A comprehensive Thai pharmacogenomics database (TPGxD-1): Phenotype prediction and variants identification in 942 whole-genome sequencing data

dc.contributor.authorJohn S.
dc.contributor.authorKlumsathian S.
dc.contributor.authorOwn-eium P.
dc.contributor.authorEu-ahsunthornwattana J.
dc.contributor.authorSura T.
dc.contributor.authorDejsuphong D.
dc.contributor.authorSritara P.
dc.contributor.authorVathesatogkit P.
dc.contributor.authorThongchompoo N.
dc.contributor.authorThabthimthong W.
dc.contributor.authorTeerakulkittipong N.
dc.contributor.authorChantratita W.
dc.contributor.authorSukasem C.
dc.contributor.correspondenceJohn S.
dc.contributor.otherMahidol University
dc.date.accessioned2024-06-16T18:12:01Z
dc.date.available2024-06-16T18:12:01Z
dc.date.issued2024-06-01
dc.description.abstractComputational methods analyze genomic data to identify genetic variants linked to drug responses, thereby guiding personalized medicine. This study analyzed 942 whole-genome sequences from the Electricity Generating Authority of Thailand (EGAT) cohort to establish a population-specific pharmacogenomic database (TPGxD-1) in the Thai population. Sentieon (version 201808.08) implemented the GATK best workflow practice for variant calling. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0 and employed Stargazer v2.0.2 for star allele analysis. The analysis of 63 very important pharmacogenes (VIPGx) reveals 85,566 variants, including 13,532 novel discoveries. Notably, we identified 464 known PGx variants and 275 clinically relevant novel variants. The phenotypic prediction of 15 VIPGx demonstrated a varied metabolic profile for the Thai population. Genes like CYP2C9 (9%), CYP3A5 (45.2%), CYP2B6 (9.4%), NUDT15 (15%), CYP2D6 (47%) and CYP2C19 (43%) showed a high number of intermediate metabolizers; CYP3A5 (41%), and CYP2C19 (9.9%) showed more poor metabolizers. CYP1A2 (52.7%) and CYP2B6 (7.6%) were found to have a higher number of ultra-metabolizers. The functional prediction of the remaining 10 VIPGx genes reveals a high frequency of decreased functional alleles in SULT1A1 (12%), NAT2 (84%), and G6PD (12%). SLCO1B1 reports 20% poor functional alleles, while PTGIS (42%), SLCO1B1 (4%), and TPMT (5.96%) showed increased functional alleles. This study discovered new variants and alleles in the 63 VIPGx genes among the Thai population, offering insights into advancing clinical pharmacogenomics (PGx). However, further validation is needed using other computational and genotyping methods.
dc.identifier.citationClinical and Translational Science Vol.17 No.6 (2024)
dc.identifier.doi10.1111/cts.13830
dc.identifier.eissn17528062
dc.identifier.issn17528054
dc.identifier.pmid38853370
dc.identifier.scopus2-s2.0-85195555052
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/98814
dc.rights.holderSCOPUS
dc.subjectPharmacology, Toxicology and Pharmaceutics
dc.subjectNeuroscience
dc.subjectBiochemistry, Genetics and Molecular Biology
dc.titleA comprehensive Thai pharmacogenomics database (TPGxD-1): Phenotype prediction and variants identification in 942 whole-genome sequencing data
dc.typeArticle
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195555052&origin=inward
oaire.citation.issue6
oaire.citation.titleClinical and Translational Science
oaire.citation.volume17
oairecerif.author.affiliationRamathibodi Hospital
oairecerif.author.affiliationUniversity of Liverpool
oairecerif.author.affiliationBumrungrad International Hospital
oairecerif.author.affiliationFaculty of Medicine Ramathibodi Hospital, Mahidol University
oairecerif.author.affiliationBurapha University

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