| Autor: |
Kristiina Rannikmäe, Konrad Rawlik, Amy C. Ferguson, Nikos Avramidis, Muchen Jiang, Nicola Pirastu, Xia Shen, Emma Davidson, Rebecca Woodfield, Rainer Malik, Martin Dichgans, Albert Tenesa, Cathie Sudlow |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Neurology, Vol 12 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1664-2295 |
| DOI: |
10.3389/fneur.2021.787107 |
| Popis: |
BackgroundStroke in UK Biobank (UKB) is ascertained via linkages to coded administrative datasets and self-report. We studied the accuracy of these codes using genetic validation.MethodsWe compiled stroke-specific and broad cerebrovascular disease (CVD) code lists (Read V2/V3, ICD-9/-10) for medical settings (hospital, death record, primary care) and self-report. Among 408,210 UKB participants, we identified all with a relevant code, creating 12 stroke definitions based on the code type and source. We performed genome-wide association studies (GWASs) for each definition, comparing summary results against the largest published stroke GWAS (MEGASTROKE), assessing genetic correlations, and replicating 32 stroke-associated loci.ResultsThe stroke case numbers identified varied widely from 3,976 (primary care stroke-specific codes) to 19,449 (all codes, all sources). All 12 UKB stroke definitions were significantly correlated with the MEGASTROKE summary GWAS results (rg.81-1) and each other (rg.4-1). However, Bonferroni-corrected confidence intervals were wide, suggesting limited precision of some results. Six previously reported stroke-associated loci were replicated using ≥1 UKB stroke definition.ConclusionsStroke case numbers in UKB depend on the code source and type used, with a 5-fold difference in the maximum case-sample size. All stroke definitions are significantly genetically correlated with the largest stroke GWAS to date. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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