Applying Multiplex Assays to Understand Variation in Pharmacogenes.
Applying Multiplex Assays to Understand Variation in Pharmacogenes.
复制标题
应用多重测定来了解药基因的变异。
DOI：
10.1002/cpt.1468
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发表时间：
2019
影响因子：
6.7
通讯作者：
Fowler,DouglasM
中科院分区：
文献类型：
--
作者：
Chiasson,Melissa;Dunham,MaitreyaJ;Rettie,AllanE;Fowler,DouglasM
基因组测序使得能够检测到前所未有数量的新的药效基因变异。但是，解释这些变异如何影响药效基因生物学和最终的药物反应是困难的。变异效应的多重检测（MAVE）利用高通量DNA测序来同时评估数千种变异的功能后果。我们讨论了大规模的功能数据的效用在pharmacogene变异的解释，并建议实施MAVE可以使药物遗传学和改善patient care.Genomes现在可以轻松测序，但了解其中发现的变异的影响构成了一个重大挑战。每一个未解释的变异都代表着错过了改善患者结局的机会。例如，临床药物遗传学实施联盟（CPIC）列出了358个基因-药物对，其中变异可以改变药物反应。对于这358对中的63对，CPIC已经发布了关于可能改善患者护理的临床干预的指南。这些指南重点关注常见变异（次要等位基因频率，MAF，通常> 5%），其临床后果最清楚地记录在案。然而，了解罕见变异（MAF< 0.5-1%）的影响也是至关重要的，这一目标还远未实现。
Genome sequencing has enabled the detection of unprecedented numbers of new pharmacogene variants. But, interpreting how these variants affect pharmacogene biology and ultimately drug response is difficult. Multiplexed assays for variant effects (MAVEs) leverage high throughput DNA sequencing to assess the functional consequences of thousands of variants simultaneously. We discuss the utility of large-scale functional data in pharmacogene variant interpretation and suggest that implementing MAVEs could empower pharmacogenetics and improve patient care.Genomes can now be sequenced with ease, but understanding the effect of the variants found therein poses a major challenge. Each uninterpreted variant represents a missed opportunity to improve patient outcomes. For example, the Clinical Pharmacogenetics Implementation Consortium (CPIC) lists 358 gene-drug pairs where variation can change drug response. For 63 of these 358 pairs, CPIC has issued guidelines regarding clinical interventions that may improve patient care. These guidelines focus on common variants (minor allele frequencies, MAF, typically> 5%) whose clinical consequences are most clearly documented. However, understanding the effects of rare variants (MAF< 0.5–1%) is also essential, and this goal is far from realized.