Mustavich Laura F, Miller Perry, Kidd Kenneth K, Zhao Hongyu
Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520-8034, USA.
Hum Hered. 2010;70(3):177-93. doi: 10.1159/000317056. Epub 2010 Aug 12.
BACKGROUND/AIMS: Population-based studies have successfully identified genes affecting common diseases, but have not provided a molecular mechanism. We describe an approach for alcohol dependence connecting a mechanistic model at the molecular level with disease risk at the population level, and investigate how this model implies statistical gene-gene interactions that affect disease risk.
We develop a pharmacokinetic model describing how genetic variations in ADH1B, ADH1C, ADH7, ALDH2, and TAS2R38 affect consumption behavior, and alcohol and acetaldehyde levels over time in various tissues of individuals with a particular genotype to predict their susceptibility to alcohol dependence.
We show that there is good agreement between the observed genotype/haplotype frequencies and those predicted by the model among cases and controls. Based on this framework, we show that we expect to observe statistical interactions among these genes for a reasonably large sample size when logistic regression models are used to relate genotype effects and disease risk.
Our model exemplifies mechanistic modeling of how genes interact to influence an individual's susceptibility to alcohol dependence. We anticipate that this general approach could also be applied to study other diseases at the molecular level.
背景/目的：基于人群的研究已成功鉴定出影响常见疾病的基因，但尚未提供分子机制。我们描述了一种将分子水平的机制模型与人群水平的疾病风险相联系的酒精依赖研究方法，并研究该模型如何暗示影响疾病风险的统计基因-基因相互作用。
我们开发了一个药代动力学模型，描述乙醇脱氢酶1B（ADH1B）、乙醇脱氢酶1C（ADH1C）、乙醇脱氢酶7（ADH7）、乙醛脱氢酶2（ALDH2）和味觉感受器2型成员38（TAS2R38）的基因变异如何影响特定基因型个体不同组织中随时间变化的饮酒行为、酒精和乙醛水平，以预测其对酒精依赖的易感性。
我们表明，病例组和对照组中观察到的基因型/单倍型频率与模型预测的频率之间具有良好的一致性。基于此框架，我们表明，当使用逻辑回归模型关联基因型效应和疾病风险时，对于合理大的样本量，我们预期会观察到这些基因之间的统计相互作用。
我们的模型例证了基因如何相互作用以影响个体对酒精依赖易感性的机制建模。我们预计这种通用方法也可应用于在分子水平研究其他疾病。
