Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229-3039, USA.
Pharmacogenomics J. 2010 Dec;10(6):465-77. doi: 10.1038/tpj.2010.71. Epub 2010 Oct 5.
There is great interest in characterizing the genetic architecture underlying drug response. For many drugs, gene-based dosing models explain a considerable amount of the overall variation in treatment outcome. As such, prescription drug labels are increasingly being modified to contain pharmacogenetic information. Genetic data must, however, be interpreted within the context of relevant clinical covariates. Even the most predictive models improve with the addition of data related to biogeographical ancestry. The current review explores analytical strategies that leverage population structure to more fully characterize genetic determinants of outcome in large clinical practice-based cohorts. The success of this approach will depend upon several key factors: (1) the availability of outcome data from groups of admixed individuals (that is, populations recombined over multiple generations), (2) a measurable difference in treatment outcome (that is, efficacy and toxicity end points), and (3) a measurable difference in allele frequency between the ancestral populations.
人们对阐明药物反应背后的遗传结构非常感兴趣。对于许多药物，基于基因的剂量模型可以解释治疗结果总体变化的很大一部分。因此，处方药标签越来越多地被修改，以包含药物遗传学信息。然而，遗传数据必须在相关临床协变量的背景下进行解释。即使是最具预测性的模型，也可以通过添加与生物地理起源相关的数据来改进。本综述探讨了利用群体结构来更全面地描述大型临床实践队列中治疗结果的遗传决定因素的分析策略。这种方法的成功将取决于几个关键因素：（1）可从混合个体群体（即经过多代重组的群体）获得结果数据；（2）治疗结果（即疗效和毒性终点）有可衡量的差异；（3）在祖先群体之间存在可衡量的等位基因频率差异。
