The Benefits of Using Genetic Information to Design Prevention Trials

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• 作者：Youna Hu¹ ; ² ; Li Li³ ; Margaret G. Ehm³ ; Nan Bing³ ; Kijoung Song⁴ ; Matthew R. Nelson³ ; Philippa J. Talmud⁵ ; Aroon D. Hingorani⁶ ; Meena Kumari⁶ ; Mika Kivimä ; ki⁶ ; Chun-Fang Xu⁷ ; Dawn M. Waterworth⁴ ; John C. Whittaker⁷ ; Gonç ; alo R. Abecasis¹ ; Cathie Spino¹ ; Hyun Min Kang¹ ; ^{hmkang@umich.edu}
• 刊名：The American Journal of Human Genetics
• 出版年：2013
• 出版时间：4 April, 2013
• 年：2013
• 卷：92
• 期：4
• 页码：547-557
• 全文大小：684 K

文摘

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¡ªin terms of trial size, duration, and cost¡ªof 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.