A testable prognostic model of nicotine dependence.
A testable prognostic model of nicotine dependence.
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DOI：
10.1080/01677060802572911
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发表时间：
2009
影响因子：
1.9
通讯作者：
Ramoni MF
中科院分区：
文献类型：
--
作者：
Ramoni RB;Saccone NL;Hatsukami DK;Bierut LJ;Ramoni MF
个人对尼古丁的依赖，主要是通过吸烟，是世界范围内发病率和死亡率的主要来源。许多吸烟者试图戒烟但失败了，这促使研究人员确定这种依赖的起源。由于已知尼古丁依赖表型的遗传性，相当大的兴趣一直集中在发现的遗传因素支持的性状。然而，这一目标并不容易实现：没有一个单一的因素可能解释任何大比例的依赖，因为尼古丁依赖被认为是一个复杂的特征（即，许多因素相互作用的结果）。全基因组关联研究是寻找复杂性状基因组碱基的有力工具，在这种情况下，通过单核苷酸多态性（SNP）关联分析已经确定了新的候选基因。然而，除了关联之外，遗传数据还可以用于生成尼古丁依赖的预测模型。正如在复杂性状的背景下所预期的那样，单个SNP无法准确预测尼古丁依赖，需要使用多变量模型。标准方法，如逻辑回归，无法考虑大量的SNP给定现有的样本量。然而，使用贝叶斯网络，可以克服这些限制，以生成多变量预测模型，其相对于单个SNP的拟合值具有显著增强的预测准确性。这种方法，结合全基因组关联研究产生的数据，有望为常见的复杂尼古丁依赖性状提供新的线索。
Individuals’ dependence on nicotine, primarily through cigarette smoking, is a major source of morbidity and mortality worldwide. Many smokers attempt but fail to quit smoking, motivating researchers to identify the origins of this dependence. Because of the known heritability of nicotine-dependence phenotypes, considerable interest has been focused on discovering the genetic factors underpinning the trait. This goal, however, is not easily attained: no single factor is likely to explain any great proportion of dependence because nicotine dependence is thought to be a complex trait (i.e., the result of many interacting factors). Genomewide association studies are powerful tools in the search for the genomic bases of complex traits, and in this context, novel candidate genes have been identified through single nucleotide polymorphism (SNP) association analyses. Beyond association, however, genetic data can be used to generate predictive models of nicotine dependence. As expected in the context of a complex trait, individual SNPs fail to accurately predict nicotine dependence, demanding the use of multivariate models. Standard approaches, such as logistic regression, are unable to consider large numbers of SNPs given existing sample sizes. However, using Bayesian networks, one can overcome these limitations to generate a multivariate predictive model, which has markedly enhanced predictive accuracy on fitted values relative to that of individual SNPs. This approach, combined with the data being generated by genomewide association studies, promises to shed new light on the common, complex trait nicotine dependence.
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影响因子：
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