Division of Pharmacogenomics and Personalized Medicine, Department of Pathology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Laboratory for Pharmacogenomics, Somdech Phra Debaratana Medical Center (SDMC), Ramathibodi Hospital, Bangkok, Thailand.
Clin Transl Sci. 2024 Jun;17(6):e13830. doi: 10.1111/cts.13830.
Computational 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.
计算方法分析基因组数据，以识别与药物反应相关的遗传变异，从而指导个性化医疗。本研究分析了泰国发电局（EGAT）队列的 942 个全基因组序列，以建立泰国人群特有的群体药物基因组学数据库（TPGxD-1）。Sentieon（版本 201808.08）实施了 GATK 最佳工作流程实践用于变异调用。然后，我们使用 Golden Helix VarSeq 2.5.0 注释变体调用格式（VCF）文件，并使用 Stargazer v2.0.2 进行星等位基因分析。对 63 个非常重要的药物基因组学基因（VIPGx）的分析揭示了 85566 个变体，包括 13532 个新发现。值得注意的是，我们鉴定了 464 个已知的 PGx 变体和 275 个临床相关的新变体。对 15 个 VIPGx 的表型预测显示，泰国人群的代谢特征存在差异。CYP2C9（9%）、CYP3A5（45.2%）、CYP2B6（9.4%）、NUDT15（15%）、CYP2D6（47%）和 CYP2C19（43%）等基因显示出大量的中间代谢物；CYP3A5（41%）和 CYP2C19（9.9%）显示出更多的弱代谢物。CYP1A2（52.7%）和 CYP2B6（7.6%）发现有更多的超快代谢物。其余 10 个 VIPGx 基因的功能预测显示，SULT1A1（12%）、NAT2（84%）和 G6PD（12%）中降低功能等位基因的频率很高。SLCO1B1 报告 20%的功能不良等位基因，而 PTGIS（42%）、SLCO1B1（4%）和 TPMT（5.96%）显示出增加的功能等位基因。本研究在泰国人群中发现了 63 个 VIPGx 基因中的新变体和等位基因，为推进临床药物基因组学（PGx）提供了新的见解。然而，还需要使用其他计算和基因分型方法进行进一步验证。
