Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm)

Runcharoen, Chakkaphan; Fukunaga, Koya; Sensorn, Insee; Iemwimangsa, Nareenart; Klumsathian, Sommon; Tong, Hang; Vo, Nam S; Le, Ly; Hlaing, Tin Maung; Thant, Myo; Zain, Sharifuddin M.; Mohamed, Zahurin; Pung, Yuh Fen; Capule, Francis; Nevado, Jose B.; Silao, Catherine Lynn T.; Al-Mahayri, Zeina N.; Ali, Bassam R.; Yuliwulandari, Rika; Prayuni, Kinasih; Zahroh, Hilyatuz; Noor, Dzul Azri Mohamed; Xangsayarath, Phonepadith; Xayavong, Dalouny; Kounnavong, Sengchanh; Sayasone, Somphou; Kordou, Zoe; Liopetas, Ioannis; Tsikrika, Athina; Tsermpini, Evangelia‐Eirini; Koromina, Maria; Mitropoulou, Christina; Patrinos, George P.; Kesornsit, Aumpika; Charoenyingwattana, Angkana; Wattanapokayakit, Sukanya; Mahasirimongkol, Surakameth; Mushiroda, Taisei; Chantratita, Wasun

doi:10.1038/s41439-021-00135-z

Cited by 22 publications

(15 citation statements)

References 29 publications

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“…The data presented was limited strictly to the geographical boundaries of Singapore and Indonesia, which could account for the difference in allele frequencies observed in comparison to PharmGKB, which is representative of a more expansive and global cohort 33 . Whilst dissimilar to database figures, our study demonstrated the distributions for the following to be concordant with previous studies 7,34,35 , suggesting a niche in the PGx landscape of Singapore and Indonesia -1. CYP2C9*3 allele as the more common variant within Indians than CYP2C9*2 7 2.…”

Section: Discussionsupporting

confidence: 79%

“…CYP2C9*3 allele as the more common variant within Indians than CYP2C9*2 7 2. A SLCO1B1 rs4149056 frequency of 12.5% amongst Indonesians 34 3. High frequencies of the CYP2D6*10 amongst Indonesians 34 4.…”

Section: Discussionmentioning

confidence: 99%

“…A SLCO1B1 rs4149056 frequency of 12.5% amongst Indonesians 34 3. High frequencies of the CYP2D6*10 amongst Indonesians 34 4. High frequencies of the CYP2D6*36 allele as seen in the Indonesian, Chinese and Malay ethnicities 35 5.…”

Section: Discussionmentioning

confidence: 99%

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Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

Kothary¹,

Mahendra²,

Tan³

et al. 2021

Preprint

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Background: With up to 70% of adverse drug reactions (ADRs) having high genetic associations, the clinical utility of pharmacogenomics (PGx) has been gaining traction. Nala PGx Core is a multi-gene qPCR-based panel that comprises 18 variants and 2 CYP2D6 Copy Number markers across 4 pharmacogenes - CYP2C9, CYP2C19, CYP2D6 and SLCO1B1. Objectives: In this study, we validated the performance of Nala PGx Core against benchmark methods, on the Singaporean and Indonesian populations. Additionally, we examined the allele and diplotype frequencies across 5 major ethnic groups present in these populations namely, Indonesians, Chinese, Malays, Indians and Caucasians. Methods: Human gDNA samples, extracted from the buccal swabs of 246 participants, were tested on Nala PGx Core and two chosen benchmarks, Agena VeriDose Core and CYP2D6 Copy Number Variation (CNV) Panel, and TaqMan DME Genotyping Assays. Performance was evaluated based on assay robustness, precision and accuracy at the genotype- and diplotype-level. Results: Nala PGx Core demonstrated high genotype- and diplotype-level call rates of >97% and >95% respectively in CYP2D6, and 100% for CYP2C9, CYP2C19 and SLCO1B1. A precision rate of 100% was observed on both intra- and inter-precision studies. Variant-level concordance to the benchmark methods was >96.9% across all assays, which consequently resulted in a diplotype-level concordance of >94.7% across CYP2C9, CYP2C19 and CYP2D6. Overall, the allele frequencies of CYP2D6*10 and CYP2D6*36 were higher in our cohort as compared to previous records. Notably, CYP2D6 copy number variation (CNV) analysis demonstrated a CYP2D6 *10/*36 frequency of 26.5% amongst the Indonesian cohort. Conclusion: Nala PGx Core produced robust and accurate genotyping when compared to other established benchmarks. Furthermore, the panel successfully characterized alleles of clinical relevance in the Singaporean and Indonesian populations such as CYP2D6*10 and CYP2D6*36, suggesting its potential for adoption in clinical workflows regionally.

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Section: Discussionsupporting

confidence: 79%

Section: Discussionmentioning

confidence: 99%

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Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

Kothary¹,

Mahendra²,

Tan³

et al. 2021

Preprint

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“…To further demonstrate the applicability of the machine learning model, we applied the classifier in data derived from NGS analyses. Τo this end, variant call format (.vcf) files comprised of the results from (i) a WGS analysis of a single individual of Greek origin diagnosed with coeliac disease, and (ii) a targeted pharmacogene sequencing analysis of 304 individuals of Greek origin diagnosed with psychiatric diseases [ 70 ]. Firstly, the provided variants were annotated, using the web interface of ensemble VEP tool, while the resulting data were preprocessed to select only these identified in the transcripts of interest.…”

Section: Methodsmentioning

confidence: 99%

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Pandi

Koromina

Tsafaridis³

et al. 2021

Hum Genomics

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Background The field of pharmacogenomics focuses on the way a person’s genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient’s genome, hence rendering its incorporation into clinical routine practice a realistic possibility. Methods This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. Results All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to “Decreased function,” 41 variants as “No” function proteins, and 1 variant in “Increased function.” Conclusion Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

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“…Integrating rare genetic variants into pre-emptive pharmacogenetic tests will allow genotyping to be highly effective for a true individualization of patients' drug treatment regimens (33). Besides understanding which drug would fully benefi t from a pharmacogene characterization at the sequencing level, population-specifi c patterns of rare variants should be known (34,35) and taken into consideration for developing population-adjusted genetic profi ling strategies (2,36,37). For instance, CPIC guidelines recommended TPMT and NUDT15 genotyping of common variants for thiopurine dosing.…”

Section: Role Of Rare Variants In Improving the Sensitivity Of Pharmacogenetic Testsmentioning

confidence: 99%

Cancer Pharmacogenetics: perspective on newly discovered and implemented predictive biomarkers

Roncato¹,

Cecchini²,

Fratte³

et al. 2021

Pharm Adv

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Implementation of pharmacogenetics (PGx) in the clinical practice of cancer therapy is one of the goals for a precision medicine. For some anticancer drugs (fluoropyrimidines and irinotecan) precise recommendations by regulatory agencies are available. However, in the future, implementation of PGx in clinical practice should also consider the anticancer drugs without available pharmacogenetic guidelines and the increasingly important role of rare PGx variants. To overcome barriers to PGx implementation we need to focus on pharmacogenetic test clinical utility by adopting a panel-based approach and sensitize both physician and patients to the therapeutic value of pharmacogenetic tests. Impact statementImplementation of Pharmacogenetics in Oncology is mandatory for precision medicine. Validated pharmacogenetic biomarkers require a pre-therapeutic screening, while the contribute of less validated and rare variants, of polytherapeutic and polygenic effect on patient's phenotype are becoming increasingly important.

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Prevalence of pharmacogenomic variants in 100 pharmacogenes among Southeast Asian populations under the collaboration of the Southeast Asian Pharmacogenomics Research Network (SEAPharm)

Cited by 22 publications

References 29 publications

Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

Validation of a multi-gene qPCR-based pharmacogenomics panel across major ethnic groups in Singapore and Indonesia

A novel machine learning-based approach for the computational functional assessment of pharmacogenomic variants

Cancer Pharmacogenetics: perspective on newly discovered and implemented predictive biomarkers

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