.012. S. Kanoni et al. , “Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis,” Genome Biol , vol. 23, no. 1, pp. 1–42, 2022, doi: 10.1186/s13059-022-02837-1. R. E. Hogg et al. , “Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study,” British Journal of Ophthalmology , vol. 107, no. 12, pp. 1873–1879, 2023, doi: 10.1136/bjo-2021-320469. T. W. Winkler et al. , “Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease,” BMC Med Genomics , vol. 13, no. 1, pp. 1–18, 2020, doi: 10.1186/s12920-020-00760-7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Apr, 2026 Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 18 Mar, 2026 Submission checks completed at journal 18 Mar, 2026 First submitted to journal 09 Mar, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9075179","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Data Note","associatedPublications":[],"authors":[{"id":632447812,"identity":"237d11ef-cb14-486b-8a44-a904a88598f7","order_by":0,"name":"Laura Smyth","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACPgSTh/EBA8MBMJOxAY8WNgSLh9mAZC1sEsRpYT9j9uEHQ120wf3eYxVv2+4w8LcfYJOcgU8LT47xzB6Gw7kbjvGl3Zzb9oxB4kwCm+QGvA7LMWbgYTgA1MJjdpu37TADww0GNskH+LTwvzFm/MNQB9ZSDNIiT1CLRI4xMw8DM1gLM0iLAUgLXodJPCtmljE4nDvzWI6x5Jxzz3gMzyQ2W+LzPj9/8mbGNxV1uX2Hzxh+eFN2R07u+OGDN3vwaIEAYBwqHADSPGCEN1aQgHwDRMsoGAWjYBSMAgwAAHmLSdtq+emTAAAAAElFTkSuQmCC","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":true,"prefix":"","firstName":"Laura","middleName":"","lastName":"Smyth","suffix":""},{"id":632447821,"identity":"6beab80a-3b78-49d1-b5a3-e4fed20c1144","order_by":1,"name":"Marisa Canadas Garre","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Marisa","middleName":"Canadas","lastName":"Garre","suffix":""},{"id":632447822,"identity":"005c4098-0042-40c8-969b-2bdb16e24b29","order_by":2,"name":"Claire Hill","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Hill","suffix":""},{"id":632447823,"identity":"0f7df927-e3ee-4950-8f44-b8cd31b32933","order_by":3,"name":"Claire Potter","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Claire","middleName":"","lastName":"Potter","suffix":""},{"id":632447828,"identity":"00a75a40-2f9d-4c89-976f-787c13c63191","order_by":4,"name":"Ian Young","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Ian","middleName":"","lastName":"Young","suffix":""},{"id":632447829,"identity":"fa32c830-0972-4a8a-9ce9-fe61b8f6f6f2","order_by":5,"name":"Bernadette McGuinness","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Bernadette","middleName":"","lastName":"McGuinness","suffix":""},{"id":632447831,"identity":"66ab8c13-4b20-4125-a310-92e26b9ecfea","order_by":6,"name":"Frank Kee","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Kee","suffix":""},{"id":632447836,"identity":"e11e51c4-3b3a-470b-aae3-339414dda15b","order_by":7,"name":"Amy Jayne McKnight","email":"","orcid":"","institution":"Queen's University Belfast","correspondingAuthor":false,"prefix":"","firstName":"Amy","middleName":"Jayne","lastName":"McKnight","suffix":""}],"badges":[],"createdAt":"2026-03-09 16:10:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9075179/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9075179/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805038,"identity":"ab42e37a-c648-494b-ba40-c241844c5997","added_by":"auto","created_at":"2026-05-08 15:24:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":169632,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9075179/v1/8ac5d4d9-b647-4fd2-bf00-19c82309bc2d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic array data from the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA)","fulltext":[{"header":"Objective","content":"\u003cp\u003eNorthern Ireland\u0026rsquo;s largest population cohort collecting longitudinal data from a sample of the over-50s population (n=8,283) was launched in 2012 as the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) [1], [2]. Throughout the recruitment, spouses or partners of invited individuals who resided in the same home at time of interview, who were under the age of 50 (n=195, n=8,478 total population) were also invited to take part. A strong focus was placed on the generation of molecular biomarkers as part of the Wave 1 data collection and analysis. The genetic data generated forms a key part of this and is described in this data note.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGenetic data was generated using the Illumina Infinium CoreExome-24 array (Illumina, USA) for 3,266 individuals and is available for 2,978 NICOLA participants following initial quality control for 551,839 directly genotyped and 18,148,478 imputed single nucleotide polymorphisms (SNPs). Summary statistics for the association of these gene polymorphisms with ~30 phenotypes have been generated to date and are available as derived variables from the NICOLA Data Access Committee[3].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe genetic .bam, .bim and .fam data files are available through data access requests made to the European Genome-Phenome Archive using study ID EGAS00001007915, dataset ID EGAD00010002762.\u003c/p\u003e"},{"header":"Data description ","content":"\u003cp\u003e\u003cstrong\u003eNICOLA Sample Collection and Preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNICOLA participants who consented to the in-person health assessment element of data collection provided blood samples which were collected in EDTA tubes at the clinical research facility of Queen\u0026rsquo;s University Belfast. Sample processing using aseptic technique was conducted in laboratories on the same floor of the facility; blood tubes were centrifuged at 3000 rpm for 10 minutes at 4\u0026deg;C before a 1 mL pipette was used to carefully remove each buffy coat before being frozen at -80\u0026deg;C.\u0026nbsp;DNA was extracted, quantified using PicoGreen, normalised to 200 ng/\u0026mu;L using 0.1 TE and stored in aliquots at -80\u0026deg;C to minimise freeze/thaw cycles. Critically important for this data, all samples were processed in a single recruitment laboratory with genetic data generated in a single laboratory. All wet-lab typing included standard experimental controls including replicate samples (n=12), control samples of known genotype blinded to the genotyping laboratory (n=7) and no template controls (n=4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement of Genetic Biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were genotyped by Eurofins Scientific (https://www.eurofinsgenomics.eu). Genotype data (n=551,839 markers directly typed) was generated using the Illumina Infinium CoreExome-24 on an iScan for two batches composed of 2,799 and 467 participants respectively (n=3,266). For the purposes of analysis, each batch was processed separately as they were genotyped at different timepoints.\u003c/p\u003e\n\u003cp\u003eGenomeStudio\u0026reg; Software\u0026rsquo;s Genotyping Module was used alongside the Genome Reference Consortium Human Build 37 (GRCh37) for analysis. Each SNP was called using clustering methods with an independent visual review of any outliers to more accurately call genotypes. Samples were excluded if they had insufficient DNA quality, quantity, or poor genotype concordance with previous\u0026nbsp;genotypes during careful evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality Control and Imputation of Genetic Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe QC of genotyped data was performed in PLINK 1.90 beta[4]. QC included removal of samples with a call rate \u0026lt;95%, heterozygosity, principal component analysis outliers, sex mismatches and duplicates. Up to second-degree related individuals were also removed by eliminating one individual from each pair with an identity by descent value \u0026gt;0.1875[5], [6]. Variants with a call rate \u0026ge;98%, Hardy-Weinberg equilibrium p\u0026gt;10\u003csup\u003e-6\u003c/sup\u003e and minor allele frequency \u0026lt;0.0001 were removed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFiles were prepared for imputation using the Haplotype Reference Consortium (HRC)\u003cem\u003e/\u003c/em\u003e 1000 Genomes \u003cem\u003eImputation Preparation and Checking Tool\u003c/em\u003e, developed by Will Rayner[7], before imputation to the 1000 Genomes Phase3 v5 (1KGP3) and HRC r1.1 2016 reference panels using the Michigan Imputation Server[8]. A minor allele cut-off of 5 and imputation quality of 0.3 was applied to imputed files; monomorphic markers were removed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUpon completion of initial QC, 2,526 samples for batch 1 and 452 samples for batch 2 remained in the cohort (n=2,978 total). An overview of the datafiles is included in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Overview of data files/data sets.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of data file/data set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFile types\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(file extension)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eData repository and identifier (DOI or accession number)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .bed file for batch 1 of the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.bed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .bim file for batch 1 of NICOLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.bim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .fam file for batch 1 of NICOLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.fam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .bed file for batch 2 of NICOLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.bed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .bim file for batch 2 of NICOLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.bim\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eData file 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eGenetic data .fam file for batch 2 of NICOLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e.fam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eEGA ID: EGAD00010002762\u003c/p\u003e\n \u003cp\u003eDAC ID:\u003c/p\u003e\n \u003cp\u003eEGAC00001003504\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;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExemplar Genetic Data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGWAS analysis of the NICOLA dataset has contributed to an international GWAS examining human height[9], an international multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels[10], [11], [12] and two GWAS examining age-related macular degeneration[13], [14]. The associated summary statistics have been returned to the NICOLA bioresource and are available for researchers within our data access agreements.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cul\u003e\n \u003cli\u003eGenetic data is not available for all individuals who participated in NICOLA, rather is available for a sub-set of the larger cohort from Wave 1. All individuals who attended the health assessment and provided a venous blood sample with permission for genetic analysis were submitted for genotyping.\u003c/li\u003e\n \u003cli\u003eDespite being a nationally representative cohort, there is a limitation in terms of the response bias which has an over-representation of individuals with higher education levels.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eDespite the study being designed to be representative of the over 50s population in NI using a random selector for participants based on residential address, there is potential for selection bias to have attracted more healthy individuals.\u003c/li\u003e\n \u003cli\u003eDue to the small number of ethnic minorities included within NICOLA, no statistically robust estimates could be generated for this population subset.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e1KGP3 - 1000 Genomes Phase3 v5\u003c/p\u003e\n\u003cp\u003eDNA \u0026ndash; deoxyribose nucleic acid\u003c/p\u003e\n\u003cp\u003eEDTA - ethylenediaminetetraacetic acid\u003c/p\u003e\n\u003cp\u003eGRCh37 - Genome Reference Consortium Human Build 37\u003c/p\u003e\n\u003cp\u003eHRC - Haplotype Reference Consortium\u003c/p\u003e\n\u003cp\u003eNICOLA - Northern Ireland COhort for the Longitudinal study of Ageing\u003c/p\u003e\n\u003cp\u003eSNP \u0026ndash; single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eQC \u0026ndash; quality control\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003cbr\u003e\u003c/strong\u003eEthical approval for the study was obtained from the School of Medicine, Dentistry and Biomedical Sciences Ethics Committee, Queen\u0026rsquo;s University Belfast. Consent for biological samples has been collected in accordance with the Human Tissue Act (2004).\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all participants in the study - Ethical approval for the study was obtained from the School of Medicine, Dentistry and Biomedical Sciences Ethics Committee, Queen\u0026rsquo;s University Belfast (Ref 12/23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003cbr\u003e\u003c/strong\u003eAll data is anonymised.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e \u003cbr\u003e The data described in this Data note can be freely and openly accessed through the European Genome-Phenome Archive under the study accession number EGAS00001007915.\u003cem\u003e\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e\u003c/strong\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003cbr\u003e\u003c/strong\u003eThe Atlantic Philanthropies, the Economic and Social Research Council (ES/L0084559/1), the UKCRC Centre of Excellence for Public Health Northern Ireland, the Centre for Ageing Research and Development in Ireland, the Office of the First Minister and Deputy First Minister, the Health and Social Care Research and Development Division of the Public Health Agency, the Wellcome Trust/Wolfson Foundation and Queen\u0026rsquo;s University Belfast provided core financial support for NICOLA Waves 1 and 2. The authors alone are responsible for the interpretation of the data and any views or opinions presented are solely those of the authors and do not necessarily represent those of the NICOLA Study team.\u003c/p\u003e\n\u003cp\u003eWe also acknowledge individual funding awards from, the Northern Ireland Kidney Research Fund, the Medical Research Council (MC_PC_20026) and the Public Health Agency R\u0026amp;D Division (STL/5569/19), Science Foundation Ireland (SFI15/US/B3130), NIH R01_DK105154, a Science Foundation Ireland and the Department for the Economy, Northern Ireland US partnership award 15/IA/3152, the Economic and Social Research Council (ES/L008459/1) and Welcome Trust/ Health Research Board Ireland\u0026rsquo;s Irish Clinical Academic Training (ICAT) Fellowship grant number 203930/B/16/Z.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003cbr\u003e AJM, BM, FK and IY were responsible for study concept, design, and funding acquisition. LJS and MCG prepared the data. LJS, MCG, CH and CP prepared the initial drafts of the manuscript. LS, CH, CP, and AJM carried out subsequent revisions. All authors contributed to the critical revision of the manuscript, proof reading and approval of the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the participants of the NICOLA Study, and the whole NICOLA team, which includes nursing staff, research scientists, clerical staff, computer and laboratory technicians, managers and receptionists.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eC. E. Neville \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA): health assessment protocol, participant profile and patterns of participation,\u0026rdquo; \u003cem\u003eBMC Public Health\u003c/em\u003e, vol. 23, no. 1, pp. 1\u0026ndash;19, 2023, doi: 10.1186/s12889-023-15355-x.\u003c/li\u003e\n\u003cli\u003eC. Neville \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Cohort Profile: The Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA),\u0026rdquo; \u003cem\u003eInt J Epidemiol\u003c/em\u003e, pp. 1\u0026ndash;11, 2023, doi: 10.1093/ije/dyad026.\u003c/li\u003e\n\u003cli\u003eM. Canadas-Garre, L. Smyth, C. Neville, F. Kee, J. Woodside, and A. McKnight, \u0026ldquo;Objective Measures of Health and Wellbeing of Older Adults in Northern Ireland The NICOLA Study Wave 1; Chapter 8 Molecular Biomarkers ,\u0026rdquo; Belfast, Jun. 2021.\u003c/li\u003e\n\u003cli\u003eC. C. Chang, C. C. Chow, L. C. A. M. Tellier, S. Vattikuti, S. M. Purcell, and J. J. Lee, \u0026ldquo;Second-generation PLINK: Rising to the challenge of larger and richer datasets,\u0026rdquo; \u003cem\u003eGigascience\u003c/em\u003e, vol. 4, no. 1, pp. 1\u0026ndash;16, 2015, doi: 10.1186/s13742-015-0047-8.\u003c/li\u003e\n\u003cli\u003eS. H. Slifer, \u0026ldquo;Current Protocols in Human Genetics Current Protocols in Human Genetics UNIT PLINK: Key Functions for Data Analysis,\u0026rdquo; \u003cem\u003eCurr Protoc Hum Genet\u003c/em\u003e, vol. 97, no. 1, p. e59, 2018, doi: 10.1002/cphg.59.\u003c/li\u003e\n\u003cli\u003eC. A. Anderson, F. H. Pettersson, G. M. Clarke, L. R. Cardon, A. P. Morris, and K. T. Zondervan, \u0026ldquo;Data quality control in genetic case-control association studies,\u0026rdquo; \u003cem\u003eNat Protoc\u003c/em\u003e, vol. 5, no. 9, pp. 1564\u0026ndash;1573, 2010, doi: 10.1038/nprot.2010.116.\u003c/li\u003e\n\u003cli\u003eW. Rayner, \u0026ldquo;McGarthy Group Tools.\u0026rdquo; Accessed: Jul. 18, 2023. [Online]. Available: https://www.well.ox.ac.uk/~wrayner/tools/\u003c/li\u003e\n\u003cli\u003eS. Das \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Next-generation genotype imputation service and methods,\u0026rdquo; \u003cem\u003eNat Genet\u003c/em\u003e, vol. 48, no. 10, pp. 1284\u0026ndash;1287, 2016, doi: 10.1038/ng.3656.\u003c/li\u003e\n\u003cli\u003eL. Yengo \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;A saturated map of common genetic variants associated with human height,\u0026rdquo; \u003cem\u003eNature\u003c/em\u003e, vol. 610, no. 7933, pp. 704\u0026ndash;712, 2022, doi: 10.1038/s41586-022-05275-y.\u003c/li\u003e\n\u003cli\u003eS. E. Graham \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The power of genetic diversity in genome-wide association studies of lipids,\u0026rdquo; \u003cem\u003eNature\u003c/em\u003e, vol. 600, no. 7890, pp. 675\u0026ndash;679, 2021, doi: 10.1038/s41586-021-04064-3.\u003c/li\u003e\n\u003cli\u003eS. Ramdas \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids,\u0026rdquo; \u003cem\u003eAm J Hum Genet\u003c/em\u003e, vol. 109, no. 8, pp. 1366\u0026ndash;1387, 2022, doi: 10.1016/j.ajhg.2022.06.012.\u003c/li\u003e\n\u003cli\u003eS. Kanoni \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis,\u0026rdquo; \u003cem\u003eGenome Biol\u003c/em\u003e, vol. 23, no. 1, pp. 1\u0026ndash;42, 2022, doi: 10.1186/s13059-022-02837-1.\u003c/li\u003e\n\u003cli\u003eR. E. Hogg \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence and risk factors for age-related macular degeneration in a population-based cohort study of older adults in Northern Ireland using multimodal imaging: NICOLA Study,\u0026rdquo; \u003cem\u003eBritish Journal of Ophthalmology\u003c/em\u003e, vol. 107, no. 12, pp. 1873\u0026ndash;1879, 2023, doi: 10.1136/bjo-2021-320469.\u003c/li\u003e\n\u003cli\u003eT. W. Winkler \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease,\u0026rdquo; \u003cem\u003eBMC Med Genomics\u003c/em\u003e, vol. 13, no. 1, pp. 1\u0026ndash;18, 2020, doi: 10.1186/s12920-020-00760-7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ageing , Association , Genetic , Longitudinal , Molecular , NICOLA , Northern Ireland , Population , SNP","lastPublishedDoi":"10.21203/rs.3.rs-9075179/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9075179/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNorthern Ireland’s largest national population cohort over 50 years of age, with a focus on the collection of longitudinal data including computer-assisted personal interviews, self-completed questionnaires and in-person health assessments takes form as the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA). Data from 8,283 individuals over 50 years of age was collected from individuals within NICOLA’s first wave of data collection for this cohort. Together, the data collected aims to help researchers explore how social, economic, health, lifestyle, and biological factors influence healthy ageing. NICOLA places a strong emphasis on molecular biomarkers, with a subset of 3,471 participants (44%) undergoing health assessments at wave 1, including the collection of blood samples specifically for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e: \u003cbr\u003e\n Using blood-derived DNA collected from individuals within NICOLA, genetic data was generated to enable research projects associated with rich cross-sectional and longitudinal data within NICOLA to explore genetic connections to the health, lifestyle, environment and financial situations of people as they grow older.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData description: \u003c/strong\u003e\u003cbr\u003e\nGenetic data was generated using the Illumina Infinium CoreExome-24 array (Illumina, USA) and is available for 2,978 NICOLA participants with 551,839 directly genotyped and 18,148,478 imputed single nucleotide polymorphisms following initial quality control.\u003c/p\u003e","manuscriptTitle":"Genetic array data from the Northern Ireland COhort for the Longitudinal study of Ageing (NICOLA)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 13:43:31","doi":"10.21203/rs.3.rs-9075179/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-30T12:22:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T13:03:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36398391589869338990497233400431490784","date":"2026-04-29T09:27:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T12:03:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-18T14:32:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-18T14:31:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Research Notes","date":"2026-03-09T16:01:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-research-notes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"resn","sideBox":"Learn more about [BMC Research Notes](http://bmcresnotes.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/resn/default.aspx","title":"BMC Research Notes","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9dd4342-e089-4fdc-a51d-697678f3c4bd","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-04-30T12:22:17+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T13:43:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 13:43:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9075179","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9075179","identity":"rs-9075179","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}