Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway Ave., Baltimore, MD, 21205, USA.
Department of Psychology and Neuroscience, Baylor University, Waco, TX, USA.
Prev Sci. 2018 Jan;19(1):49-57. doi: 10.1007/s11121-016-0730-8.
The common paradigm for conceptualizing the influence of genetic and environmental factors on a particular disease relies on the concept of risk. Consequently, the bulk of etiologic, including genetic, work focuses on "risk" factors. These factors are aggregated at the high end of the distribution of liability to disease, the latent variable underlying the distribution of probability and severity of a disorder. However, liability has a symmetric but distinct aspect to risk, resistance to disorder. Resistance factors, aggregated at the low end of the liability distribution and supporting health and recovery, appear to be more promising for effective prevention and intervention. Herein, we discuss existing work on resistance factors, highlighting those with known genetic influences. We examine the utility of incorporating resistance genetics in prevention and intervention trials and compare the statistical power of a series of ascertainment schemes to develop a general framework for examining resistance outcomes in genetically informative designs. We find that an approach that samples individuals discordant on measured liability, a low-risk design, is the most feasible design and yields power equivalent to or higher than commonly used designs for detecting resistance genetic and environmental effects.
将遗传和环境因素对特定疾病影响的概念化通常依赖于风险的概念。因此,病因学的大部分工作,包括遗传工作,都集中在“风险”因素上。这些因素聚集在疾病易感性分布的高端,这是概率和严重程度分布的潜在变量。然而,易感性具有与风险对称但不同的方面,即对疾病的抵抗力。聚集在易感性分布低端并支持健康和恢复的抵抗因素,对于有效的预防和干预似乎更有希望。在此,我们讨论现有的抵抗因素研究,重点介绍那些具有已知遗传影响的因素。我们研究了在预防和干预试验中纳入抵抗遗传学的效用,并比较了一系列确定方案的统计效力,以建立一个通用框架,用于在遗传信息设计中检查抵抗结果。我们发现,一种对测量易感性不一致的个体进行抽样的方法,即低风险设计,是最可行的设计,其效力等同于或高于常用的检测抵抗遗传和环境效应的设计。