Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations

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Regular article

Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations Q4

Lay Kek Teh a, b, *, Vinothini Subramaniam a, Tuan Azlin Tuan Abdu Aziz a, Lian Shien Lee a, Mohamed Izwan Ismail a, Choo Yee Yu a, Geik Yong Ang a, Mohammad Richard James Johari a, b, Rose Iszati Ismet a, Noor Saadah Sahak a, Aminuddin Ahmad c, Thuhairah Abdul Rahman c, Fadzilah Mohd Nor @ Ghazali c, SyahrulAzlin Shaari c, Mustaffa Omar d, Adzrool Idzwan Ismail e, Kamarudzaman Md. Isa f, Hood Salleh d, g, Mohd Zaki Salleh a, b, * a

Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA (UiTM), Malaysia Faculty of Pharmacy, Universiti Teknologi MARA (UiTM), Malaysia c Faculty of Medicine, Universiti Teknologi MARA (UiTM), Malaysia d Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia e Faculty of Art and Design (FSSR), Universiti Teknologi MARA (UiTM), Malaysia f Faculty of Communication and Media, University Selangor (Unisel), Malaysia g Institut Alam Sekitar dan Pembangunan (LESTARI), Universiti Kebangsaan Malaysia (UKM), Malaysia b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 March 2016 Received in revised form 4 April 2016 Accepted 20 April 2016 Available online xxx

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.

Keywords: Genetic polymorphism Interethnic Hardy Weinberg Equilibrium Orang Asli Malaysia

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© 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

1. Introduction

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CYP2C9 is one of the important cytochrome P450 enzymes that metabolizes not only xenobiotics but also endogenous compounds such as arachidonic acid, 5-hydroxytryptamine, and linoleic acid [1]. Therapeutic agents that are substrates of CYP2C9 include warfarin, phenytoin, tolbutamide, losartan, glipizide and some nonsteroidal anti-inflammatory drugs [2]. Differences in the

* Corresponding authors. E-mail addresses: [email protected], [email protected] (L.K. Teh), [email protected] (M.Z. Salleh).

metabolic activities of the CYP2C9 had been reported to result in different drug responses; from therapeutic failure due to toxicity or insufficient dose to desired therapeutics efficacy. Diminished metabolic capacity of Cytochrome p450 enzymes because of genetic polymorphisms or drugedrug interactions can lead to toxicities at normal therapeutic doses [3,4]. Studies have successfully associated CYP2C9 variants, rs1799853 (CYP2C9*2) and rs1057910 (CYP2C9*3) with poor metabolism phenotypes. Patients with these variants require lower doses of warfarin as they are at risks of prolonged bleeding time and increased incidence of severe bleeding [5e8]. Carriers of these variants were also associated with an increased risk of

http://dx.doi.org/10.1016/j.dmpk.2016.04.004 1347-4367/© 2016 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.

Please cite this article in press as: Teh LK, et al., Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations, Drug Metabolism and Pharmacokinetics (2016), http://dx.doi.org/10.1016/j.dmpk.2016.04.004

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hypoglycaemic responses when prescribed with glipizide or tolbutamide [9]. As CYP2C9 is responsible for about 90% of phenytoin metabolism, more frequent symptoms of overdose in phenytoin therapy had been observed in patients with these variants [10e13]. In addition to these 2 variants, a large number of SNPs in the regulatory and coding regions of the CYP2C9 gene have been described and most of these SNPs are associated with reduced enzyme activities when compared to wild-type. Examples of other null alleles include rs56165452 (CYP2C*4), rs28371683 (CYP2C9*5), rs933213 (CYP2C9*6), rs7900194 (CYP2C9*8) and rs28371685 (CYP2C9*11) [9,14e18]. CYP2C9 is highly polymorphic with damaging alleles that result in large variability in drug response. Studies on the interethnic differences in the types and frequencies of the alleles have received research interests as extrapolation of the data from one ethnic group to another is almost not possible. Some alleles were found present only in Caucasian and absent in the Asians while some are common in most populations. For example, rs1799853 (CYP2C9*2) has not been reported among the Asians while rs1057910 (CYP2C9*3) is present in most ethnic groups but with different percentage of frequencies [19e21]. These alleles have clinical implication with respect to choice of drugs with narrow therapeutic window for the patients of different ethnic groups. Genome data on different population have been made available by several consortiums including The 1000 Genomes Project Consortium [22] and the International HapMap Consortium [23]. With the completion of 1000 genome project, the most comprehensive whole genome data on the human genome variation is now made available. In parallel with the availability of the extensive catalog of Genome-Wide Association Studies [24] that allow association of genetic variants with risks of diseases and PharmGKB for differential drug responses [25], the implementation of personalized medicine is imminent. In this study, we aim to perform a systematic characterization of the genetic variabilities of CYP2C9 among the Orang Asli in Malaysia. The Orang Asli in Malaysia comprised of three tribes which are the Negrito, Senoi and Proto Malays. The Orang Asli are believed to be the descendant of the earliest inhabitants in Peninsular Malaysia from the migration of “Out of Africa” [26]. Each tribe is further categorized into 6 smaller sub-ethnic groups with different number of individuals. Some of the sub-ethnic groups such as the Kanaq, the Che Wong, Lanoh and Kensiu have very small number of population (80e700 individuals); while Semai is the biggest sub-ethnic group with more than 40,000 individuals [27]. In this study, we included participation of individuals from the Kanaq, Che Wong, Lanoh, Kensiu, Bateq and Semai. Many studies thus far reported on ethnic groups with bigger population and these smaller populations are seldom included. With a small number of individuals remaining, they are unique and therefore further study is now being carried out to understand the factors that influence the survivability of these sub-ethnics of the Orang Asli. In addition to mining the whole genome data of the Orang Asli, we also mined the whole genome sequence data of the Malays made available by the Singapore Genome Project [28]. Malays ethnic groups are Austronesianspeaking people inhabiting the Malay Archipelago including Malaysia, Singapore, the islands of Indonesia in the Southeast Asian region. Presently, Malays account for 13.4% of the local population in Singapore and are broadly defined as comprising descendants of indigenous Malays residing in Singapore prior to the British colonization, as well as migrants of other Southeast Asian Malay ethnic groups [28]. Most of the Malays in Singapore today are believed to have their roots from Indonesia and Malaysia. The heterogeneity of CYP2C9 among the Orang Asli was compared to the Malays as well as 11 other populations studied by the International HapMap Project [23] and as curated in the PharmGKB database [25]. The entire

genome for each individual was also scanned for new variants. We hope that this study approach would serve as a useful and effective platform in mining the genetic and genomic information in public database for pharmacogenomics applications which could be used to discover biologically and medically important genetic variants to improve the use of drugs in healthcare. In view of the importance of realizing pharmacogenetic based practice to enhance quality of care for every individual, we aim to uncover the genetic variants of CYP2C9 among the Orang Asli in Peninsular Malaysia. The data provided will help the local practitioners to optimize drug therapy containing warfarin, phenytoin or antidiabetic drugs which are substrates of CYP2C9 among the Orang Asli. 2. Materials and methods 2.1. Ethical approval The protocol of this study was approved by UiTM Research Ethics Committee of Universiti Teknologi MARA (UiTM-600-RMI (5/ 1/6/01)) and the Department of Development of Orang Asli (JAKOA), Malaysia (JAKOA.PP.30.052 Jld. 5 (62)). Written consents were obtained from the Orang Asli who participated in this study. They were explained the objectives of the study, the benefits of the study, risks involved, and how their samples would be used by the researchers. The written consent was prepared in English and Bahasa Melayu (the national language of Malaysia). Measures were also taken to protect the rights of the participants where all the information obtained were kept confidential. The volunteers' identities were secured using a random alphanumeric coding system. The demographic information of the Orang Asli is provided in Table 1. A total of 61 Orang Asli recruited were healthy and unrelated individuals. They were also able to recall their ethnicities up to their parental sets. While for the Singaporean Malays, 96 healthy unrelated subjects were included in this study. They are from the cohort of the Singapore Population Health Study and self-reported Malays whose genome sequencing were completed and successfully analyzed and reported. Their ethnicity had been confirmed up to both sets of grandparents to be Malays [28]. 2.2. Genomic DNA extraction and whole genome sequencing Blood samples were collected from the antecubital vein of the participants. DNA was extracted from the blood samples using Wizard® Genomic DNA Purification Kit (Promega, Wisconsin, USA). The Orang Asli samples were sequenced at different times of coverage. For this analysis, 35 samples were sequencing for an average of 3.55 times and the remaining samples achieved an average depth of 56 times coverage. All DNA samples were prepared using the TruSeq DNA PCR-Free Library Preparation Kit, with a target insert size of 550 bp. Sequencing was performed on 2

Table 1 Demographics of the Orang Asli.

Bateq Che Wong Kanaq Kensiu Lanoh Semai

Age Mean (years)

Male

Female

31 33.75 33.75 39.22 40 34.87

9 5 1 1 5 6 27

1 7 4 8 5 9 34

Please cite this article in press as: Teh LK, et al., Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations, Drug Metabolism and Pharmacokinetics (2016), http://dx.doi.org/10.1016/j.dmpk.2016.04.004

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instruments: The Genome Analyzer IIx for deep coverage samples (2
100 bp runs) and MiSeq for low coverage samples (2
300 bp runs) (Illumina, California, USA). 2.3. Quality assessment, whole genome assembly and alignment Qualities of the sequencing raw data were assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Where necessary, reads were trimmed using SolexaQA (version 2.5). The genome assembly and alignment of the genomes were performed according to GATK Best Practices workflow for preprocessing [29]. First, reads of the samples were aligned to the hg19 reference genome with BWA (version 0.6.1-r104) [30]. Duplicates were then marked using Picard (version 1.119) (http:// picard.sourceforge.net). The alignment files, in BAM format, were then fed to the GATK package version 2.5e2 [29,31,32] for both indel realignment and recalibration of the base quality score. Only reads with a Q score of 30 and above (>Q30) were included in the subsequent analysis. 2.4. Variant discovery and quality score recalibration

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The variants were uncovered using the GATK Best Practices workflow for variant discovery [32,33]. The variants were called individually for each genome. HaplotypeCaller tool from GATK (version 2.5-2) was used for variant discovery. False positive calls were reduced by VQSR (GATK version 2.5-2). VQSR was run separately for each variant type with different sets of resource datasets for SNPs and INDELs. All the datasets used were retrieved from the GATK resource bundle. Resources were selected according to the GATK Best Practices where SNPs were recalibrated to the databases of HapMap release 3.3, 1000G Omni release 2.5, 1000G phased SNPs, as well as to dbSNP138. The INDELs on the other hand were recalibrated with dbSNP138. Filtering was then performed for both SNPs and INDELs with truth sensitivity of 99.9. Genotype phasing and refinement were also conducted by ReadBackedPhasing for all SNPs. These steps provided Variant Call Format (vcf) phased files for each genome that encode structural genetic variants which include SNPs, indels, and genomic rearrangements. The region of the genomes that spanned the CYP2C9 gene (browser position chr10: 96698415e96749148) were extracted and saved as the annotation files in accordance to the instructions provided by UCSC genome browser tutorial [34]. Table Browser was configured to display the gene, chromosomal position and accession number of the reference sequence (RefSeq) of the genetic variants of CYP2C9 for each of the genome samples. 2.5. Retrieval of Singaporean Malays genomes The variant call formats (vcf) of the 96 genomes of the Singaporean Malays were retrieved from Singapore Sequencing Malay (http://www.statgen.nus.edu.sg/~SSMP/). Similar to the Orang Asli genomes, vcf files containing variants spanning the chromosomal position of the CYP2C9 were retrieved and saved as the annotation files in the format required by UCSC Genome Browser. The files were then subjected to analysis using the previously configured UCSC Table Browser. 2.6. Determination of the allelic frequencies and clinical annotations of genetic variants of CYP2C9 using PharmGKB The correct position of the chromosomal location of each genetic variant was further checked against database curated by PharmGKB [25]. The distribution and frequencies of the genetic

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variants deposited in PharmGKP were based on HapMap project, version 2010-08_phaseII þ I. The HapMap population included individuals from different ethnic groups such as African, Chinese, Indians, Japanese, Yoruba, Toscani, Maasai, Mexican and other Europeans Consortium. Genetic variants with clinical implication for CYP2C9 were also retrieved from PharmGKB. 2.7. Hardy Weinberg Equilibrium analysis and visualization of LD and haplotype maps The alleles which were assigned RefSeq ID (rs) for both Orang Asli and Malays were then subjected to further analysis of Hardy Weinberg Equilibrium and visualization of LD and haplotype maps using Haploview [35]. 3. Results 3.1. Types and frequencies of variants A total of 237 CYP2C9 genetic variants were assigned with Reference SNP ID (rs) for the Orang Asli while the Malays have 268 variants. In addition, we detected 829 variants of CYP2C9 which have not been assigned with rs number. The frequency distribution of the known variants among the Orang Asli was compared with the Singaporean Malays and 11 other populations as curated based on HapMap data by PharmGKB and dbSNPs. Only 42 variants were successfully curated from PharmGKB and dbSNPs with respect to the presence and frequencies of the variants in the other 11 populations. Among the 42 variants that were compared, 4 variants were not reported in the Yoruba and other African populations but in the Asians and Caucasians. These 4 variants were rs12569850, rs7073165, rs9332140 and rs9332161. The Orang Asli and Singaporean Malays have 3 of the earlier variants; while rs7073165 were not reported in the Japanese and Chinese. The Orang Asli has the highest frequency for 10 of the variants detected and compared with other populations. They are rs9332108 (17.2%), rs4086116 (29.5%), rs4917639 (32.8%), rs9332172 (30.3%), rs17110288 (18.0%), rs9325473 (18.9%), rs1934963 (32%), rs1057910 (18.0%), rs9332214 (18.03%), rs9332217 (16.4%). rs1934968 was found to be high among the Orang Asli and other Asian populations (>40%); while the allele was found at low frequencies (<3%) among the YRI, MKK, LWK, ASW. The other European population have percentage of 9e12%. Surprisingly, the Orang Asli have the highest percentage of rs1057910 (CYP2C9*3) reported (18%) while the population with the second highest percentage is GIH (13.4%); the other population have percentage between 4 and 8%. Another variant which was found highest among the Orang Asli is rs1934963 (31.9%) while the YRI have reported 18% and CEU 19.2%. The Orang Asli reported the highest percentage for rs1934962 among the Asians but have similar percentage compared to other HapMap populations. rs9332168 was found highest in the Orang Asli among the Asian but the other population in Europe and Africa countries have higher percentage. rs4917639 were found in 32.8% of the Orang Asli; and 17e23% of the African and Caucasian populations. It was found lower among the Asians (less than 10%). rs10509680 was detected in 20% of the Orang Asli but none in the Singaporean Malays. Its frequencies in the Indians were at 13% while in other HapMap populations it ranged from 2 to 6% among the Africans, Asians and the Italians. On the other hand, rs1200313, rs2153628, rs9332161 and rs9332177 were not detected in the Orang Asli. rs1799853 (CYP2C9*2) were not detected in both the Orang Asli and Singaporean Malays. Six variants were found lower among the Africans compared to other populations. They are rs4918766, rs12772675, rs1934967, rs9332105, rs12572351 and rs10509679 (Fig. 1 and Supplementary Table 1).

Please cite this article in press as: Teh LK, et al., Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations, Drug Metabolism and Pharmacokinetics (2016), http://dx.doi.org/10.1016/j.dmpk.2016.04.004

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3.2. Clinical impacts In terms of clinical relevance, the Orang Asli were found to have 3 SNPs that have been associated with clinical impact. They are rs1057910 (CYP2C9*3), rs10509680 and rs4917639. Two of the alleles, rs10509680 and rs4917639 were however not in Hardy Weinberg Equilibrium (HWpval < 0.001). rs1057910 which represent CYP2C9*3 was present commonly among the Orang Asli (16.39% heterozygous and 9.84% homozygous CYP2C9*3) but less frequent among the Malays CYP2C9*3. Another defective SNP, rs10509680 was not detected in the Malays but 28% of the Orang

Asli carry either one or two defective alleles. rs4917639 occurs at the highest frequencies with 22.95% of homozygous variant and 19.67% of heterozygous variant among the Orang Asli. No homozygous rs4917639 was detected among the Malays but 15.63% are heterozygous carrier (Table 2). 3.3. Hardy Weinberg Equilibrium, LD and haplotype maps The observed and predicted genotypes for the 339 genetic variants of CYP2C9 were compared. The Malays have a total of 270 variants and the Orang Asli have 237 variants that were subjected

Fig. 1. Comparison of distribution of CYP2C9 variations in Orang Asli, Singaporean Malays and 11 HapMap populations. ASW: African ancestry in Southwest USA; CEU: Utah residents with Northern and Western European ancestry from the CEPH collection; CHB: Han Chinese in Beijing, China; CHD: Chinese in Metropolitan Denver, Colorado; GIH: Gujarati Indians in Houston, Texas; JPT: Japanese in Tokyo, Japan; LWK: Luhya in Webuye, Kenya; MX: Mexican ancestry in Los Angeles, California; MKK: Maasai in Kinyawa, Kenya; TSI: Toscani in Italia; YRI: Yoruba in Ibadan, Nigeria; SiM Singaporean Malay; OA: Orang Asli in Peninsular Malaysia.

Table 2 Comparison of the clinically important SNPs and Hardy Weinberg Equilibrium of CYP2C9 in the Orang Asli and Malays. SNPs

Position

Alleles

Level of evidence [25]

Substitution of amino acid

RS name rs1057910 CYP2C9*3 rs10509680 rs4917639

OA

SiM

ObsHET

PredHET

HWpval

MAF

ObsHET

PredHET

HWpval

MAF

96741053

A:C

1A

Ile359Leu

0.164

0.296

0.0038

0.18

0.073

0.07

1

0.036

96734339 96725535

G:T A:C

3 2A

No No

0.148 0.197

0.326 0.441

2.00E04 4.03E05

0.205 0.328

ND 0.156

ND 0.144

ND 1

ND 0.078

ND ¼ not determined. ObsHET ¼ observe heterozygosity. PredHET ¼ predicted heterozygosity. HWpval ¼ Hardy Weinberg Equilibrium p value. MAF ¼ minor allele frequency. OA ¼ Orang Asli. SiM ¼ Singaporean Malays.

Please cite this article in press as: Teh LK, et al., Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations, Drug Metabolism and Pharmacokinetics (2016), http://dx.doi.org/10.1016/j.dmpk.2016.04.004

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to haplotype analysis. A total of 199 variants were present at a frequency with a minimum allele frequencies (MAF) of more than 5%. The Orang Asli has 189 variants while the Singaporean Malays had 162 variants. We detected 1 variant of the Malays and 42 variants of the Orang Asli with HW p-value < 0.001 (default setting as in Haploview) and therefore they were significantly different (Hardy Weinberg Disequilibrium (Table 3)). The Malays have only one haplotype block (Fig. 2); while thirteen [13] haplotype blocks were identified in the Orang Asli (Fig. 3) The 13 LD blocks observed for the Orang Asli are unique while the Malays have one LD block as shown in Figs. 2 and 3. The value of multiallelic D0 describing the level of recombination between three blocks [7,8and9] was low (0.15 and 0.23), which implies that they are less tightly correlated and display evidence of historical recombination. 4. Discussion Ethnic differences in the types and frequencies of the Cytochrome P450 have been studied in many different populations and the information have been made available in the databases of PharmGKB and 1000 Genomes. In this study, we used the database curated by PharmKGB and dbSNPs to compare the patterns and allele frequencies of the Orang Asli as well as the Malays in Singapore with 11 other populations. We intend to report for the first time the comprehensive patterns and frequencies of CYP2C9 among the Orang Asli. A total of 237 CYP2C9 variants were successfully annotated and assigned with reference SNPs ID's using the 1000 Genome Browser. The numbers of variants detected among the Orang Asli which were not assigned with rs number for CYP2C9 are 829. The majority of these variants without designated rs numbers are present at low frequencies; 10 of the 829 unmatched variants somehow have frequencies of more than 5%. The variants were intronic variants and their functions are unclear. The information on the allele frequencies among the Orang Asli and the Singaporean Malays are useful to be added on to the existing database because it can provide clues to the evolutionary patterns of CYP2C9 in different parts of the world. A number of variants were detected to be present at the highest frequencies in the Orang Asli compared to the African and other Asian population (Fig. 1). We could not determine if the variants arised due to evolutionary pressure but they certainly require further investigation to understand the implications of these variants in the Orang Asli. In addition, from the distributions and types of different alleles of CYP2C9 using our local genome data and public database as conducted in this study, no specific pattern of evolution was observed. More comprehensive whole genome databases from other parts of the world are required to close the gaps of evolutionary link. There are apparent differences in the LD blocks between the two populations studied. Forty-two variants were not in equilibrium among the Orang Asli. The reasons for the violation of HardyeWeinberg equilibrium as observed among the Orang Asli could be due to a number of factors. One obvious reason could be the small sample size of the Orang Asli in this study. Other reasons include mutations, natural selection, nonrandom mating, genetic drift, and gene flow among the Orang Asli. The Orang Asli selected in this study were from sub-ethnic groups where their population are small (200e700) and their culture have prevented them from inter-marriage and therefore there are likely to suffer forces of disequilibrium. The Orang Asli have a higher number of variants for CYP2C9 compared to the Malays and perhaps other populations. Another possible explanation is that mutations disrupt the equilibrium of allele frequencies by introducing new alleles into a

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population. In addition, natural selection and nonrandom mating disrupt the HardyeWeinberg equilibrium resulting in changes in gene frequencies. Another factor that can upset this equilibrium is genetic drift, which occurs when allele frequencies grow higher or lower by chance especially in small populations, such as the Orang Asli. Gene flow, which occurs when breeding between two populations transfers new alleles into a population, can also alter the HardyeWeinberg equilibrium [36]. There are several internal blue boxes defined as complete LD with lower statistical confidence, generally resulting from lower frequency SNPs among the Orang Asli. Our results showed that rs1057910 (CYP2C9*3) is one of the major variants of CYP2C9 with defective function in both the populations studied. rs1057910 (CYP2C9*3) was found to be present in 18% of the Orang Asli. About 10% (6 out of 61) of them were homozygous CYP2C9*3/*3 and 16.4% were heterozygous CYP2C9*1/ *3. None of the Malays was homozygous CYP2C9*3/*3 and 7% were heterozygous of CYP2C9*1/*3. Accumulating evidence is pointing to the requirement of cautious use of drugs and dose adjustments for individuals with defective alleles. In addition to warfarin whereby rs1057910 (CYP2C9*3) has a clear evidence for associated risks of adverse effects and dose adjustment of level 1A (Table 2), the use of other drugs which are substrates of CYP2C9 also require adjustment. The presence of rs1057910 (CYP2C9*3) allele was associated with a significant high risk of bleeding (adjusted OR, 7.3; 95% CI, 2.058e26.004) when treated with NSAIDs [37]. Impaired metabolism of sulfonylureas due to gene polymorphisms in the metabolic enzyme CYP2C9 might lead to hypoglycemia. Presence of and rs1057910 (CYP2C9*3) allele has been associated with an increases risk for hypoglycemia in Type 2 diabetic patients treated with sulfonylureas [38]. In short, a large number of the Orang Asli and approximately 20% of the Malays may therefore require lower doses of drugs metabolized by CYP2C9 or at increased risks of adverse effects if standard doses of drugs are consumed. One drug which requires alert in medical therapy is warfarin. Evidences are accumulating on the safe use of warfarin which are dependent on CYP2C9 variants as variants that confer lower metabolic capacity result in higher risks of bleeding. CYP2C9*2 was not detected in both the populations studied. It is extremely uncommon in the Asian population [21,39,40]. Our results are similar to the previous studies and hence it is not necessary to routinely genotype this allele for the prediction of catalytic activity of CYP2C9 in Asians. Another two variants which have been shown to have association with clinical impacts are rs10509680 and rs4917639. In the case of warfarin therapy, patients with homozygous rs10509680 GG genotype may require increased dose as compared to patients with the TT or TG genotypes; while patients with the AA genotype of rs4917639 may require increased dose of warfarin as compared to patients with the CC or CA genotypes [41,42]. The types and frequencies of the CYP2C9 variants were different between the African population and Orang Asli. Only one variant, rs12772675 was reported in both the African and Orang Asli; while the Orang Asli has profiles closer to those of the Malays, Chinese and Japanese. We propose that CYP2C9 evolved and different mutation take place in different regions of the world due to different diet and life styles and to allow them to strive in different environmental pressure. Information about the polymorphisms of CYP2C9 in the Orang Asli and the Malays is important for another reason; drug-herb or herbeherb interaction. Frequent use of herbs among the Orang Asli and Malays may result in unintentional harm and cautions is required to prevent avoidable events. Herbs may be used with or without knowledge of the medical doctors and the co-use of

Please cite this article in press as: Teh LK, et al., Systematic characterization and comparison of the CYP2C9 variability of the Orang Asli in Malaysia with 12 populations, Drug Metabolism and Pharmacokinetics (2016), http://dx.doi.org/10.1016/j.dmpk.2016.04.004

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Table 3 Comparison of the SNPs of CYP2C9 with MAF > 0.05 in the Orang Asli and Malays and Hardy Weinberg Equilibrium. SNP

rs1057910 rs1057911 rs10509679 rs10509680 rs10637048 rs111309918 rs111598382 rs111680074 rs111691688 rs111767681 rs111770290 rs112217529 rs112621808 rs112704334 rs112978985 rs113003580 rs113114298 rs113250521 rs113284703 rs113348400 rs113503880 rs114628972 rs116715413 rs117340371 rs117357747 rs117424644 rs117936722 rs117973766 rs1200313 rs12252334 rs12264083 rs12268897 rs12569850 rs12572071 rs12572351 rs12572373 rs12572588 rs12573036 rs12769684 rs12772675 rs12772884 rs12783288 rs12785201 rs138281832 rs138598575 rs138928966 rs139222355 rs139319534 rs139699620 rs139804441 rs140184390 rs140494635 rs142082743 rs143299910 rs144101608 rs144532159 rs144551427 rs145267141 rs146140076 rs147235175 rs147996541 rs149189071 rs149259501 rs150129357 rs150870360 rs151311764 rs1578438 rs17110288 rs181227778 rs181444436 rs182129768 rs1856908 rs186461662

Position

96741053 96748737 96708226 96734339 96734292 96711706 96709877 96744727 96699682 96720169 96728688 96715247 96711268 96730214 96716374 96715585 96733596 96715386 96718609 96704918 96717122 96744732 96739282 96728657 96719521 96704144 96703413 96728357 96722059 96718809 96699683 96719827 96727160 96726897 96703220 96727552 96703491 96730064 96717478 96706409 96700630 96720171 96699444 96727403 96699291 96735126 96705891 96728650 96718774 96703891 96728015 96728169 96729574 96716893 96736108 96728326 96730424 96719207 96716374 96699512 96746526 96726673 96730064 96714787 96746814 96726746 96713760 96732599 96718815 96717122 96746063 96732731 96717149

Alleles

A:C A:T G:A G:T G:T G:C G:A A:C C:T A:G T:C A:G G:A T:C A:C G:T A:G G:A G:C A:G T:A C:T T:C G:A G:A G:A G:T T:G A:C G:T G:A G:A A:G G:A G:A G:A C:A C:T T:C C:G A:T A:G A:C C:A A:C T:G C:T A:G G:T G:T A:G A:C T:C A:C A:T T:C G:A A:T A:C A:G T:C A:G C:T G:A T:A G:A G:A G:A C:T T:A C:T G:T G:A

OA

SiM

ObsHET

PredHET

HWpval

MAF

ObsHET

PredHET

HWpval

MAF

0.164 0.18 0.164 0.148 0.311 0.164 0.18 0.803 0.197 0.082 0.18 0.197 0.016 0.164 ND 0.164 0.164 0.148 0.18 0.197 ND 0.148 0.083 0.197 0.246 0.049 0.131 0.115 0.295 0.115 0.115 0.262 0.197 0.164 0.18 0.164 0.148 ND 0.197 0.246 0.23 0.115 0.148 0.18 0.082 0.173 0.164 0.016 ND 0.123 0.082 0.213 0.164 0.115 0.607 0.23 0.098 0.131 0.23 0.197 ND 0.098 0.098 0.082 ND 0.18 0.148 0.164 0.18 0.18 0.033 0.295 0.148

0.296 0.285 0.274 0.326 0.263 0.274 0.285 0.481 0.296 0.19 0.306 0.402 0.016 0.203 ND 0.274 0.203 0.216 0.285 0.296 ND 0.306 0.193 0.296 0.423 0.048 0.228 0.19 0.499 0.164 0.108 0.499 0.228 0.252 0.263 0.228 0.24 ND 0.228 0.499 0.499 0.216 0.263 0.306 0.19 0.158 0.274 0.016 ND 0.115 0.19 0.362 0.252 0.19 0.423 0.274 0.123 0.252 0.493 0.296 ND 0.228 0.228 0.216 ND 0.216 0.24 0.296 0.285 0.285 0.063 0.5 0.216

0.0038 0.0196 0.0111 2.00E04 0.3799 0.0111 0.0196 5.40E08 0.0327 0.0013 0.0073 2.00E04 1 0.3196 ND 0.0111 0.3196 0.0697 0.0196 0.0327 ND 6.00E04 0.0014 0.0327 0.0027 1 0.0113 0.0265 0.0023 0.1216 1 3.00E04 0.5123 0.0338 0.0541 0.1047 0.0202 ND 0.5123 1.00E04 3.57E05 0.006 0.006 0.0073 0.0013 1 0.0111 1 ND 1 0.0013 0.0047 0.0338 0.0265 6.00E04 0.3661 0.4341 0.003 4.52E05 0.0327 ND 6.00E04 6.00E04 2.00E04 ND 0.4104 0.0202 0.0038 0.0196 0.0196 0.0988 0.0022 0.0697

0.18 0.172 0.164 0.205 0.156 0.164 0.172 0.402 0.18 0.107 0.189 0.279 0.008 0.115 ND 0.164 0.115 0.123 0.172 0.18 ND 0.189 0.108 0.18 0.303 0.025 0.131 0.107 0.475 0.09 0.057 0.475 0.131 0.148 0.156 0.131 0.139 ND 0.131 0.484 0.475 0.123 0.156 0.189 0.107 0.087 0.164 0.008 ND 0.061 0.107 0.238 0.148 0.107 0.303 0.164 0.066 0.148 0.443 0.18 ND 0.131 0.131 0.123 ND 0.123 0.139 0.18 0.172 0.172 0.033 0.492 0.123

0.073 0.073 0.354 ND ND 0.073 0.073 ND 0.073 0.073 0.042 0.126 ND ND 0.365 0.083 0.021 ND 0.073 0.073 0.052 ND 0.083 ND 0.167 0.062 0.083 ND 0.417 0.052 0.042 ND 0.354 0.354 0.354 0.333 0.354 0.158 0.354 ND 0.385 0.354 0.333 0.073 0.083 ND 0.073 ND 0.062 ND 0.281 ND ND 0.073 ND 0.042 ND 0.083 ND 0.073 0.052 0.083 ND 0.083 0.094 0.354 0.344 0.073 0.073 ND 0.062 0.406 0.083

0.07 0.07 0.385 ND ND 0.07 0.07 ND 0.07 0.07 0.041 0.118 ND ND 0.434 0.08 0.021 ND 0.07 0.07 0.051 ND 0.08 ND 0.153 0.061 0.08 ND 0.469 0.051 0.041 ND 0.385 0.385 0.385 0.375 0.385 0.229 0.385 ND 0.46 0.385 0.375 0.07 0.08 ND 0.07 ND 0.061 ND 0.38 ND ND 0.07 ND 0.041 ND 0.08 ND 0.07 0.051 0.08 ND 0.08 0.089 0.385 0.38 0.07 0.07 ND 0.061 0.471 0.08

1 1 0.5539 ND ND 1 1 ND 1 1 1 1 ND ND 0.1697 1 1 ND 1 1 1 ND 1 ND 1 1 1 ND 0.3545 1 1 ND 0.5539 0.5539 0.5539 0.3796 0.5539 0.0374 0.5539 ND 0.1535 0.5539 0.3796 1 1 ND 1 ND 1 ND 0.0224 ND ND 1 ND 1 ND 1 ND 1 1 1 ND 1 1 0.5539 0.4622 1 1 ND 1 0.2341 1

0.036 0.036 0.26 ND ND 0.036 0.036 ND 0.036 0.036 0.021 0.063 ND ND 0.318 0.042 0.01 ND 0.036 0.036 0.026 ND 0.042 ND 0.083 0.031 0.042 ND 0.375 0.026 0.021 ND 0.26 0.26 0.26 0.25 0.26 0.132 0.26 ND 0.359 0.26 0.25 0.036 0.042 ND 0.036 ND 0.031 ND 0.255 ND ND 0.036 ND 0.021 ND 0.042 ND 0.036 0.026 0.042 ND 0.042 0.047 0.26 0.255 0.036 0.036 ND 0.031 0.38 0.042

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

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7

Table 3 (continued ) SNP

Position

Alleles

rs1934960 rs1934962 rs1934963 rs1934964 rs1934965 rs1934967 rs1934968 rs1934969 rs199789854 rs199800427 rs199850072 rs200852044 rs200852044 rs201463097 rs201512361 rs201798466 rs201920202 rs202073632 rs2096069 rs2153628 rs2253635 rs2265608 rs2298037 rs2475376 rs2487501 rs2487502 rs28371675 rs28371676 rs28371677 rs28371678 rs28371682 rs2860905 rs2860906 rs2984310 rs34532201 rs35126593 rs35144931 rs35406621 rs35511771 rs35617077 rs35626865 rs35687798 rs370247558 rs371216918 rs371618201 rs4917639 rs4917641 rs4918766 rs547474494 rs56233220 rs61886778 rs6583964 rs7073165 rs7083767 rs7089580 rs74494115 rs7475054 rs7476934 rs74803483 rs74963911 rs74996264 rs75541073 rs75645849 rs76074552 rs76076565 rs76673638 rs76905088 rs77467078 rs77582920 rs78144779 rs78831378 rs7897079 rs79748969

96733537 96734624 96734676 96737371 96737935 96741426 96741817 96748495 96699010 96726102 96731595 96716953 96717053 96709447 96708815 96726084 96728380 96714647 96720032 96723424 96700537 96728897 96746078 96712400 96719188 96712899 96702337 96702363 96702472 96702552 96703109 96702295 96704576 96709993 96710164 96725811 96739382 96720051 96712143 96726285 96718794 96718213 96730086 96714291 96715138 96725535 96738041 96711884 96730151 96717286 96718308 96705934 96721107 96710725 96705223 96719845 96717196 96717148 96713372 96706316 96713995 96707990 96698970 96725037 96747206 96719827 96737791 96734466 96744890 96735342 96713686 96720518 96715671

C:T C:T T:C T:C T:C C:T G:A A:T C:G A:G C:A A:G T:C G:A T:C C:T G:T T:A C:T A:G C:C A:T C:T G:A G:A C:T C:T T:C A:G T:C G:T G:A G:A C:T C:T G:A A:G G:A T:C A:C C:T T:C G:A G:A T:C A:C C:T G:A C:T G:C T:A A:G C:G T:C A:T G:T G:T C:A C:T C:T A:G G:A T:G C:A G:T A:G C:G T:A A:T A:T C:G A:G T:C

OA

SiM

ObsHET

PredHET

HWpval

MAF

ObsHET

PredHET

HWpval

MAF

0.279 0.082 0.18 0.279 0.295 0.098 0.295 0.295 0.143 0.049 0.2 0.131 ND 0.016 0.016 0.197 0.098 0.82 0.262 0.115 0.279 0.082 0.115 0.246 0.295 0.016 0.18 0.18 0.23 0.033 0.213 0.246 0.262 0.295 0.164 0.197 0.049 0.082 0.098 0.098 0.164 0.295 0.164 0.033 ND 0.197 0.23 0.262 0.18 0.279 0.23 0.246 0.279 0.262 0.115 0.197 0.328 0.23 0.164 0.164 0.18 0.082 0.131 0.18 0.197 ND 0.164 0.148 0.18 0.18 0.164 0.115 0.18

0.495 0.263 0.435 0.498 0.5 0.123 0.493 0.5 0.133 0.048 0.18 0.252 ND 0.016 0.048 0.177 0.094 0.484 0.5 0.137 0.5 0.216 0.216 0.487 0.47 0.016 0.24 0.306 0.441 0.063 0.306 0.435 0.499 0.477 0.203 0.228 0.048 0.216 0.123 0.094 0.203 0.489 0.296 0.032 ND 0.441 0.489 0.489 0.306 0.435 0.416 0.456 0.499 0.497 0.108 0.387 0.499 0.416 0.274 0.316 0.306 0.216 0.274 0.285 0.296 ND 0.274 0.306 0.285 0.285 0.274 0.137 0.285

0.0011 1.09E05 1.38E05 0.001 0.0022 0.4341 0.0028 0.0022 1 1 1 0.003 ND 1 0.0496 1 1 1.78E08 3.00E04 0.544 9.00E04 2.00E04 0.006 2.00E04 0.0068 1 0.1509 0.0073 4.00E04 0.0988 0.0523 0.0016 3.00E04 0.0051 0.3196 0.5123 1 2.00E04 0.4341 1 0.3196 0.0033 0.0038 1 ND 4.03E05 5.45E05 5.00E04 0.0073 0.0104 0.0013 7.00E04 9.00E04 4.00E04 1 5.00E04 0.012 0.0013 0.0111 0.0013 0.0073 2.00E04 9.00E04 0.0196 0.0327 ND 0.0111 6.00E04 0.0196 0.0196 0.0111 0.544 0.0196

0.451 0.156 0.32 0.467 0.492 0.066 0.443 0.492 0.071 0.025 0.1 0.148 ND 0.008 0.025 0.098 0.049 0.41 0.492 0.074 0.5 0.123 0.123 0.418 0.377 0.008 0.139 0.189 0.328 0.033 0.189 0.32 0.475 0.393 0.115 0.131 0.025 0.123 0.066 0.049 0.115 0.426 0.18 0.016 ND 0.328 0.426 0.426 0.189 0.32 0.295 0.352 0.484 0.459 0.057 0.262 0.475 0.295 0.164 0.197 0.189 0.123 0.164 0.172 0.18 ND 0.164 0.189 0.172 0.172 0.164 0.074 0.172

0.385 0.083 0.156 0.385 0.385 0.188 0.365 0.385 ND ND ND ND 0.083 0.01 0.01 ND ND ND 0.177 0.042 0.417 0.021 0.354 0.375 0.354 0.031 0.354 0.062 0.156 ND 0.073 0.156 0.385 0.375 0.344 0.354 0.031 0.25 0.042 ND 0.333 0.406 0.023 0.062 0.062 0.156 0.406 0.406 ND 0.156 0.146 0.385 0.385 0.385 0.042 ND 0.385 ND 0.073 0.073 0.073 0.083 0.073 0.062 0.073 0.292 0.073 0.073 0.073 0.073 0.073 0.052 0.312

0.46 0.08 0.144 0.46 0.46 0.203 0.5 0.46 ND ND ND ND 0.08 0.01 0.01 ND ND ND 0.39 0.041 0.469 0.041 0.385 0.5 0.5 0.031 0.385 0.061 0.144 ND 0.07 0.144 0.46 0.5 0.38 0.385 0.031 0.33 0.041 ND 0.353 0.448 0.067 0.061 0.061 0.144 0.448 0.448 ND 0.144 0.135 0.434 0.46 0.46 0.041 ND 0.46 ND 0.07 0.07 0.07 0.08 0.07 0.061 0.07 0.43 0.07 0.07 0.07 0.07 0.07 0.051 0.364

0.1535 1 1 0.1535 0.1535 0.6941 0.0118 0.1535 ND ND ND ND 1 1 1 ND ND ND 5.75E07 1 0.3545 0.0627 0.5539 0.0208 0.0064 1 0.5539 1 1 ND 1 1 0.1535 0.0208 0.4622 0.5539 1 0.0405 1 ND 0.7311 0.4594 0.0706 1 1 1 0.4594 0.4594 ND 1 1 0.3633 0.1535 0.1535 1 ND 0.1535 ND 1 1 1 1 1 1 1 0.0034 1 1 1 1 1 1 0.2432

0.359 0.042 0.078 0.359 0.359 0.115 0.495 0.359 ND ND ND ND 0.042 0.005 0.005 ND ND ND 0.266 0.021 0.375 0.021 0.26 0.5 0.49 0.016 0.26 0.031 0.078 ND 0.036 0.078 0.359 0.5 0.255 0.26 0.016 0.208 0.021 ND 0.229 0.339 0.035 0.031 0.031 0.078 0.339 0.339 ND 0.078 0.073 0.318 0.359 0.359 0.021 ND 0.359 ND 0.036 0.036 0.036 0.042 0.036 0.031 0.036 0.312 0.036 0.036 0.036 0.036 0.036 0.026 0.24

(continued on next page)

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

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Table 3 (continued ) SNP

Position

Alleles

rs79800397 rs79861341 rs80159642 rs9325473 rs9332104 rs9332105 rs9332108 rs9332113 rs9332116 rs9332117 rs9332118 rs9332120 rs9332127 rs9332128 rs9332129 rs9332136 rs9332137 rs9332139 rs9332140 rs9332141 rs9332146 rs9332147 rs9332149 rs9332157 rs9332160 rs9332161 rs9332167 rs9332168 rs9332169 rs9332172 rs9332174 rs9332175 rs9332177 rs9332181 rs9332183 rs9332184 rs9332187 rs9332188 rs9332192 rs9332198 rs9332211 rs9332212 rs9332214 rs9332217 rs9332219 rs9332220 rs9332223 rs9332224 rs9332226 rs9332227 rs9332228 rs9332230 rs9332238

96714425 96733367 96719846 96734582 96698690 96698925 96699980 96700402 96700779 96701077 96701176 96701850 96707471 96708627 96708750 96712970 96712978 96721592 96721607 96721662 96722244 96722552 96722674 96724115 96724395 96724697 96731214 96731292 96731310 96731788 96732097 96732484 96734790 96735927 96736184 96736514 96737177 96737210 96740013 96741497 96742737 96742750 96743108 96743228 96743663 96743943 96744162 96744239 96744295 96745180 96745195 96745984 96748492

C:A C:T A:T G:A T:C G:C T:C G:C G:A G:T T:C T:C G:C G:A A:G G:C T:C G:A T:C T:C G:A T:C T:A C:T T:G T:C T:C C:T A:G A:G A:G T:C A:T C:T G:A C:T A:G C:A C:T G:A C:T C:T T:C A:C T:G G:A C:G T:C C:T T:G C:A A:T G:A

OA

SiM

ObsHET

PredHET

HWpval

MAF

ObsHET

PredHET

HWpval

MAF

0.18 0.098 0.197 0.18 0.115 0.098 0.18 0.131 0.098 0.066 0.115 0.098 0.098 0.115 0.131 0.098 0.164 0.082 0.131 0.148 ND 0.164 0.131 0.164 0.197 0.016 0.164 0.131 0.18 0.18 0.115 0.115 0.164 0.082 0.18 0.148 0.164 0.148 0.016 0.115 0.131 0.164 0.197 0.164 0.115 0.246 0.115 0.016 0.158 0.18 0.131 0.197 0.18

0.285 0.252 0.371 0.306 0.137 0.274 0.285 0.228 0.177 0.094 0.24 0.123 0.203 0.216 0.274 0.203 0.296 0.216 0.274 0.306 ND 0.296 0.252 0.296 0.296 0.016 0.203 0.177 0.285 0.423 0.137 0.263 0.316 0.216 0.306 0.285 0.274 0.263 0.016 0.216 0.123 0.15 0.296 0.274 0.216 0.409 0.19 0.016 0.145 0.285 0.228 0.296 0.263

0.0196 1.00E04 0.0011 0.0073 0.544 3.48E05 0.0196 0.0113 0.0149 0.2417 0.0014 0.4341 0.0029 0.006 9.00E04 0.0029 0.0038 2.00E04 9.00E04 6.00E04 ND 0.0038 0.003 0.0038 0.0327 1 0.3196 0.1749 0.0196 2.51E05 0.544 4.00E04 0.0013 2.00E04 0.0073 0.0019 0.0111 0.006 1 0.006 1 1 0.0327 0.0111 0.006 0.0047 0.0265 1 1 0.0196 0.0113 0.0327 0.0541

0.172 0.148 0.246 0.189 0.074 0.164 0.172 0.131 0.098 0.049 0.139 0.066 0.115 0.123 0.164 0.115 0.18 0.123 0.164 0.189 ND 0.18 0.148 0.18 0.18 0.008 0.115 0.098 0.172 0.303 0.074 0.156 0.197 0.123 0.189 0.172 0.164 0.156 0.008 0.123 0.066 0.082 0.18 0.164 0.123 0.287 0.107 0.008 0.079 0.172 0.131 0.18 0.156

0.073 0.083 ND 0.073 0.042 0.354 0.073 0.354 0.083 0.042 0.083 0.042 0.083 0.083 0.083 0.083 0.073 0.083 0.354 0.073 0.094 0.073 0.083 0.073 0.073 ND 0.354 0.042 0.073 0.156 0.042 0.073 0.073 0.083 0.073 0.385 0.073 0.073 0.042 0.073 0.042 0.042 0.073 0.073 0.083 0.156 0.083 ND ND 0.073 0.083 0.073 0.073

0.07 0.08 ND 0.07 0.041 0.385 0.07 0.385 0.08 0.041 0.08 0.041 0.08 0.08 0.08 0.08 0.07 0.08 0.385 0.07 0.089 0.07 0.08 0.07 0.07 ND 0.385 0.041 0.07 0.144 0.041 0.07 0.07 0.08 0.07 0.409 0.07 0.07 0.041 0.089 0.041 0.041 0.07 0.07 0.08 0.144 0.08 ND ND 0.07 0.08 0.07 0.07

1 1 ND 1 1 0.5539 1 0.5539 1 1 1 1 1 1 1 1 1 1 0.5539 1 1 1 1 1 1 ND 0.5539 1 1 1 1 1 1 1 1 0.7064 1 1 1 0.3564 1 1 1 1 1 1 1 ND ND 1 1 1 1

0.036 0.042 ND 0.036 0.021 0.26 0.036 0.26 0.042 0.021 0.042 0.021 0.042 0.042 0.042 0.042 0.036 0.042 0.26 0.036 0.047 0.036 0.042 0.036 0.036 ND 0.26 0.021 0.036 0.078 0.021 0.036 0.036 0.042 0.036 0.286 0.036 0.036 0.021 0.047 0.021 0.021 0.036 0.036 0.042 0.078 0.042 ND ND 0.036 0.042 0.036 0.036

ND ¼ not determined. ObsHET ¼ observe heterozygosity. PredHET ¼ predicted heterozygosity. HWpval ¼ Hardy Weinberg Equilibrium p value. MAF ¼ minor allele frequency. OA ¼ Orang Asli. SiM ¼ Singaporean Malays.

modern and traditional therapies may result in both pharmacokinetic and pharmacodynamic herbedrug interactions. Many herbs and natural compounds isolated from herbs have been identified as substrates, inhibitors, and/or inducers of various CYP enzymes. Components of St. John's wort are known to be inducer of CYP2C9 [43]. As there is a high prevalence of poor metabolizer of CYP2C9 among the Orang Asli and known or potential herb-CYP interactions; information on the heterogeneity of CYP2C9 is

therefore important for the Orang Asli and also the Singaporean Malays. 5. Conclusion In conclusion, our results showed that there are different polymorphism patterns, allele frequencies, genotype frequencies and LD blocks between the Orang Asli, the Malays and the other

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66 67 68 69 70 Conflict of interest 71 72 The authors declare that they have no competing interests. 73 74 Funding 75 76 This work was supported by a grant from the Ministry of Higher Q3 77 Education Malaysia [600-RMI/LRGS 5/3 (1/2011)-1]. 78 79 80 Acknowledgments 81 82 We thank members of the LRGS “Evolutionary Genomics and 83 Anthropological Approaches on the Endangered Malaysian 84 Aborigine Populations: Towards Ensuring their Sustainability” 85 project (600-RMI/LRGS 5/3 (1/2011)-1); the volunteers involved in 86 the project; and members of the Integrative Pharmacogenomics 87 Institute (iPROMISE). 88 89 Appendix A. Supplementary data 90 91 Supplementary data related to this article can be found at http:// 92 dx.doi.org/10.1016/j.dmpk.2016.04.004. 93 94 References 95 96 [1] Rettie AE, Jones JP. Clinical and toxicological relevance of CYP2C9: drug-drug 97 interactions and pharmacogenetics. Annu Rev Pharmacol Toxicol 2005;45: 98 477e94. [2] Pirmohamed M, Park BK. Cytochrome P450 enzyme polymorphisms and 99 adverse drug reactions. Toxicology 2003;192:23e32. 100 [3] García-Martín E, Martínez C, Ladero JM, Agúndez JA. Interethnic and intra101 ethnic variability of CYP2C8 and CYP2C9 polymorphisms in healthy individuals. Mol Diagn Ther 2006;10:29e40. 102 [4] Rosemary J, Adithan C. The pharmacogenetics of CYP2C9 and CYP2C19: ethnic 103 variation and clinical significance. Curr Clin Pharmacol 2007;2:93e109. 104 [5] Aithal GP, Day CP, Kesteven PJ, Daly AK. Association of polymorphisms in the cytochrome P450 CYP2C9 with warfarin dose requirement and risk of 105 bleeding complications. Lancet 1999;353:717e9. 106 [6] Lindh JD, Holm L, Andersson ML, Rane A. Influence of CYP2C9 genotype on 107 warfarin dose requirements e a systematic review and meta-analysis. Eur J Clin Pharmacol 2009;65:365e75. 108 [7] Ngow HA, Wan Khairina WMN, Teh LK, Lee WL, Harun R, Ismail R, et al. 109 CYP2C9 polymorphism: prevalence in healthy and warfarin treated Malay and 110 Chinese in Malaysia. Singap Med J 2009;50:490e3. [8] Teh LK, Langmia IM, Fazleen Haslinda MH, Ngow HA, Roziah MJ, Harun R, et al. 111 Clinical relevance of VKORC1 (G-1639A and C1173T) and CYP2C9*3 among 112 patients on warfarin. J Clin Pharm Ther 2012;37:232e6. 113 [9] Kidd RS, Straughn AB, Meyer MC, Blaisdell J, Goldstein JA, Dalton JT. Phar114 macokinetics of chlorpheniramine, phenytoin, glipizide and nifedipine in an individual homozygous for the CYP2C9*3 allele. Pharmacogenetics 1999;9: 115 71e80. 116 [10] Ninomiya H, Mamiya K, Matsuo S, Ieiri I, Higuchi S, Tashiro N. Genetic poly117 morphism of the CYP2C subfamily and excessive serum phenytoin concentration with central nervous system intoxication. Ther Drug Monit 2000;22: 118 230e2. 119 [11] van der Weide J, Steijns LS, van Weelden MJ, de Haan K. The effect of genetic 120 polymorphism of cytochrome P450 CYP2C9 on phenytoin dose requirement. Pharmacogenetics 2001;11:287e91. 121 [12] Mosher CM, Tai G, Rettie AE. CYP2C9 amino acid residues influencing 122 phenytoin turnover and metabolite regio- and stereochemistry. J Pharmacol 123 Exp Ther 2009;329:938e44. [13] Rosemary J, Surendiran A, Rajan S, Shashindran CH, Adithan C. Influence of the 124 CYP2C9 and CYP2C19 polymorphisms on phenytoin hydroxylation in healthy 125 individuals from South India. Indian J Med Res 2006;123:665e70. 126 [14] Sullivan-Klose TH, Ghanayem BI, Bell DA, Zhang ZY, Kaminsky LS, Shenfield GM, et al. The role of the CYP2C9-Leu359 allelic variant in the 127 tolbutamide polymorphism. Pharmacogenetics 1996;6:341e9. 128 [15] Kimura M, Ieiri I, Mamiya K, Urae A, Higuchi S. Genetic polymorphism of 129 cytochrome P450s, CYP2C19, and CYP2C9 in a Japanese population. Ther Drug 130 Monit 1998;20:243e7. populations. The information on the genetic polymorphisms of CYP2C9 in both the population are useful data for the advance of personalized medicine.

Fig. 2. CYP2C9 LD and Haplotype Block of the Singaporean Malays. (a) LD plot. Linkage disequilibrium is displayed by standard color schemes. Strong LD is displayed by bright red (very strong: LOD > 2, D0 ¼ 1) or pink red (moderately strong: LOD > 2, D0 < 1), intermediate LD is displayed by blue (LOD < 2, D0 ¼ 1), and absence of LD is displayed by white (LOD < 2, D0 < 1). (b) Haplotype block. Connect with thin line if >1% and connect with thick line if >10%.

Fig. 3. CYP2C9 LD and Haplotype Blocks of Orang Asli. (a) LD plot. Linkage disequilibrium is displayed by standard color schemes. Strong LD is displayed by bright red (very strong: LOD > 2, D0 ¼ 1) or pink red (moderately strong: LOD > 2, D0 < 1), intermediate LD is displayed by blue (LOD < 2, D0 ¼ 1), and absence of LD is displayed by white (LOD < 2, D0 < 1). (b) 13 haplotype blocks identified in the Orang Asli. Connect with thin line if >1% and connect with thick line if >10%. In the crossing areas, a value of multiallelic D0 is shown to represent the level of recombination between the two blocks.

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