A comprehensive Thai pharmacogenomics database (
Pharmacogenomics, the investigation into how an individual's genetic composition influences their response to drugs, is rapidly progressing and holds the potential to revolutionize healthcare. , Advanced DNA sequencing technologies enable computational methods to analyze genetic variations, identify star alleles, and extract genotypes from whole‐genome sequencing (WGS) and whole‐exome sequencing (WES) datasets. The clinical next‐generation sequencing (NGS) data provide pharmacogenetic information, eliminating the need for additional genetic testing, and enabling safe and effective drug administration. The integration of computational genomics into clinical pharmacogenomics (PGx) practice is now considered an essential imperative. PGx‐specific software and algorithms such as Stargazer, Aldy, PharmaVar, and PharmCAT are improving the accuracy of computational methods in clinical pharmacogenomics, despite initial challenges. A study by Shugg et al. demonstrated that Aldy v3.3 and v4.4 can accurately call genotypes for key pharmacogenes using clinical WES and WGS data. Likewise, the Stargazer, a bioinformatic tool, is the predominant diplotype caller for 58 PGx genes and structural variations of CYP2D6 . Identifying novel variants associated with drug‐related responses is crucial due to the unique genetic variations found in each population. A population‐based PGx database is essential for advancing personalized medicine and optimizing patient outcomes. Existing genomics databases may not accurately represent diverse populations, particularly Asians, due to limited sampling. Ultimately, a comprehensive population‐specific PGx database facilitates a more informed and personalized approach to healthcare, promoting optimal therapeutic outcomes for individuals across diverse populations. Thailand leads Southeast Asia in pharmacogenomics research, with funding from the Thailand Centre of Excellence for Life Sciences (TCELS) and Genomics Thailand Initiatives (GTI), enabling clinically valid data for PGx biomarkers in the Thai population. The current TPGxD‐1 study is proposed to catalog genetic variants of 63 VIPGx genes within Thai populations, enhancing drug treatment efficacy and minimizing adverse reactions. Two studies in Thailand have explored star alleles and novel variants in pharmacogenes. , This study extends these findings by cataloging alleles, diplotype frequency, functions, activity score for 25 VIPGx genes, and frequencies of known and novel variants for 63 VIPGx using 942 whole‐genome sequences.   WGS data retrieval The pilot project for the Thailand Reference Genome Panel (Protocol No. ID11‐60‐12) collected samples from 942 individuals across various regions of Thailand. The study participants are the employees of the Electricity Generating Authority of Thailand (EGAT) from various socioeconomic backgrounds and health profiles in Thailand. The study participants include a wide range of occupations, reflecting the diversity within the EGAT workforce. This study included participants who decided to volunteer their DNA for WGS analysis. Variant calling and filtering in 63 VIPGx genes The 942 samples were sequenced using Illumina and HiSeq X technologies. The analysis commenced by aligning WGS reads to the reference genome sequence GRCh38, utilizing the Burrows–Wheeler Aligner (BWA). Identification and marking of duplicate reads were executed using GATK 4.40.0's “Mark Duplicates” tool. Subsequently, base quality score recalibration (BQSR) was performed by generating a recalibration data table with the “BaseRecalibrator” tool. For variant calling, Sentieon (version 201808.08) was employed, resulting in the generation of raw variant call format (VCF) files. During this process, variants with a missing rate >5%, samples with a missing rate >10%, or duplicate samples were removed. The raw VCF files obtained were utilized for PGx‐specific analysis. Our focus was on extracting variants of interest from specific genes, particularly 63 VIPGx pharmacogenes (VIPGx), including ABCB1 , ABCG2 , ABL1 , ACE , ADH1A , ADH1B , ADH1C , ADRB1 , ADRB2 , AHR , ALDH1A1 , ALK , ALOX5 , BCR , BRAF , BRCA1 , CACNA1S , CFTR , COMT , CYP1A2 , CYP2A13 , CYP2A6 , CYP2B6 , CYP2C19 , CYP2C8 , CYP2C9 , CYP2D6 , CYP2J2 , CYP3A4 , CYP3A5 , CYP4F2 , DPYD , DRD2 , EGFR , ERBB2 , F5 , G6PD , GSTP1 , HLA‐B , HMGCR , KCNH2 , KCNJ11 , KIT , KRAS , MTHFR , NAT2 , NQO1 , NR1I2 , NRAS , NUDT15 , P2RY1 , PTGIS , PTGS2 , RYR1 , SCN5A , SLC19A1 , SLC22A1 , SLCO1B1 , SULT1A1 , TPMT , UGT1A1 , VDR , VKORC1 . A BED file, containing the genomic coordinates of these 63 VIPGx genes listed in the PharmGKB database was downloaded from the UCSC genome database. Subsequently, the BCFtool (version 1.18) facilitated the extraction of these 63 VIPGx genes from raw VCF files, resulting in files named as “PGx‐VCF files.” These files underwent comprehensive annotation and cataloging through Golden Helix VARSeq 2.5.0 (build 14,706‐608a25a286) (Figure ). Star allele analysis The Stargazer v2.0.2 was employed for the precise calling of star alleles from a dataset comprising 942 whole‐genome sequences. Utilizing the “PGx‐VCF files” extracted in the preceding step as the input, Stargazer v2.0.2 identified star alleles associated with 25 VIPGx genes, including ABCB1 , CACNA1S , CFTR , CYP1A2 , CYP2A13 , CYP2A6 , CYP2B6 , CYP2C19 , CYP2C8 , CYP2C9 , CYP2D6 , CYP3A4 , CYP3A5 , CYP4F2 , DPYD , G6PD , GSTP1 , NAT2 , NUDT15 , PTGIS , SLCO1B1 , SULT1A1 , TPMT , UGT1A1 , and VKORC1 . The output genotype calls.tsv files included alleles, diplotypes, phenotypes or functions, activity scores, and variant information (Figure ). Star allele analysis for CYP2D6 “PGx‐VCF” files obtained in the previous step were used for star allele analysis for CYP2D6 according to the Stargazer v2.0.2 CYP2D6 bioinformatic pipeline. Binary Alignment Map files were used to read depth for CYP2D6 and CYP2D7 with a new internal feature of Stargazer v2.0.2 and this output is called the GDF (GATK‐DepthOfCoverage format) file. Stargazer extracts all SNVs and indels within 3 kb from the VCF file, storing essential variant details for each sample. Both VCF and a corresponding GDF file were used for each sample to get outputs such as the reference allele, the alternate allele(s), the genotype status (homozygous or heterozygous), and the allelic depth for each sample. VDR was used as a control gene because it has low rates of whole gene deletion or duplication according to the database of genetic variants and gives identical copy number results for CYP2D6 and CYP2D7 . Alleles nomenclature/variants information/Phenotype and Scoring Algorithms Stargazer v2.0.2 output was cross‐checked with PharmVar, the CPIC gene information database, and PharmGKB. Stargazer v2.0.2 did not include identifier (rsID) details, using other variant information, rsIDs were obtained from the aforementioned PGx databases. For instance, for the G6PD and DPYD genes, Stargazer v2.0.2 provided results as alleles, but in the CPIC functionality table, the functions are based on the names. In such cases, we have first defined the alleles based on the position (CPIC allele definition table) and then cross‐checked with functions using the CPIC allele functionality table. We calculated diplotype frequencies and phenotypes or functions for 25 VIPGx genes, verified them against the CPIC diplotype–phenotype table, and documented any discrepancies in the phenotype category and activity score. Ethics statement The study was approved by the Ramathibodi Hospital Mahidol University faculty review board and adhered to the Declaration of Helsinki, The Belmont Report, CIOMS guidelines, and ICH‐GCP (COA. MURA2017/759/Ref.646).   data retrieval The pilot project for the Thailand Reference Genome Panel (Protocol No. ID11‐60‐12) collected samples from 942 individuals across various regions of Thailand. The study participants are the employees of the Electricity Generating Authority of Thailand (EGAT) from various socioeconomic backgrounds and health profiles in Thailand. The study participants include a wide range of occupations, reflecting the diversity within the EGAT workforce. This study included participants who decided to volunteer their DNA for WGS analysis.   VIPGx genes The 942 samples were sequenced using Illumina and HiSeq X technologies. The analysis commenced by aligning WGS reads to the reference genome sequence GRCh38, utilizing the Burrows–Wheeler Aligner (BWA). Identification and marking of duplicate reads were executed using GATK 4.40.0's “Mark Duplicates” tool. Subsequently, base quality score recalibration (BQSR) was performed by generating a recalibration data table with the “BaseRecalibrator” tool. For variant calling, Sentieon (version 201808.08) was employed, resulting in the generation of raw variant call format (VCF) files. During this process, variants with a missing rate >5%, samples with a missing rate >10%, or duplicate samples were removed. The raw VCF files obtained were utilized for PGx‐specific analysis. Our focus was on extracting variants of interest from specific genes, particularly 63 VIPGx pharmacogenes (VIPGx), including ABCB1 , ABCG2 , ABL1 , ACE , ADH1A , ADH1B , ADH1C , ADRB1 , ADRB2 , AHR , ALDH1A1 , ALK , ALOX5 , BCR , BRAF , BRCA1 , CACNA1S , CFTR , COMT , CYP1A2 , CYP2A13 , CYP2A6 , CYP2B6 , CYP2C19 , CYP2C8 , CYP2C9 , CYP2D6 , CYP2J2 , CYP3A4 , CYP3A5 , CYP4F2 , DPYD , DRD2 , EGFR , ERBB2 , F5 , G6PD , GSTP1 , HLA‐B , HMGCR , KCNH2 , KCNJ11 , KIT , KRAS , MTHFR , NAT2 , NQO1 , NR1I2 , NRAS , NUDT15 , P2RY1 , PTGIS , PTGS2 , RYR1 , SCN5A , SLC19A1 , SLC22A1 , SLCO1B1 , SULT1A1 , TPMT , UGT1A1 , VDR , VKORC1 . A BED file, containing the genomic coordinates of these 63 VIPGx genes listed in the PharmGKB database was downloaded from the UCSC genome database. Subsequently, the BCFtool (version 1.18) facilitated the extraction of these 63 VIPGx genes from raw VCF files, resulting in files named as “PGx‐VCF files.” These files underwent comprehensive annotation and cataloging through Golden Helix VARSeq 2.5.0 (build 14,706‐608a25a286) (Figure ).   The Stargazer v2.0.2 was employed for the precise calling of star alleles from a dataset comprising 942 whole‐genome sequences. Utilizing the “PGx‐VCF files” extracted in the preceding step as the input, Stargazer v2.0.2 identified star alleles associated with 25 VIPGx genes, including ABCB1 , CACNA1S , CFTR , CYP1A2 , CYP2A13 , CYP2A6 , CYP2B6 , CYP2C19 , CYP2C8 , CYP2C9 , CYP2D6 , CYP3A4 , CYP3A5 , CYP4F2 , DPYD , G6PD , GSTP1 , NAT2 , NUDT15 , PTGIS , SLCO1B1 , SULT1A1 , TPMT , UGT1A1 , and VKORC1 . The output genotype calls.tsv files included alleles, diplotypes, phenotypes or functions, activity scores, and variant information (Figure ). Star allele analysis for CYP2D6 “PGx‐VCF” files obtained in the previous step were used for star allele analysis for CYP2D6 according to the Stargazer v2.0.2 CYP2D6 bioinformatic pipeline. Binary Alignment Map files were used to read depth for CYP2D6 and CYP2D7 with a new internal feature of Stargazer v2.0.2 and this output is called the GDF (GATK‐DepthOfCoverage format) file. Stargazer extracts all SNVs and indels within 3 kb from the VCF file, storing essential variant details for each sample. Both VCF and a corresponding GDF file were used for each sample to get outputs such as the reference allele, the alternate allele(s), the genotype status (homozygous or heterozygous), and the allelic depth for each sample. VDR was used as a control gene because it has low rates of whole gene deletion or duplication according to the database of genetic variants and gives identical copy number results for CYP2D6 and CYP2D7 . Alleles nomenclature/variants information/Phenotype and Scoring Algorithms Stargazer v2.0.2 output was cross‐checked with PharmVar, the CPIC gene information database, and PharmGKB. Stargazer v2.0.2 did not include identifier (rsID) details, using other variant information, rsIDs were obtained from the aforementioned PGx databases. For instance, for the G6PD and DPYD genes, Stargazer v2.0.2 provided results as alleles, but in the CPIC functionality table, the functions are based on the names. In such cases, we have first defined the alleles based on the position (CPIC allele definition table) and then cross‐checked with functions using the CPIC allele functionality table. We calculated diplotype frequencies and phenotypes or functions for 25 VIPGx genes, verified them against the CPIC diplotype–phenotype table, and documented any discrepancies in the phenotype category and activity score.   CYP2D6 “PGx‐VCF” files obtained in the previous step were used for star allele analysis for CYP2D6 according to the Stargazer v2.0.2 CYP2D6 bioinformatic pipeline. Binary Alignment Map files were used to read depth for CYP2D6 and CYP2D7 with a new internal feature of Stargazer v2.0.2 and this output is called the GDF (GATK‐DepthOfCoverage format) file. Stargazer extracts all SNVs and indels within 3 kb from the VCF file, storing essential variant details for each sample. Both VCF and a corresponding GDF file were used for each sample to get outputs such as the reference allele, the alternate allele(s), the genotype status (homozygous or heterozygous), and the allelic depth for each sample. VDR was used as a control gene because it has low rates of whole gene deletion or duplication according to the database of genetic variants and gives identical copy number results for CYP2D6 and CYP2D7 .   Stargazer v2.0.2 output was cross‐checked with PharmVar, the CPIC gene information database, and PharmGKB. Stargazer v2.0.2 did not include identifier (rsID) details, using other variant information, rsIDs were obtained from the aforementioned PGx databases. For instance, for the G6PD and DPYD genes, Stargazer v2.0.2 provided results as alleles, but in the CPIC functionality table, the functions are based on the names. In such cases, we have first defined the alleles based on the position (CPIC allele definition table) and then cross‐checked with functions using the CPIC allele functionality table. We calculated diplotype frequencies and phenotypes or functions for 25 VIPGx genes, verified them against the CPIC diplotype–phenotype table, and documented any discrepancies in the phenotype category and activity score.   The study was approved by the Ramathibodi Hospital Mahidol University faculty review board and adhered to the Declaration of Helsinki, The Belmont Report, CIOMS guidelines, and ICH‐GCP (COA. MURA2017/759/Ref.646).   Characteristics of the study population The study population consisted mainly of the EGAT cohort, a long‐term cohort study of the employees of the Electricity Generating Authority of Thailand (71.6%). The remaining samples were from the Faculty of Tropical Medicine, Mahidol University (28.2%). Age and gender information were missing for 38.9% and 38.6% of the study population, respectively. Based on the available data, the average age was found to be 57.1 ± 7.4, with a male preponderance (Table ). Variants analysis Genetic variants reported in 63 VIPGx genes The analysis of 63 VIPGx genes revealed 85,566 variants, of which 13,532 are novel variants and 72,034 are known variants (with rsID). The study reveals that the ALK ( n = 14, 572) and DPYD ( n = 10, 333) genes exhibit significantly higher variants, indicating a higher likelihood of genetic variations or mutations within these genomic regions (Figure ). This study identified variants from 32 tier‐1 genes with significant importance in PGx and recommended CPIC clinical guidelines, 22 tier‐2 genes with limited evidence, and 9 genes associated with tumor pharmacogenomics (Table ). Among 13, 532 novel variants identified, 275 variants are clinically relevant missense variants, and it has been presented along with their effect on coding and protein sequence in Table . Out of 72,034 known variants, 464 were found to be significantly associated with the Pharmacokinetics/Pharmacodynamics (PK/PD) and toxicities of various drugs. Golden Helix VARSeq 2.5.0 (build 14706‐608a25a286) used PharmGKB Variants 2019‐06‐19, GHI, to annotate these variants and their associations with PK/PD and toxicity. Known PGx variants reported (of 72,034) in 63 VIPGx genes This study found 464 known variants in 72, 034 known variants (Figure ). The majority of reported variants, ~71.9% are common, whereas 10.7% and 17.2% are low frequent and rare variants, respectively. Out of 464 variants, 220 affect the PK/PD of at least one drug, while 244 affect the PK/PD of multiple drugs. The majority of variants (46.1%) did not have any “PharmGKB significance level” (clinical relevance), but almost 41% of the variants were reported with “PharmGKB 3‐level significance” and 8.11% of the variants with “PharmGKB 4‐level significance.” Out of 464 known PGx variants, 183 were annotated to be associated with various diseases. Drugs affected by these variants include anti‐neoplastic and immunomodulatory drugs ( n = 153), cardiovascular drugs ( n = 96), and antipsychotics and antiepileptic drugs ( n = 50). The sequence ontology reveals that intron variants have the highest count at 265, followed by missense variants ( n = 115), synonymous variants ( n = 34), stop‐gained variants ( n = 3), and splice acceptor variants ( n = 1) (Table ). The known PGx variants that are relevant to both PK/PD and toxicities are listed in Table . All the 464 variants were listed based on genes, along with their frequencies in the Thai and other populations in Table , based on association with only PK/PD in Table , only with toxicities in Table . The difference in variant frequency between the Thai population and East Asians, whether less than or more than 0.10, is presented in Figures and . Cataloging variants that are not called in star allele analysis Stargazer v2.0.2 successfully called all variants reported in variant analysis in genes like CYP3A5 , NUDT15 , CFTR , and SULT1A1 in this study. For several other genes, additional variants were identified in variant analysis and it has been documented along with their corresponding rsID in Table . Likewise, the star allele analysis called for many other alleles or variants that were not annotated in variant analysis. Star allele analysis Based on their output, the Stargazer v2.0.2 results were documented separately. For example, Stargazer v2.0.2 identified star alleles, diplotypes, phenotypes, and activity scores for 15 of the 25 VIPGx genes, and for the remaining 10 of them, the data showed functions rather than phenotypes. The frequency of diplotype and phenotypes associated with 15 VIPGx genes in the Thai population The study population has a diverse metabolic profile for each gene. However, as the diplotype–phenotype frequency for each gene was compared with the CPIC and other PGx databases, a slight variation of metabolic profile in the total population was seen. This study found that most Thais had a normal metabolic profile for several genes, including CYP2A13 (85.8%), CYP2A6 (97.3%), CYP2B6 (82.8%), CYP2C8 (97.8%), CYP4F2 (93.9%), DPYD (98.6%), NUDT15 (77.8%), and TPMT (94%) (Table ). The WGS data showed varying prevalence rates for genes related to intermediate metabolizers, including CYP2C19 (42.5%), CYP2D6 (47%), CYP2A6 (2.1%), CYP2C9 (9.2%), CYP3A5 (45.2%), CYP4F2 (6.1%), NUDT15 (14.9%), and TPMT (6%). The study identified several alleles ( *2 , *3 , *5 , *16 , *34 , *35 , and *38 ) with no ( *2 , *3 , *5 ,) or decreased functional alleles ( *16 ), with frequencies ranging from 0.1% to 27% for CYP2C19 . The diplotype frequency for CYP2C19 indicates that 42% of individuals were IMs with an activity score of 1. Likewise, IMs were highly frequent for CYP3A5 (45.22%), with a distribution of *1 for normal function and *3 for no function. Several CYP2C9 alleles ( *2 , *3 , *8 , *11 , *14 , *16 , *19 , *29 , *44 , *58 , *66 , *73 ) have been reported to have decreased or uncertain function, with activity scores ranging from 0.5–1 in IMs in this study. On the other hand, for NUDT15 , IMs have allele combinations like *1/*13 , *1/*2 , and *1/*3 , with frequencies of 0.1%, 3.6%, and 11.3%, respectively (Table ). This study found that the *1 allele is prevalent in 97.1% of TPMT , while the *3C allele is present in 3.1%, indicating no function. IMs have genotype of *1/*29 and *1/*3C , with frequencies of 0.1% and 5.5%, respectively. For CYP4F2 gene, the study found that 6.05% of people had carried ( *3/*3 ) genotype for IMs (Table ). This study found that 2.1% are IMs for CYP2A6 . In the case of poor metabolizer phenotype, CYP2C19 (9.9%) and CYP2D6 (0.3%) genes have higher frequencies of poor metabolizers. The current analysis revealed that CYP1A2 (52.7%), CYP2B6 (7.6%), and CYP2D6 (0.4%) had significantly high numbers of UMs (Table ). The frequency of diplotypes and functions associated with 10 VIPGx genes in the Thai population The majority of the study group carries normal function alleles for the CACNA1S (99.9%), CFTR (99%), and VKORC1 (99.9%) genes (Table ). Star allele analysis revealed a significant number of individuals with decreased function alleles for certain genes, particularly NAT2 (84.1%), SULT1A1 (11.7%), and G6PD (12.3%). For instance, in the NAT2 genotype data, diplotypes linked with reduced function, such as *1/*6 , *1/*7 , *6/*6 , and *6/*7 , were observed to occur more frequently, with corresponding activity scores of 1.5, 1.5, 1, and 1, respectively. Similarly, in the case of SULT1A1 , the *2/*2 genotype, linked with diminished function, is observed in 6.3% of individuals, while the functional status of the *3 allele remains uncertain in 0.3% of cases. Concerning G6PD , the *1 allele is primarily linked with normal function, whereas other alleles—including *8 , *22 , *28 , *50 , *51 , *126 , and *171— are associated with reduced function. This study also found that 42.4%, and 4.1%, of participants carry increased functional alleles for genes like PTGIS , SLCO1B1 , respectively. For instance, PTGIS reports increased function in the haplotype *1D . The intermediate function (IMF) category comprises the genotypes *1/*1D , *1/*1E , *1D/*1D , *1D/*1E , and *1E/*1E , with frequencies ranging from 3.07% to 16.45% (Table ). This study reported eight different alleles for SLCO1B1 *1 (24.4%) and *37 (63%) were found to have normal function, whereas *14 and *20 were found to have increased function with frequencies of 0.3 and 1.9, respectively. The *20/*37 and *1/*20 genotypes are associated with increased function, in 27 and 3 individuals, respectively. A poor functional allele was reported, primarily in the only gene that is SLCO1B1 , with a frequency of 19.6%. The *1/*15 and *15/*37 genotypes, indicating poor function, were observed in 49 and 121 individuals, respectively (Table ). All the reported alleles, their functions, and allele frequencies are listed in Table . The diplotype frequency of CYP2D6 The study identified 48% of NMs, 47% of IMs, 0.5% of UMs, 0.3% of PMs, and 3.1% of UKMs. This study found many novel diplotypes (not recorded in the CPIC diplotype–phenotype table), especially *1/*36 + *10 (25.22%) and *2/*36 + *10 (8.2%), reported in higher frequency among NMs. PharmVar presents *36 + *10 as a tandem or non‐identical duplication. We found that the activity scores for normal metabolizers, which ranged from 1.25 to 2.25, were consistent with the CPIC score. The study revealed structural variations in the normal metabolizer category, such as identical gene duplication or multiplication (*10x2 , *1x2 , *14x2 , and *34x2) and non‐identical duplication (*S2 + *1 , *36 + *10) . Similarly, the study found a high frequency of structural variations in IMs, including *10/*36 + *10 (22.43%). Many structural variants reported among IMs in this study have not been found in the CPIC diplotype–phenotype database. The activity score of IMs ranges from 0.25–1 and is consistent with the CPIC score. The study found three PMs and four UMs, but the phenotype of 31 participants' was unknown. Of these unknown phenotypes, *1/*144 and *41/*144 were likely IMs according to CPIC, with activity scores of 1 and 0.25, respectively (Table ).   The study population consisted mainly of the EGAT cohort, a long‐term cohort study of the employees of the Electricity Generating Authority of Thailand (71.6%). The remaining samples were from the Faculty of Tropical Medicine, Mahidol University (28.2%). Age and gender information were missing for 38.9% and 38.6% of the study population, respectively. Based on the available data, the average age was found to be 57.1 ± 7.4, with a male preponderance (Table ).   Genetic variants reported in 63 VIPGx genes The analysis of 63 VIPGx genes revealed 85,566 variants, of which 13,532 are novel variants and 72,034 are known variants (with rsID). The study reveals that the ALK ( n = 14, 572) and DPYD ( n = 10, 333) genes exhibit significantly higher variants, indicating a higher likelihood of genetic variations or mutations within these genomic regions (Figure ). This study identified variants from 32 tier‐1 genes with significant importance in PGx and recommended CPIC clinical guidelines, 22 tier‐2 genes with limited evidence, and 9 genes associated with tumor pharmacogenomics (Table ). Among 13, 532 novel variants identified, 275 variants are clinically relevant missense variants, and it has been presented along with their effect on coding and protein sequence in Table . Out of 72,034 known variants, 464 were found to be significantly associated with the Pharmacokinetics/Pharmacodynamics (PK/PD) and toxicities of various drugs. Golden Helix VARSeq 2.5.0 (build 14706‐608a25a286) used PharmGKB Variants 2019‐06‐19, GHI, to annotate these variants and their associations with PK/PD and toxicity. Known PGx variants reported (of 72,034) in 63 VIPGx genes This study found 464 known variants in 72, 034 known variants (Figure ). The majority of reported variants, ~71.9% are common, whereas 10.7% and 17.2% are low frequent and rare variants, respectively. Out of 464 variants, 220 affect the PK/PD of at least one drug, while 244 affect the PK/PD of multiple drugs. The majority of variants (46.1%) did not have any “PharmGKB significance level” (clinical relevance), but almost 41% of the variants were reported with “PharmGKB 3‐level significance” and 8.11% of the variants with “PharmGKB 4‐level significance.” Out of 464 known PGx variants, 183 were annotated to be associated with various diseases. Drugs affected by these variants include anti‐neoplastic and immunomodulatory drugs ( n = 153), cardiovascular drugs ( n = 96), and antipsychotics and antiepileptic drugs ( n = 50). The sequence ontology reveals that intron variants have the highest count at 265, followed by missense variants ( n = 115), synonymous variants ( n = 34), stop‐gained variants ( n = 3), and splice acceptor variants ( n = 1) (Table ). The known PGx variants that are relevant to both PK/PD and toxicities are listed in Table . All the 464 variants were listed based on genes, along with their frequencies in the Thai and other populations in Table , based on association with only PK/PD in Table , only with toxicities in Table . The difference in variant frequency between the Thai population and East Asians, whether less than or more than 0.10, is presented in Figures and . Cataloging variants that are not called in star allele analysis Stargazer v2.0.2 successfully called all variants reported in variant analysis in genes like CYP3A5 , NUDT15 , CFTR , and SULT1A1 in this study. For several other genes, additional variants were identified in variant analysis and it has been documented along with their corresponding rsID in Table . Likewise, the star allele analysis called for many other alleles or variants that were not annotated in variant analysis.   VIPGx genes The analysis of 63 VIPGx genes revealed 85,566 variants, of which 13,532 are novel variants and 72,034 are known variants (with rsID). The study reveals that the ALK ( n = 14, 572) and DPYD ( n = 10, 333) genes exhibit significantly higher variants, indicating a higher likelihood of genetic variations or mutations within these genomic regions (Figure ). This study identified variants from 32 tier‐1 genes with significant importance in PGx and recommended CPIC clinical guidelines, 22 tier‐2 genes with limited evidence, and 9 genes associated with tumor pharmacogenomics (Table ). Among 13, 532 novel variants identified, 275 variants are clinically relevant missense variants, and it has been presented along with their effect on coding and protein sequence in Table . Out of 72,034 known variants, 464 were found to be significantly associated with the Pharmacokinetics/Pharmacodynamics (PK/PD) and toxicities of various drugs. Golden Helix VARSeq 2.5.0 (build 14706‐608a25a286) used PharmGKB Variants 2019‐06‐19, GHI, to annotate these variants and their associations with PK/PD and toxicity.   PGx variants reported (of 72,034) in 63 VIPGx genes This study found 464 known variants in 72, 034 known variants (Figure ). The majority of reported variants, ~71.9% are common, whereas 10.7% and 17.2% are low frequent and rare variants, respectively. Out of 464 variants, 220 affect the PK/PD of at least one drug, while 244 affect the PK/PD of multiple drugs. The majority of variants (46.1%) did not have any “PharmGKB significance level” (clinical relevance), but almost 41% of the variants were reported with “PharmGKB 3‐level significance” and 8.11% of the variants with “PharmGKB 4‐level significance.” Out of 464 known PGx variants, 183 were annotated to be associated with various diseases. Drugs affected by these variants include anti‐neoplastic and immunomodulatory drugs ( n = 153), cardiovascular drugs ( n = 96), and antipsychotics and antiepileptic drugs ( n = 50). The sequence ontology reveals that intron variants have the highest count at 265, followed by missense variants ( n = 115), synonymous variants ( n = 34), stop‐gained variants ( n = 3), and splice acceptor variants ( n = 1) (Table ). The known PGx variants that are relevant to both PK/PD and toxicities are listed in Table . All the 464 variants were listed based on genes, along with their frequencies in the Thai and other populations in Table , based on association with only PK/PD in Table , only with toxicities in Table . The difference in variant frequency between the Thai population and East Asians, whether less than or more than 0.10, is presented in Figures and .   Stargazer v2.0.2 successfully called all variants reported in variant analysis in genes like CYP3A5 , NUDT15 , CFTR , and SULT1A1 in this study. For several other genes, additional variants were identified in variant analysis and it has been documented along with their corresponding rsID in Table . Likewise, the star allele analysis called for many other alleles or variants that were not annotated in variant analysis.   Based on their output, the Stargazer v2.0.2 results were documented separately. For example, Stargazer v2.0.2 identified star alleles, diplotypes, phenotypes, and activity scores for 15 of the 25 VIPGx genes, and for the remaining 10 of them, the data showed functions rather than phenotypes. The frequency of diplotype and phenotypes associated with 15 VIPGx genes in the Thai population The study population has a diverse metabolic profile for each gene. However, as the diplotype–phenotype frequency for each gene was compared with the CPIC and other PGx databases, a slight variation of metabolic profile in the total population was seen. This study found that most Thais had a normal metabolic profile for several genes, including CYP2A13 (85.8%), CYP2A6 (97.3%), CYP2B6 (82.8%), CYP2C8 (97.8%), CYP4F2 (93.9%), DPYD (98.6%), NUDT15 (77.8%), and TPMT (94%) (Table ). The WGS data showed varying prevalence rates for genes related to intermediate metabolizers, including CYP2C19 (42.5%), CYP2D6 (47%), CYP2A6 (2.1%), CYP2C9 (9.2%), CYP3A5 (45.2%), CYP4F2 (6.1%), NUDT15 (14.9%), and TPMT (6%). The study identified several alleles ( *2 , *3 , *5 , *16 , *34 , *35 , and *38 ) with no ( *2 , *3 , *5 ,) or decreased functional alleles ( *16 ), with frequencies ranging from 0.1% to 27% for CYP2C19 . The diplotype frequency for CYP2C19 indicates that 42% of individuals were IMs with an activity score of 1. Likewise, IMs were highly frequent for CYP3A5 (45.22%), with a distribution of *1 for normal function and *3 for no function. Several CYP2C9 alleles ( *2 , *3 , *8 , *11 , *14 , *16 , *19 , *29 , *44 , *58 , *66 , *73 ) have been reported to have decreased or uncertain function, with activity scores ranging from 0.5–1 in IMs in this study. On the other hand, for NUDT15 , IMs have allele combinations like *1/*13 , *1/*2 , and *1/*3 , with frequencies of 0.1%, 3.6%, and 11.3%, respectively (Table ). This study found that the *1 allele is prevalent in 97.1% of TPMT , while the *3C allele is present in 3.1%, indicating no function. IMs have genotype of *1/*29 and *1/*3C , with frequencies of 0.1% and 5.5%, respectively. For CYP4F2 gene, the study found that 6.05% of people had carried ( *3/*3 ) genotype for IMs (Table ). This study found that 2.1% are IMs for CYP2A6 . In the case of poor metabolizer phenotype, CYP2C19 (9.9%) and CYP2D6 (0.3%) genes have higher frequencies of poor metabolizers. The current analysis revealed that CYP1A2 (52.7%), CYP2B6 (7.6%), and CYP2D6 (0.4%) had significantly high numbers of UMs (Table ). The frequency of diplotypes and functions associated with 10 VIPGx genes in the Thai population The majority of the study group carries normal function alleles for the CACNA1S (99.9%), CFTR (99%), and VKORC1 (99.9%) genes (Table ). Star allele analysis revealed a significant number of individuals with decreased function alleles for certain genes, particularly NAT2 (84.1%), SULT1A1 (11.7%), and G6PD (12.3%). For instance, in the NAT2 genotype data, diplotypes linked with reduced function, such as *1/*6 , *1/*7 , *6/*6 , and *6/*7 , were observed to occur more frequently, with corresponding activity scores of 1.5, 1.5, 1, and 1, respectively. Similarly, in the case of SULT1A1 , the *2/*2 genotype, linked with diminished function, is observed in 6.3% of individuals, while the functional status of the *3 allele remains uncertain in 0.3% of cases. Concerning G6PD , the *1 allele is primarily linked with normal function, whereas other alleles—including *8 , *22 , *28 , *50 , *51 , *126 , and *171— are associated with reduced function. This study also found that 42.4%, and 4.1%, of participants carry increased functional alleles for genes like PTGIS , SLCO1B1 , respectively. For instance, PTGIS reports increased function in the haplotype *1D . The intermediate function (IMF) category comprises the genotypes *1/*1D , *1/*1E , *1D/*1D , *1D/*1E , and *1E/*1E , with frequencies ranging from 3.07% to 16.45% (Table ). This study reported eight different alleles for SLCO1B1 *1 (24.4%) and *37 (63%) were found to have normal function, whereas *14 and *20 were found to have increased function with frequencies of 0.3 and 1.9, respectively. The *20/*37 and *1/*20 genotypes are associated with increased function, in 27 and 3 individuals, respectively. A poor functional allele was reported, primarily in the only gene that is SLCO1B1 , with a frequency of 19.6%. The *1/*15 and *15/*37 genotypes, indicating poor function, were observed in 49 and 121 individuals, respectively (Table ). All the reported alleles, their functions, and allele frequencies are listed in Table . The diplotype frequency of CYP2D6 The study identified 48% of NMs, 47% of IMs, 0.5% of UMs, 0.3% of PMs, and 3.1% of UKMs. This study found many novel diplotypes (not recorded in the CPIC diplotype–phenotype table), especially *1/*36 + *10 (25.22%) and *2/*36 + *10 (8.2%), reported in higher frequency among NMs. PharmVar presents *36 + *10 as a tandem or non‐identical duplication. We found that the activity scores for normal metabolizers, which ranged from 1.25 to 2.25, were consistent with the CPIC score. The study revealed structural variations in the normal metabolizer category, such as identical gene duplication or multiplication (*10x2 , *1x2 , *14x2 , and *34x2) and non‐identical duplication (*S2 + *1 , *36 + *10) . Similarly, the study found a high frequency of structural variations in IMs, including *10/*36 + *10 (22.43%). Many structural variants reported among IMs in this study have not been found in the CPIC diplotype–phenotype database. The activity score of IMs ranges from 0.25–1 and is consistent with the CPIC score. The study found three PMs and four UMs, but the phenotype of 31 participants' was unknown. Of these unknown phenotypes, *1/*144 and *41/*144 were likely IMs according to CPIC, with activity scores of 1 and 0.25, respectively (Table ).   VIPGx genes in the Thai population The study population has a diverse metabolic profile for each gene. However, as the diplotype–phenotype frequency for each gene was compared with the CPIC and other PGx databases, a slight variation of metabolic profile in the total population was seen. This study found that most Thais had a normal metabolic profile for several genes, including CYP2A13 (85.8%), CYP2A6 (97.3%), CYP2B6 (82.8%), CYP2C8 (97.8%), CYP4F2 (93.9%), DPYD (98.6%), NUDT15 (77.8%), and TPMT (94%) (Table ). The WGS data showed varying prevalence rates for genes related to intermediate metabolizers, including CYP2C19 (42.5%), CYP2D6 (47%), CYP2A6 (2.1%), CYP2C9 (9.2%), CYP3A5 (45.2%), CYP4F2 (6.1%), NUDT15 (14.9%), and TPMT (6%). The study identified several alleles ( *2 , *3 , *5 , *16 , *34 , *35 , and *38 ) with no ( *2 , *3 , *5 ,) or decreased functional alleles ( *16 ), with frequencies ranging from 0.1% to 27% for CYP2C19 . The diplotype frequency for CYP2C19 indicates that 42% of individuals were IMs with an activity score of 1. Likewise, IMs were highly frequent for CYP3A5 (45.22%), with a distribution of *1 for normal function and *3 for no function. Several CYP2C9 alleles ( *2 , *3 , *8 , *11 , *14 , *16 , *19 , *29 , *44 , *58 , *66 , *73 ) have been reported to have decreased or uncertain function, with activity scores ranging from 0.5–1 in IMs in this study. On the other hand, for NUDT15 , IMs have allele combinations like *1/*13 , *1/*2 , and *1/*3 , with frequencies of 0.1%, 3.6%, and 11.3%, respectively (Table ). This study found that the *1 allele is prevalent in 97.1% of TPMT , while the *3C allele is present in 3.1%, indicating no function. IMs have genotype of *1/*29 and *1/*3C , with frequencies of 0.1% and 5.5%, respectively. For CYP4F2 gene, the study found that 6.05% of people had carried ( *3/*3 ) genotype for IMs (Table ). This study found that 2.1% are IMs for CYP2A6 . In the case of poor metabolizer phenotype, CYP2C19 (9.9%) and CYP2D6 (0.3%) genes have higher frequencies of poor metabolizers. The current analysis revealed that CYP1A2 (52.7%), CYP2B6 (7.6%), and CYP2D6 (0.4%) had significantly high numbers of UMs (Table ).   VIPGx genes in the Thai population The majority of the study group carries normal function alleles for the CACNA1S (99.9%), CFTR (99%), and VKORC1 (99.9%) genes (Table ). Star allele analysis revealed a significant number of individuals with decreased function alleles for certain genes, particularly NAT2 (84.1%), SULT1A1 (11.7%), and G6PD (12.3%). For instance, in the NAT2 genotype data, diplotypes linked with reduced function, such as *1/*6 , *1/*7 , *6/*6 , and *6/*7 , were observed to occur more frequently, with corresponding activity scores of 1.5, 1.5, 1, and 1, respectively. Similarly, in the case of SULT1A1 , the *2/*2 genotype, linked with diminished function, is observed in 6.3% of individuals, while the functional status of the *3 allele remains uncertain in 0.3% of cases. Concerning G6PD , the *1 allele is primarily linked with normal function, whereas other alleles—including *8 , *22 , *28 , *50 , *51 , *126 , and *171— are associated with reduced function. This study also found that 42.4%, and 4.1%, of participants carry increased functional alleles for genes like PTGIS , SLCO1B1 , respectively. For instance, PTGIS reports increased function in the haplotype *1D . The intermediate function (IMF) category comprises the genotypes *1/*1D , *1/*1E , *1D/*1D , *1D/*1E , and *1E/*1E , with frequencies ranging from 3.07% to 16.45% (Table ). This study reported eight different alleles for SLCO1B1 *1 (24.4%) and *37 (63%) were found to have normal function, whereas *14 and *20 were found to have increased function with frequencies of 0.3 and 1.9, respectively. The *20/*37 and *1/*20 genotypes are associated with increased function, in 27 and 3 individuals, respectively. A poor functional allele was reported, primarily in the only gene that is SLCO1B1 , with a frequency of 19.6%. The *1/*15 and *15/*37 genotypes, indicating poor function, were observed in 49 and 121 individuals, respectively (Table ). All the reported alleles, their functions, and allele frequencies are listed in Table .   CYP2D6 The study identified 48% of NMs, 47% of IMs, 0.5% of UMs, 0.3% of PMs, and 3.1% of UKMs. This study found many novel diplotypes (not recorded in the CPIC diplotype–phenotype table), especially *1/*36 + *10 (25.22%) and *2/*36 + *10 (8.2%), reported in higher frequency among NMs. PharmVar presents *36 + *10 as a tandem or non‐identical duplication. We found that the activity scores for normal metabolizers, which ranged from 1.25 to 2.25, were consistent with the CPIC score. The study revealed structural variations in the normal metabolizer category, such as identical gene duplication or multiplication (*10x2 , *1x2 , *14x2 , and *34x2) and non‐identical duplication (*S2 + *1 , *36 + *10) . Similarly, the study found a high frequency of structural variations in IMs, including *10/*36 + *10 (22.43%). Many structural variants reported among IMs in this study have not been found in the CPIC diplotype–phenotype database. The activity score of IMs ranges from 0.25–1 and is consistent with the CPIC score. The study found three PMs and four UMs, but the phenotype of 31 participants' was unknown. Of these unknown phenotypes, *1/*144 and *41/*144 were likely IMs according to CPIC, with activity scores of 1 and 0.25, respectively (Table ).   The study utilized 942 WGS datasets to analyze 63 VIPGx gene variants, frequencies, phenotypes, and functional alleles of 25 VIPGx genes, providing a comprehensive pharmacogenomic landscape of the Thai population. Disparities exist in the number of genetic variants found in the 63 VIPGx genes. For example, ALK is known to have many variants ( n = 14,571), while genes such as ADRB2 ( n = 39) and KCNJIL ( n = 62) were found to have the least number of variants in this study. Gene complexity, size, population‐specific variations, selection pressures, evolutionary factors, mutation rate, functional constraints, and research focus all influence gene diversity. This study identified 464 known PGx variants, with significant clinical evidence linking them to drug response and toxicity. Fifteen variants of these 464 different genes were found to be highly frequent among the Thai population than East Asians (EAs), likewise, the frequency of around 12 variants was found to be less in the Thai population than in EAs. The differences in variant frequencies highlight the genetic diversity within Asian populations. So, understanding the frequency of rare and common variants among populations and their functional impact on drug response is crucial in personalized and pharmacogenomic studies. However, further research is warranted to elucidate differences statistically. Star allele nomenclature is a user‐friendly method for identifying gene‐level haplotype patterns, including variants like SNPs, InDels, and CNVs, making it accessible for non‐specialized professionals. PharmVar has systematically converted 14 genes' variants into star nomenclature for approximately 14 genes. The identified novel alleles in this study can be included in the already existing system with new allele numbers. Phenotype associated with PGx genes Genes reported with high proportion of IM / PMs In case CYP2C19 , the reported frequency of IM and PM is similar to earlier studies that have been reported in the Thai population. , Drugs like clopidogrel antidepressant , and proton pump inhibitors must be cautiously used in intermediate Thai metabolizers. It is appropriate to consider incorporating CYP2C19 genetic testing before starting clopidogrel treatment in Thailand, as well as discussing the possibility of using prasugrel and ticagrelor as empirical therapy for the Thai population. However, it is important to thoroughly assess the economic aspects of genotyping costs and alternative drug costs, as well as other factors like bleeding risk and patient adherence. These considerations are necessary to determine the suitability of these drugs for wider empirical use. Earlier TagSNP methods reported *1 , *2 , *3 , and *17 alleles for CYP2C19 , but in this study, new alleles, such as *35 , *38 , *5 , *16 , and *34 ( 7C>T (P3S) have been reported, no *17 allele was called in this study. The phenotype prevalence of CYP3A5 is similar to earlier studies reported in Thai and Asian populations. , , CYP3A5 variations predict tacrolimus dose requirements, as individuals with the CYP3A5*1 allele require a higher daily dose to maintain the desired target trough level than CYP3A5*3/*3 (non‐expressers). , Literature highlights CYP3A5*1 , *2 , *3s role in breast cancer treatment kidney transplant outcomes and non‐small‐cell lung cancer prognosis highlighting its potential in tamoxifen toxicity minimization. The study found that almost 15% of the population is an IMs for the NUDT15 gene, a crucial pharmacogene in thiopurines response such as mercaptopurine and azathioprine. It is advisable to consider reduced starting doses to minimize toxicity, especially if the initial doses are high for NUDT15 (e.g., 75 mg/m 2 /day for mercaptopurine). , , Sertraline‐ CYP2B6 dosing recommendations for IMs may be adjusted for reduced metabolism (25%) and elevated concentration, but these are optional due to a lack of clinical outcome data. Also, CYP2B6 IMs may experience higher dose‐adjusted trough concentrations, potentially increasing the risk of adverse effects, suggesting a moderate recommendation for a reduced efavirenz dose. For UGT1A1 , 930 Thai people have normal function, and 12 carry increased functional genotypes. The study investigates the prevalence of UGT1A1 variants in Malaysia, revealing their association with reduced enzyme activity and increased irinotecan toxicity in Malays, Chinese, and Indians. A study from Japan found no significant correlation between the UGT1A1*27 polymorphism and severe irinotecan toxicity in lung cancer patients, indicating its inconsistency. A Thailand‐based pyrosequencing study found allele frequencies for UGT1A1 genetic polymorphisms as *1/*1 , *1/*6 , *1/*28 , *28/*6 , and *28/*28 , with no homozygous mutation of UGT1A1*6 . The combination of UGT1A1*28 and *6 polymorphisms emerged as a strong risk factor for severe hyperbilirubinemia in Thai neonates, suggesting a need for closer observation in neonates with these genetic variations to prevent more severe bilirubin levels. However, in this study, star allele analysis did not call for the *28 (Del) allele for this gene. Genes reported with high proportion of UMs The study identified 52.7% ultra‐rapid metabolizers in the study population, with 92.25% carrying alleles of *1 and *1F for CYP1A2 . Literature shows that heterozygous genotypes do not affect drug metabolism, especially caffeine metabolism, in non‐smoking individuals taking omeprazole. On the other hand, for CYP2B6 , 7.6% of the Thai study population carries UM alleles such as *1 (3.3%), *2 (0.5%), *4 (3.7%) with genotypes of *1/*4 , *2/*4 , and *4/*4 . However, literature shows that because of the small increase in metabolism in CYP2B6 UMs and RMs, no pre‐emptive dose increase is recommended. Genes reported with high proportion of various abnormal functional alleles ABCB1 , also known as MDRP1 , is a member of the ABC transporter superfamily, with 72% of patients carrying normal function alleles and 15.5% carrying uncertain functional alleles in this study. In vivo assessments with the drug fexofenadine reveal that individuals with the MDR1*2 variant exhibit altered P‐glycoprotein activity, leading to differences in drug levels. SULT1A1 plays a key role in the bioactivation of procarcinogens and the sulfate conjugation of steroid hormones, catecholamines, and phenolic drugs. The frequency of alleles is similar to the already reported Thai study, which investigated the influence of SULT1A1*2 polymorphism on plasma efavirenz (EFV) concentration in Thai HIV‐1 patients. The study suggests that a combination of SULT1A1*2 and SULT1A1 copy number may influence EFV metabolism and drug response, requiring further investigation in a larger study population. The CPIC guideline advises cautious prescribing of HMG CoA reductase inhibitors for patients with certain genetic variants, such as SLCO1B1*5/*5 or *15 homozygotes on simvastatin. A study that explored the genotype frequencies of the S LCO1B1 and ABCG2 genes, including their predicted phenotypes, in 53 Thai participants, reported that 69.81% had normal function 13.21% had intermediate function ( *1A/*17 , *1B/*15 . and *1B/*17 ), and 7.55% had low function ( *5/*15 , *15/*15 , and *15/*17 ) and our current results are similar to thoseof earlier literature. Genetic variants of NAT2 are primarily associated with the metabolism, response, and toxicities of multiple drugs, especially anti‐infective agents. A study in Thailand found key NAT2 allele frequency distributions in the Thai population, including NAT2*4 (0.38), NAT2*5 (0.038), NAT2*6 (0.32), and NAT2*7 (0.204). Our study found new decreased functional alleles like *12 , *13 , and *19 , suggesting dose adjustment or alternative therapy is needed for slow acetylators. GSTP1 variants have been associated with the toxicity and outcome of several cancer drugs, especially platinum agents. In this study, *2 allele was reported to have a higher frequency (23.5%), hence PGx‐guided platinum chemotherapy can be administered in the Thai population. G6PD increases the risk of adverse drug reactions of many drugs, including primaquine, chloroquine, dapsone, metformin, and glibenclamide. In star real analysis, 12.31% of people have the genotype of decreased function, and this is similar to the already existing literature. According to the 2014 CPIC guidelines, the primary screening method is a quantitative enzyme assay, unless pre‐emptive genotyping confirms G6PD deficiency. As this study found a noticeable number of decreased functional alleles in the Thai population, rasburicase, nitrofurantoin, and primaquine should be administered with precaution. The findings of this study revealed a notable occurrence of the *1 (24.2%) and *10 (20%) alleles, along with a non‐identical gene duplication of *36 + *10 (28.2%) for CYP2D6 . These results align with previous research conducted among the Thai population. CPIC has recommended guidelines for testing CYP2D6 for various drugs, such as codeine and other opioids, antidepressants, and tamoxifen. Suzuki et al., (2021) found that specific CYP2D6 gene variant combinations, such as *1/*5 , *1/*10 , *2/*5 , *2/*10 , *5/*10 , and *10/*10 increase n‐desmethyltramadol levels in individuals with head and neck neoplasms. As the CYP2D6 activity score decreases, concentrations of N‐desmethyltramadol tend to increase. However, there was no significant association found between the CYP2D6 activity score and concentrations of tramadol or O‐desmethyltramadol (CPIC) guidelines. A Thailand study shows that revised CYP2D6 genotype to phenotype groupings improve the relationship between genotype and risperidone measures in autism patients, challenging substrate‐dependent phenotype classification.   PGx genes Genes reported with high proportion of IM / PMs In case CYP2C19 , the reported frequency of IM and PM is similar to earlier studies that have been reported in the Thai population. , Drugs like clopidogrel antidepressant , and proton pump inhibitors must be cautiously used in intermediate Thai metabolizers. It is appropriate to consider incorporating CYP2C19 genetic testing before starting clopidogrel treatment in Thailand, as well as discussing the possibility of using prasugrel and ticagrelor as empirical therapy for the Thai population. However, it is important to thoroughly assess the economic aspects of genotyping costs and alternative drug costs, as well as other factors like bleeding risk and patient adherence. These considerations are necessary to determine the suitability of these drugs for wider empirical use. Earlier TagSNP methods reported *1 , *2 , *3 , and *17 alleles for CYP2C19 , but in this study, new alleles, such as *35 , *38 , *5 , *16 , and *34 ( 7C>T (P3S) have been reported, no *17 allele was called in this study. The phenotype prevalence of CYP3A5 is similar to earlier studies reported in Thai and Asian populations. , , CYP3A5 variations predict tacrolimus dose requirements, as individuals with the CYP3A5*1 allele require a higher daily dose to maintain the desired target trough level than CYP3A5*3/*3 (non‐expressers). , Literature highlights CYP3A5*1 , *2 , *3s role in breast cancer treatment kidney transplant outcomes and non‐small‐cell lung cancer prognosis highlighting its potential in tamoxifen toxicity minimization. The study found that almost 15% of the population is an IMs for the NUDT15 gene, a crucial pharmacogene in thiopurines response such as mercaptopurine and azathioprine. It is advisable to consider reduced starting doses to minimize toxicity, especially if the initial doses are high for NUDT15 (e.g., 75 mg/m 2 /day for mercaptopurine). , , Sertraline‐ CYP2B6 dosing recommendations for IMs may be adjusted for reduced metabolism (25%) and elevated concentration, but these are optional due to a lack of clinical outcome data. Also, CYP2B6 IMs may experience higher dose‐adjusted trough concentrations, potentially increasing the risk of adverse effects, suggesting a moderate recommendation for a reduced efavirenz dose. For UGT1A1 , 930 Thai people have normal function, and 12 carry increased functional genotypes. The study investigates the prevalence of UGT1A1 variants in Malaysia, revealing their association with reduced enzyme activity and increased irinotecan toxicity in Malays, Chinese, and Indians. A study from Japan found no significant correlation between the UGT1A1*27 polymorphism and severe irinotecan toxicity in lung cancer patients, indicating its inconsistency. A Thailand‐based pyrosequencing study found allele frequencies for UGT1A1 genetic polymorphisms as *1/*1 , *1/*6 , *1/*28 , *28/*6 , and *28/*28 , with no homozygous mutation of UGT1A1*6 . The combination of UGT1A1*28 and *6 polymorphisms emerged as a strong risk factor for severe hyperbilirubinemia in Thai neonates, suggesting a need for closer observation in neonates with these genetic variations to prevent more severe bilirubin levels. However, in this study, star allele analysis did not call for the *28 (Del) allele for this gene. Genes reported with high proportion of UMs The study identified 52.7% ultra‐rapid metabolizers in the study population, with 92.25% carrying alleles of *1 and *1F for CYP1A2 . Literature shows that heterozygous genotypes do not affect drug metabolism, especially caffeine metabolism, in non‐smoking individuals taking omeprazole. On the other hand, for CYP2B6 , 7.6% of the Thai study population carries UM alleles such as *1 (3.3%), *2 (0.5%), *4 (3.7%) with genotypes of *1/*4 , *2/*4 , and *4/*4 . However, literature shows that because of the small increase in metabolism in CYP2B6 UMs and RMs, no pre‐emptive dose increase is recommended.   IM / PMs In case CYP2C19 , the reported frequency of IM and PM is similar to earlier studies that have been reported in the Thai population. , Drugs like clopidogrel antidepressant , and proton pump inhibitors must be cautiously used in intermediate Thai metabolizers. It is appropriate to consider incorporating CYP2C19 genetic testing before starting clopidogrel treatment in Thailand, as well as discussing the possibility of using prasugrel and ticagrelor as empirical therapy for the Thai population. However, it is important to thoroughly assess the economic aspects of genotyping costs and alternative drug costs, as well as other factors like bleeding risk and patient adherence. These considerations are necessary to determine the suitability of these drugs for wider empirical use. Earlier TagSNP methods reported *1 , *2 , *3 , and *17 alleles for CYP2C19 , but in this study, new alleles, such as *35 , *38 , *5 , *16 , and *34 ( 7C>T (P3S) have been reported, no *17 allele was called in this study. The phenotype prevalence of CYP3A5 is similar to earlier studies reported in Thai and Asian populations. , , CYP3A5 variations predict tacrolimus dose requirements, as individuals with the CYP3A5*1 allele require a higher daily dose to maintain the desired target trough level than CYP3A5*3/*3 (non‐expressers). , Literature highlights CYP3A5*1 , *2 , *3s role in breast cancer treatment kidney transplant outcomes and non‐small‐cell lung cancer prognosis highlighting its potential in tamoxifen toxicity minimization. The study found that almost 15% of the population is an IMs for the NUDT15 gene, a crucial pharmacogene in thiopurines response such as mercaptopurine and azathioprine. It is advisable to consider reduced starting doses to minimize toxicity, especially if the initial doses are high for NUDT15 (e.g., 75 mg/m 2 /day for mercaptopurine). , , Sertraline‐ CYP2B6 dosing recommendations for IMs may be adjusted for reduced metabolism (25%) and elevated concentration, but these are optional due to a lack of clinical outcome data. Also, CYP2B6 IMs may experience higher dose‐adjusted trough concentrations, potentially increasing the risk of adverse effects, suggesting a moderate recommendation for a reduced efavirenz dose. For UGT1A1 , 930 Thai people have normal function, and 12 carry increased functional genotypes. The study investigates the prevalence of UGT1A1 variants in Malaysia, revealing their association with reduced enzyme activity and increased irinotecan toxicity in Malays, Chinese, and Indians. A study from Japan found no significant correlation between the UGT1A1*27 polymorphism and severe irinotecan toxicity in lung cancer patients, indicating its inconsistency. A Thailand‐based pyrosequencing study found allele frequencies for UGT1A1 genetic polymorphisms as *1/*1 , *1/*6 , *1/*28 , *28/*6 , and *28/*28 , with no homozygous mutation of UGT1A1*6 . The combination of UGT1A1*28 and *6 polymorphisms emerged as a strong risk factor for severe hyperbilirubinemia in Thai neonates, suggesting a need for closer observation in neonates with these genetic variations to prevent more severe bilirubin levels. However, in this study, star allele analysis did not call for the *28 (Del) allele for this gene.   UMs The study identified 52.7% ultra‐rapid metabolizers in the study population, with 92.25% carrying alleles of *1 and *1F for CYP1A2 . Literature shows that heterozygous genotypes do not affect drug metabolism, especially caffeine metabolism, in non‐smoking individuals taking omeprazole. On the other hand, for CYP2B6 , 7.6% of the Thai study population carries UM alleles such as *1 (3.3%), *2 (0.5%), *4 (3.7%) with genotypes of *1/*4 , *2/*4 , and *4/*4 . However, literature shows that because of the small increase in metabolism in CYP2B6 UMs and RMs, no pre‐emptive dose increase is recommended.   ABCB1 , also known as MDRP1 , is a member of the ABC transporter superfamily, with 72% of patients carrying normal function alleles and 15.5% carrying uncertain functional alleles in this study. In vivo assessments with the drug fexofenadine reveal that individuals with the MDR1*2 variant exhibit altered P‐glycoprotein activity, leading to differences in drug levels. SULT1A1 plays a key role in the bioactivation of procarcinogens and the sulfate conjugation of steroid hormones, catecholamines, and phenolic drugs. The frequency of alleles is similar to the already reported Thai study, which investigated the influence of SULT1A1*2 polymorphism on plasma efavirenz (EFV) concentration in Thai HIV‐1 patients. The study suggests that a combination of SULT1A1*2 and SULT1A1 copy number may influence EFV metabolism and drug response, requiring further investigation in a larger study population. The CPIC guideline advises cautious prescribing of HMG CoA reductase inhibitors for patients with certain genetic variants, such as SLCO1B1*5/*5 or *15 homozygotes on simvastatin. A study that explored the genotype frequencies of the S LCO1B1 and ABCG2 genes, including their predicted phenotypes, in 53 Thai participants, reported that 69.81% had normal function 13.21% had intermediate function ( *1A/*17 , *1B/*15 . and *1B/*17 ), and 7.55% had low function ( *5/*15 , *15/*15 , and *15/*17 ) and our current results are similar to thoseof earlier literature. Genetic variants of NAT2 are primarily associated with the metabolism, response, and toxicities of multiple drugs, especially anti‐infective agents. A study in Thailand found key NAT2 allele frequency distributions in the Thai population, including NAT2*4 (0.38), NAT2*5 (0.038), NAT2*6 (0.32), and NAT2*7 (0.204). Our study found new decreased functional alleles like *12 , *13 , and *19 , suggesting dose adjustment or alternative therapy is needed for slow acetylators. GSTP1 variants have been associated with the toxicity and outcome of several cancer drugs, especially platinum agents. In this study, *2 allele was reported to have a higher frequency (23.5%), hence PGx‐guided platinum chemotherapy can be administered in the Thai population. G6PD increases the risk of adverse drug reactions of many drugs, including primaquine, chloroquine, dapsone, metformin, and glibenclamide. In star real analysis, 12.31% of people have the genotype of decreased function, and this is similar to the already existing literature. According to the 2014 CPIC guidelines, the primary screening method is a quantitative enzyme assay, unless pre‐emptive genotyping confirms G6PD deficiency. As this study found a noticeable number of decreased functional alleles in the Thai population, rasburicase, nitrofurantoin, and primaquine should be administered with precaution. The findings of this study revealed a notable occurrence of the *1 (24.2%) and *10 (20%) alleles, along with a non‐identical gene duplication of *36 + *10 (28.2%) for CYP2D6 . These results align with previous research conducted among the Thai population. CPIC has recommended guidelines for testing CYP2D6 for various drugs, such as codeine and other opioids, antidepressants, and tamoxifen. Suzuki et al., (2021) found that specific CYP2D6 gene variant combinations, such as *1/*5 , *1/*10 , *2/*5 , *2/*10 , *5/*10 , and *10/*10 increase n‐desmethyltramadol levels in individuals with head and neck neoplasms. As the CYP2D6 activity score decreases, concentrations of N‐desmethyltramadol tend to increase. However, there was no significant association found between the CYP2D6 activity score and concentrations of tramadol or O‐desmethyltramadol (CPIC) guidelines. A Thailand study shows that revised CYP2D6 genotype to phenotype groupings improve the relationship between genotype and risperidone measures in autism patients, challenging substrate‐dependent phenotype classification.   Advanced DNA sequencing technologies and computational methods were used to analyze genetic variations in the Thai population, identifying 85,566 variants, including 464 known PGx variants and 275 clinically relevant novel variants in 63 VIPGx. The study reveals diverse metabolic profiles and functional alleles for 25 VIPGx genes, providing a comprehensive understanding of the frequency various alleles and their impact on drug responses specific to the Thai population. This lays the groundwork for new star allele nomenclature numbers for the new alleles identified in Thai population, supporting further research and simplifying clinical decision‐making for physicians. The limitation includes that this study involved 942 Thai common population participants, and not a population specifically recruited for the pharmacogenomics study. In addition, these data should be further validated with other computational and reference genotyping methods, novel alleles identified in this study should be orthogonally confirmed further. Likewise, in the case of TPMT *3C was extracted without phasing it through any statistical and experimental methods, that should be further validated in future research.   J.S. wrote the manuscript. C.S., W.C., J.S., and S.K. designed the research. J.S., S.K., P.O., J.E., T.S., D.D., P.S., P.V., N.T., W.T., and N.T., performed the research. J.S., S.K., and. P.O., analyzed the data.   This study was supported by grants from: (1) Mahidol University International Postdoctoral Fellowship, Mahidol University; (2) Faculty of Medicine, Ramathibodi Hospital, Mahidol University; (3) the Health System Research Institute under Genomics Thailand Strategic Fund; and (4) The International Research Network‐The Thailand Research Fund (IRN60W003), (5) Advanced Bioinformatics for Medical Genomics Program, Faculty of Pharmacy, Burapha University (6) National Research Council of Thailand (No. 485/2022), (7) Thailand Centre of Excellence for Life Sciences (Public organization), (8) Health Systems Research Institute of Thailand.   The authors declared no competing interests for this work.   Data S1   