The Benefits of Using Genetic Information to Design Prevention Trials
The Benefits of Using Genetic Information to Design Prevention Trials
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DOI：
10.1016/j.ajhg.2013.03.003
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发表时间：
2013-04-04
影响因子：
9.8
通讯作者：
Kang, Hyun Min
中科院分区：
文献类型：
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作者：
Hu, Youna;Li, Li;Kang, Hyun Min
预防性治疗的临床试验是一项复杂而昂贵的工作，重点是在短时间内可能发生疾病的个体，将他们随机分配到治疗组，并随时间推移进行随访。在这样的试验中，统计功效由每组中疾病事件的发生率决定，而成本则由随机化、治疗和随访决定。通过招募具有高疾病风险的个体来增加疾病事件发生率的策略可以显着减少研究规模，持续时间和成本。对常见、复杂疾病的全面研究已经产生了越来越多的强相关遗传标记。在这里，我们评估的效用-在试验规模，持续时间和成本-丰富的预防试验样本相结合的临床信息与遗传风险评分，以确定个人在更大的疾病风险。我们还描述了在这些试验中利用遗传风险评分并评估相关成本和时间节省的框架。以1型糖尿病（T1 D）、2型糖尿病（T2 D）、心肌梗死（MI）和晚期年龄相关性黄斑变性（AMD）为例，我们说明了使用遗传数据进行预防试验设计的潜力和局限性。我们举例说明了结合遗传信息可以大大降低试验成本或持续时间的设置，以及潜在的节省可以忽略不计的设置。结果在很大程度上取决于疾病的遗传结构，但我们也表明，随着每种疾病的强相关标志物列表的增加以及基因型个体的大样本变得可用，这些益处应该会增加。
Clinical trials for preventative therapies are complex and costly endeavors focused on individuals likely to develop disease in a short time frame, randomizing them to treatment groups, and following them over time. In such trials, statistical power is governed by the rate of disease events in each group and cost is determined by randomization, treatment, and follow-up. Strategies that increase the rate of disease events by enrolling individuals with high risk of disease can significantly reduce study size, duration, and cost. Comprehensive study of common, complex diseases has resulted in a growing list of robustly associated genetic markers. Here, we evaluate the utility-in terms of trial size, duration, and cost-of enriching prevention trial samples by combining clinical information with genetic risk scores to identify individuals at greater risk of disease. We also describe a framework for utilizing genetic risk scores in these trials and evaluating the associated cost and time savings. With type 1 diabetes (T1D), type 2 diabetes (T2D), myocardial infarction (MI), and advanced age-related macular degeneration (AMD) as examples, we illustrate the potential and limitations of using genetic data for prevention trial design. We illustrate settings where incorporating genetic information could reduce trial cost or duration considerably, as well as settings where potential savings are negligible. Results are strongly dependent on the genetic architecture of the disease, but we also show that these benefits should increase as the list of robustly associated markers for each disease grows and as large samples of genotyped individuals become available.