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Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors
Introduction: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic diseases. However, warfarin use is complicated by its narrow therapeutic range and inter-individual variability in the starting dose required to achieve a stable international normal...
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| Format: | Thesis |
| Language: | English |
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Department of Clinical Laboratory Sciences
2023
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| _version_ | 1867613290721443840 |
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| access_status_str | Open Access |
| author | Ndadza, Arinao |
| author2 | Dandara, Collet |
| author_browse | Dandara, Collet Ndadza, Arinao |
| author_facet | Dandara, Collet Ndadza, Arinao |
| author_sort | Ndadza, Arinao |
| collection | Thesis |
| description | Introduction: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic diseases. However, warfarin use is complicated by its narrow therapeutic range and inter-individual variability in the starting dose required to achieve a stable international normalised ratio (INR). Warfarin is initiated clinically at 5mg/day then subsequent doses are adjusted accordingly to achieve a stable targeted INR. However, inter-individual variability in response to the warfarin starting dose has been observed and this is reported to be attributed to by various genetic and nongenetic factors. Non-genetics factors implicated in the warfarin dose variability include age, gender, body weight, comorbidities and concomitant drugs. Genetic factors affecting warfarin dose variability include variation in genes encoding the warfarin metabolising enzymes and targeted proteins. Genetic variants in CYP2C9 and VKORC1 have been extensively studied on how they affect warfarin dose variability, culminating in several dosing algorithms incorporating genetic (i.e., CYP2C9*2, CYP2C9*3 and VKORC1 g.-1639G>A) and non-genetic factors (i.e., age, body surface area, amiodarone, race, targeted INR, smoking and thromboembolism). However, these studies have often excluded African populations, therefore missing variants that might be important in the prediction of warfarin doses among Africans. Data on variants that specifically affect warfarin dose variability among Africans is lacking, with no dosing algorithms tailored specifically for Africans developed to date. Thus, the main aim of the study is to conduct a comprehensive evaluation of important genetic and non-genetic factors affecting warfarin response, and further make recommendations on variables important for the development of appropriate algorithms for warfarin dosing among black Africans and the Mixed Ancestry population group in Southern Africa. Method: A total of 302 black Africans and 277 Mixed Ancestry adults undergoing warfarin treatment were recruited at INR clinics in the Western Cape Province, South Africa and Harare, Zimbabwe. Their DNA samples were extracted and utilised for downstream analyses. A total of 73 candidate variants involved in either pharmacokinetics or pharmacodynamics of warfarin, were genetically characterised using a combination of allelic discrimination, Sanger sequencing, restriction fragment length polymorphism and iPLEX PGx74 Mass Array platform. Various statistical packages in STATA, R, haploview and plink were employed to determine frequency distribution, linkage disequilibrium and haplotype mapping of the studied genetic variants. Furthermore, genetic and non-genetic variables were correlated with warfarin maintenance dose and their cumulative effect on warfarin dose variability measured through a multivariate step-wise regression analysis in both the black African and Mixed Ancestry cohorts. Whole exome sequencing was done using the ion torrent Sequence ion S5 system in selected black African individuals presenting with extreme phenotypes (i.e., very low dose or very high dose) but who did not harbour variants known to significantly affect warfarin dose requirements. A workflow which applied various bioinformatics tools was employed for the analyses of the resultant raw BAM files, subsequently, population structure and frequency distribution patterns were described among our cohort and individuals in the 1000 Genomes project. Specific variants identified through WES were prioritised according to clinical significance and further genotyped in an enhanced sample size of 252 black Africans, to confirm their effect on warfarin dose requirements. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/38521 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:48.261Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Clinical Laboratory Sciences |
| publisherStr | Department of Clinical Laboratory Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/38521 Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors Ndadza, Arinao Dandara, Collet Ntsekhe Mpiko Wonkam Ambroise Warfarin Genomic Introduction: Warfarin is the most widely prescribed anticoagulant for the prevention and treatment of thromboembolic diseases. However, warfarin use is complicated by its narrow therapeutic range and inter-individual variability in the starting dose required to achieve a stable international normalised ratio (INR). Warfarin is initiated clinically at 5mg/day then subsequent doses are adjusted accordingly to achieve a stable targeted INR. However, inter-individual variability in response to the warfarin starting dose has been observed and this is reported to be attributed to by various genetic and nongenetic factors. Non-genetics factors implicated in the warfarin dose variability include age, gender, body weight, comorbidities and concomitant drugs. Genetic factors affecting warfarin dose variability include variation in genes encoding the warfarin metabolising enzymes and targeted proteins. Genetic variants in CYP2C9 and VKORC1 have been extensively studied on how they affect warfarin dose variability, culminating in several dosing algorithms incorporating genetic (i.e., CYP2C9*2, CYP2C9*3 and VKORC1 g.-1639G>A) and non-genetic factors (i.e., age, body surface area, amiodarone, race, targeted INR, smoking and thromboembolism). However, these studies have often excluded African populations, therefore missing variants that might be important in the prediction of warfarin doses among Africans. Data on variants that specifically affect warfarin dose variability among Africans is lacking, with no dosing algorithms tailored specifically for Africans developed to date. Thus, the main aim of the study is to conduct a comprehensive evaluation of important genetic and non-genetic factors affecting warfarin response, and further make recommendations on variables important for the development of appropriate algorithms for warfarin dosing among black Africans and the Mixed Ancestry population group in Southern Africa. Method: A total of 302 black Africans and 277 Mixed Ancestry adults undergoing warfarin treatment were recruited at INR clinics in the Western Cape Province, South Africa and Harare, Zimbabwe. Their DNA samples were extracted and utilised for downstream analyses. A total of 73 candidate variants involved in either pharmacokinetics or pharmacodynamics of warfarin, were genetically characterised using a combination of allelic discrimination, Sanger sequencing, restriction fragment length polymorphism and iPLEX PGx74 Mass Array platform. Various statistical packages in STATA, R, haploview and plink were employed to determine frequency distribution, linkage disequilibrium and haplotype mapping of the studied genetic variants. Furthermore, genetic and non-genetic variables were correlated with warfarin maintenance dose and their cumulative effect on warfarin dose variability measured through a multivariate step-wise regression analysis in both the black African and Mixed Ancestry cohorts. Whole exome sequencing was done using the ion torrent Sequence ion S5 system in selected black African individuals presenting with extreme phenotypes (i.e., very low dose or very high dose) but who did not harbour variants known to significantly affect warfarin dose requirements. A workflow which applied various bioinformatics tools was employed for the analyses of the resultant raw BAM files, subsequently, population structure and frequency distribution patterns were described among our cohort and individuals in the 1000 Genomes project. Specific variants identified through WES were prioritised according to clinical significance and further genotyped in an enhanced sample size of 252 black Africans, to confirm their effect on warfarin dose requirements. 2023-09-11T14:27:22Z 2023-09-11T14:27:22Z 2023 2023-09-11T14:08:25Z Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/38521 eng application/pdf Department of Clinical Laboratory Sciences Faculty of Health Sciences |
| spellingShingle | Warfarin Genomic Ndadza, Arinao Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| thesis_degree_str | Doctoral |
| title | Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| title_full | Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| title_fullStr | Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| title_full_unstemmed | Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| title_short | Pharmacogenomics of warfarin: comprehensive evaluation of important warfarin genomic response factors |
| title_sort | pharmacogenomics of warfarin comprehensive evaluation of important warfarin genomic response factors |
| topic | Warfarin Genomic |
| url | http://hdl.handle.net/11427/38521 |
| work_keys_str_mv | AT ndadzaarinao pharmacogenomicsofwarfarincomprehensiveevaluationofimportantwarfaringenomicresponsefactors |