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Teh LK, Subramaniam V, Tuan Abdu Aziz TA, Lee LS, Ismail MI, Yu CY, Ang GY, James Johari R, Ismet RI, Sahak NS, Ahmad A, Rahman TA, Nor Ghazali FM, Shaari S, Omar M, Ismail AI, Md Isa K, Salleh H, Salleh MZ. Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations. Drug Metab Pharmacokinet 2016; 31:304-13. [PMID: 27325019 DOI: 10.1016/j.dmpk.2016.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2016] [Revised: 04/04/2016] [Accepted: 04/20/2016] [Indexed: 12/30/2022]
Abstract
We conducted a systematic characterization of CYP2C9 variants in 61 Orang Asli and 96 Singaporean Malays using the whole genome sequences data and compared the variants with the other 11 HapMap populations. The frequency of rs1057910 (CYP2C9*3) is the highest in the Orang Asli compared to other populations. Three alleles with clinical implication were detected in the Orang Asli while 2 were found in the Singaporean Malays. Large numbers of the Orang Asli are predicted to have reduced metabolic capacity and therefore they would require a lower dose of drugs which are metabolized by CYP2C9. They are also at increased risks of adverse effects and therapeutic failures. A large number of CYP2C9 variants in the Orang Asli were not in the Hardy Weinberg Equilibrium which could be due to small sample size or mutations that disrupt the equilibrium of allele frequencies. In conclusion, different polymorphism patterns, allele frequencies, genotype frequencies and LD blocks are observed between the Orang Asli, the Singaporean Malays and the other populations. The study provided new information on the genetic polymorphism of CYP2C9 which is important for the implementation of precision medicine for the Orang Asli.
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Affiliation(s)
- Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Malaysia.
| | - Vinothini Subramaniam
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | | | - Lian Shien Lee
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Mohamed Izwan Ismail
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Choo Yee Yu
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Geik Yong Ang
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Richard James Johari
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Malaysia
| | - Rose Iszati Ismet
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Noor Saadah Sahak
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia
| | - Aminuddin Ahmad
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Malaysia
| | | | | | | | - Mustaffa Omar
- Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia
| | | | | | - Hood Salleh
- Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia; Institut Alam Sekitar dan Pembangunan (LESTARI), Universiti Kebangsaan Malaysia (UKM), Malaysia
| | - Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Malaysia.
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Mohamad N, Ismet RI, Rofiee M, Bannur Z, Hennessy T, Selvaraj M, Ahmad A, Nor F, Abdul Rahman T, Md Isa K, Ismail A, Teh LK, Salleh MZ. Metabolomics and partial least square discriminant analysis to predict history of myocardial infarction of self-claimed healthy subjects: validity and feasibility for clinical practice. J Clin Bioinforma 2015; 5:3. [PMID: 25806102 PMCID: PMC4371619 DOI: 10.1186/s13336-015-0018-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Accepted: 02/27/2015] [Indexed: 02/07/2023] Open
Abstract
Background The dynamics of metabolomics in establishing a prediction model using partial least square discriminant analysis have enabled better disease diagnosis; with emphasis on early detection of diseases. We attempted to translate the metabolomics model to predict the health status of the Orang Asli community whom we have little information. The metabolite expressions of the healthy vs. diseased patients (cardiovascular) were compared. A metabotype model was developed and validated using partial least square discriminant analysis (PLSDA). Cardiovascular risks of the Orang Asli were predicted and confirmed by biochemistry profiles conducted concurrently. Results Fourteen (14) metabolites were determined as potential biomarkers for cardiovascular risks with receiver operating characteristic of more than 0.7. They include 15S-HETE (AUC = 0.997) and phosphorylcholine (AUC = 0.995). Seven Orang Asli were clustered with the patients’ group and may have ongoing cardiovascular risks and problems. This is supported by biochemistry tests results that showed abnormalities in cholesterol, triglyceride, HDL and LDL levels. Conclusions The disease prediction model based on metabolites is a useful diagnostic alternative as compared to the current single biomarker assays. The former is believed to be more cost effective since a single sample run is able to provide a more comprehensive disease profile, whilst the latter require different types of sampling tubes and blood volumes. Electronic supplementary material The online version of this article (doi:10.1186/s13336-015-0018-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Nornazliya Mohamad
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - Rose Iszati Ismet
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - MohdSalleh Rofiee
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - Zakaria Bannur
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - Thomas Hennessy
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia ; Life Sciences & Diagnostics Group, Translational Research Institute, Brisbane, Australia
| | - Manikandan Selvaraj
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - Aminuddin Ahmad
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor Malaysia
| | - FadzilahMohd Nor
- Faculty of Medicine, Universiti Teknologi MARA (UiTM), Sungai Buloh, Selangor Malaysia
| | | | - Kamarudzaman Md Isa
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - AdzroolIdzwan Ismail
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia ; Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Selangor 42300 Malaysia
| | - Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Puncak Alam Malaysia, Selangor 42300 Malaysia ; Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Bandar Puncak Alam, Selangor 42300 Malaysia
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Salleh MZ, Teh LK, Lee LS, Ismet RI, Patowary A, Joshi K, Pasha A, Ahmed AZ, Janor RM, Hamzah AS, Adam A, Yusoff K, Hoh BP, Hatta FHM, Ismail MI, Scaria V, Sivasubbu S. Systematic pharmacogenomics analysis of a Malay whole genome: proof of concept for personalized medicine. PLoS One 2013; 8:e71554. [PMID: 24009664 PMCID: PMC3751891 DOI: 10.1371/journal.pone.0071554] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2012] [Accepted: 07/01/2013] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND With a higher throughput and lower cost in sequencing, second generation sequencing technology has immense potential for translation into clinical practice and in the realization of pharmacogenomics based patient care. The systematic analysis of whole genome sequences to assess patient to patient variability in pharmacokinetics and pharmacodynamics responses towards drugs would be the next step in future medicine in line with the vision of personalizing medicine. METHODS Genomic DNA obtained from a 55 years old, self-declared healthy, anonymous male of Malay descent was sequenced. The subject's mother died of lung cancer and the father had a history of schizophrenia and deceased at the age of 65 years old. A systematic, intuitive computational workflow/pipeline integrating custom algorithm in tandem with large datasets of variant annotations and gene functions for genetic variations with pharmacogenomics impact was developed. A comprehensive pathway map of drug transport, metabolism and action was used as a template to map non-synonymous variations with potential functional consequences. PRINCIPAL FINDINGS Over 3 million known variations and 100,898 novel variations in the Malay genome were identified. Further in-depth pharmacogenetics analysis revealed a total of 607 unique variants in 563 proteins, with the eventual identification of 4 drug transport genes, 2 drug metabolizing enzyme genes and 33 target genes harboring deleterious SNVs involved in pharmacological pathways, which could have a potential role in clinical settings. CONCLUSIONS The current study successfully unravels the potential of personal genome sequencing in understanding the functionally relevant variations with potential influence on drug transport, metabolism and differential therapeutic outcomes. These will be essential for realizing personalized medicine through the use of comprehensive computational pipeline for systematic data mining and analysis.
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Affiliation(s)
- Mohd Zaki Salleh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Lay Kek Teh
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Lian Shien Lee
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Rose Iszati Ismet
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Ashok Patowary
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Kandarp Joshi
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Ayesha Pasha
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Azni Zain Ahmed
- Institute of Science, Universiti Teknologi MARA (UiTM) Malaysia, Shah Alam, Selangor, Malaysia
| | - Roziah Mohd Janor
- Faculty of Computer and Mathematical Science, Universiti Teknologi MARA (UiTM) Malaysia, Shah Alam, Selangor, Malaysia
| | - Ahmad Sazali Hamzah
- Institute of Science, Universiti Teknologi MARA (UiTM) Malaysia, Shah Alam, Selangor, Malaysia
| | - Aishah Adam
- Faculty of Pharmacy, Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Khalid Yusoff
- Faculty of Medicine, Universiti Teknologi MARA (UiTM) Malaysia, Sg Buloh, Selangor, Malaysia
| | - Boon Peng Hoh
- Insitute of Medical Molecular Biotechnology (IMMB), Faculty of Medicine, Universiti Teknologi MARA (UiTM) Malaysia, Sg Buloh, Selangor, Malaysia
| | | | - Mohamad Izwan Ismail
- Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM) Malaysia, Puncak Alam, Selangor, Malaysia
| | - Vinod Scaria
- GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
| | - Sridhar Sivasubbu
- Genomics and Molecular Medicine, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India
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