系統化單倍型分析輔助選藥方法

Abstract

根據美國Food and Drug Administration (FDA) 的統計，每年用藥的不良反應案例逐漸增加，嚴重的話可能造成死亡，此外美國總統歐巴馬也在2015年時推廣precision medicine，這已經是一個未來趨勢。 而FDA所認證與藥物相關的基因裡部份是多態的，也就是有一個以上的等位基因，在傳統大多的單倍型型檢測都是建立於PCR的基礎上，這種方法一次只能分析特定幾種等位基因，無法全面地檢測。本系統主要利用次世代定序的高通量、高解析及低錯誤的優點，結合自行設計的引子 (primer) 以擴增基因中關鍵的區段，並使用新建立的單倍型分析演算法來進行預測。只需要經過一次抽血定序，就可以知道多種單倍型態，並提供足夠的資訊協助醫師正確地選擇用藥。 本研究的選藥系統，將可以大幅減少檢測的流程，達到快速且低成本，當病人都可以選擇正確的藥服用，除了可以降低風險，也可以節省社會成本，不造成醫療資源的浪費。

The number of reports associated with drug effects is increasing yearly, according to U.S. Food and Drug Administration. Since the worst effect of drug may result in death, it is so important that we can find out side effects or safely concerns for our drug. Based on the list of drug effect via FDA, most of drug-related genes are polymorphism. Thus, this study is to build a computational analysis for accurately identifying gene haplotype and SNP based on specific genetic types. It could not only provide more complete drug effects but also improve drug selection for doctors. All of the drug-related gene from FDA, the related SNP will detect by microarray, and we designed a set of primer for haplotype gene then run multiplex long-range PCR to amplify them. And sequencing by Illumina MiSeq sequencer, then analysis the sequence to identify the correct type, and suggest the drug selection. So, this method can comprehensively analyze type of drug-related gene and provide appropriate drug selection. It could not only provide more complete drug effects but also improve drug selection for doctors.