Sing C F, Zerba K E, Reilly S L
Department of Human Genetics, University of Michigan Medical School, Ann Arbor 48109-0618.
Clin Genet. 1994 Jul;46(1 Spec No):6-14. doi: 10.1111/j.1399-0004.1994.tb04196.x.
An emerging challenge facing those who are concerned about the efficacy of public health programs is to understand how information from the DNA revolution might be used to improve our ability to predict the initiation, progression and severity of a common disease having a complex multifactorial etiology. In the course of research to evaluate the role of information about DNA, combinations of genome types and environmental exposures that predispose to disease will be identified. Such information is expected to be useful in efforts to identify individuals and families at higher risk of disease and to predict their responses to a proposed therapy. This paper begins with a discussion of the features of a realistic biological model for the study of a common multifactorial disease. We present evidence for the complexity in the relationship between genome type variation and variation in risk of coronary artery disease (CAD) and review the preliminary results of our studies to determine whether information about genome type variation can improve our ability to predict the distribution of CAD among individuals in the population at large. Such studies make it apparent that new analytical strategies are necessary to deal with the plethora of genome type information available for the evaluation of risk of a common disease like CAD. This shift in the research paradigm will build upon new strategies to understand the organization of natural systems that are coming from outside the mainstream of genetic research.
那些关注公共卫生项目成效的人面临着一个新挑战，即要弄清楚如何利用DNA革命带来的信息，提高我们预测一种病因复杂且具有多因素的常见疾病的发病、进展和严重程度的能力。在评估DNA信息作用的研究过程中，将确定那些易引发疾病的基因组类型与环境暴露的组合。预计此类信息将有助于识别疾病风险较高的个体和家庭，并预测他们对拟议治疗的反应。本文首先讨论用于研究常见多因素疾病的现实生物学模型的特征。我们展示了基因组类型变异与冠状动脉疾病（CAD）风险变异之间关系复杂性的证据，并回顾了我们的研究初步结果，以确定基因组类型变异信息是否能提高我们预测CAD在普通人群中个体间分布情况的能力。此类研究表明，显然需要新的分析策略来处理大量可用于评估像CAD这样常见疾病风险的基因组类型信息。这种研究范式的转变将基于从遗传研究主流之外产生的理解自然系统组织的新策略。
