Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database.
<h4>Objectives</h4>To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD).<h4>Background</h4>Cerebrovascular disease occurs because of a complex interplay...
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2022-01-01
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| author | Fahad Alkhalfan Alex Gyftopoulos Yi-Ju Chen Charles H Williams James A Perry Charles C Hong |
| author_facet | Fahad Alkhalfan Alex Gyftopoulos Yi-Ju Chen Charles H Williams James A Perry Charles C Hong |
| author_sort | Fahad Alkhalfan |
| collection | DOAJ |
| description | <h4>Objectives</h4>To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD).<h4>Background</h4>Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown.<h4>Methods</h4>Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD.<h4>Results</h4>We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10-8) and had a minor allele frequency of > 0.5%.<h4>Discussion</h4>Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies. |
| format | Article |
| id | doaj-art-aec1dca5fa254a0a84f4a3247549b3fc |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-aec1dca5fa254a0a84f4a3247549b3fc2025-08-20T03:44:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01178e027321710.1371/journal.pone.0273217Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database.Fahad AlkhalfanAlex GyftopoulosYi-Ju ChenCharles H WilliamsJames A PerryCharles C Hong<h4>Objectives</h4>To utilize the UK Biobank to identify genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease (CeVD).<h4>Background</h4>Cerebrovascular disease occurs because of a complex interplay between vascular, environmental, and genetic factors. It is the second leading cause of disability worldwide. Understanding who may be genetically predisposed to cerebrovascular disease can help guide preventative efforts. Moreover, there is considerable interest in the use of real-world data, such as EHR (electronic health records) to better understand disease mechanisms and to discover new treatment strategies, but whether ICD10-based diagnosis can be used to study CeVD genetics is unknown.<h4>Methods</h4>Using the UK Biobank, we conducted a genome-wide association study (GWAS) where we analyzed the genomes of 11,155 cases and 122,705 controls who were sex, age and ancestry-matched in a 1:11 case: control design. Genetic variants were identified by Plink's firth logistic regression and assessed for association with the ICD10 codes corresponding to CeVD.<h4>Results</h4>We identified two groups of SNPs closely linked to PITX2 and LRRTM4 that were significantly associated with CeVD in this study (p < 5 x 10-8) and had a minor allele frequency of > 0.5%.<h4>Discussion</h4>Disease assignment based on ICD10 codes may underestimate prevalence; however, for CeVD, this does not appear to be the case. Compared to the age- and sex-matched control population, individuals with CeVD were more frequently diagnosed with comorbid conditions, such as hypertension, hyperlipidemia & atrial fibrillation or flutter, confirming their contribution to CeVD. The UK Biobank based ICD10 study identified 2 groups of variants that were associated with CeVD. The association between PITX2 and CeVD is likely explained by the increased rates of atrial fibrillation and flutter. While the mechanism explaining the relationship between LRRTM4 and CeVD is unclear, this has been documented in previous studies.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0273217&type=printable |
| spellingShingle | Fahad Alkhalfan Alex Gyftopoulos Yi-Ju Chen Charles H Williams James A Perry Charles C Hong Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. PLoS ONE |
| title | Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. |
| title_full | Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. |
| title_fullStr | Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. |
| title_full_unstemmed | Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. |
| title_short | Identifying genetic variants associated with the ICD10 (International Classification of Diseases10)-based diagnosis of cerebrovascular disease using a large-scale biomedical database. |
| title_sort | identifying genetic variants associated with the icd10 international classification of diseases10 based diagnosis of cerebrovascular disease using a large scale biomedical database |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0273217&type=printable |
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