Nature CommunicationsGenetic & Genomic MedicinePeer-reviewed journal article
Clinical Impact of Pharmacogenetic Risk Variants in a Large Chinese Cohort
Chun-Yu WeiiD, Ming‐Shien WeniD, Chih-Kuang ChengiD, Yi‐Jing SheeniD, Tsung‐Chieh YaoiD, Sing-Lian LeeiD, Jer-Yuarn WuiD, Ming-Fang TsaiiD, Ling-Hui Li, Chun‐Houh CheniD, Cathy S.-J. FanniD, Hsin‐Chou YangiD, Yen‐Tsung HuangiD, Hung-Hsin CheniD, Yi-Min LiuiD, Erh‐Chan YehiD, Yu‐Ching PengiD, Shuu‐Jiun WangiD, Shih‐Pin CheniD, Ming‐Tsun TsaiiD, Teh‐Ia HuoiD, Chien‐Wei SuiD, Der‐Cherng TarngiD, Chin‐Chou HuangiD, Jong‐Ling FuhiD, , Chien‐Ming LiniD
Abstract
Incorporating pharmacogenetics into clinical practice promises to improve therapeutic outcome by choosing the medication and dosage optimized for a patient based on genetic factors that affect drug response1. One of the most promising benefits of PGx-guided therapy is the avoidance of adverse reactions2. To evaluate the clinical impact of PGx risk variants on adverse outcomes, we performed a retrospective study and analyzed the genetic and clinical data from the largest Han Chinese cohort assembled by the Taiwan Precision Medicine Initiative. We found that nearly all participants carried at least one genetic variant that could affect drug response, with many carrying multiple risk variants. Here we show that detailed analyses of four gene-drug pairs, for which sufficient data exist for statistical power, validate previous findings that PGx risk variants are significantly associated with drug-related adverse events or ineffectiveness. However, the excess risk of side effects or lack of efficacy is small compared to that found in those without the PGx risk variants, and most patients with PGx variants do not suffer from adverse events. Our results point to the need for identifying additional risk factors that cause adverse events in patients without PGx risk variants and factors that protect those with PGx risk variants from adverse events.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- research focus: differential expression
- research focus: variant effect prediction
- approach: statistical model
- Citations
- 15
- FWCI
- 14.47
- Field percentile
- top 1%
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