SNP by SNP by environment interaction network of alcoholism.
SNP by SNP by environment interaction network of alcoholism.
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DOI：
10.1186/s12918-017-0403-7
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发表时间：
2017-03-14
影响因子：
--
通讯作者：
Alterovitz G
中科院分区：
文献类型：
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作者：
Zollanvari A;Alterovitz G
酗酒具有很强的遗传因素。双胞胎研究表明，酗酒表型变异的很大一部分具有50% - 80%的遗传率。近十年来，对与这种复杂行为相关的基因变异的探索已成为基于序列研究的典范。然而，全基因组关联研究（GWAS）取得的成功有限，这可能是由复杂性状和行为的多基因本质所导致的，这表明需要能够定量捕捉大量基因变异之间的相互作用以及它们与非基因因素关联的新型多变量模型。在这方面，捕捉单核苷酸多态性（SNP）之间或SNP与环境相互作用的网络最近引起了极大的关注。
在此，我们对3776名个体进行了评估，以构建一个能够检测和量化酗酒的合理遗传和环境因素内部及之间相互作用的网络。在这方面，我们提议使用最大权重一阶依赖树作为一种潜在的统计学习技术，来描绘支撑这种复杂性状的依赖模式。通过基于预测的分析，我们进一步对基因、人口统计学因素、生物学通路以及我们的SNP - SNPE网络所代表的相互作用进行了排序。所提出的框架相当通用，有可能应用于其他复杂性状的研究。
本文的在线版本（doi:10.1186/s12918 - 2017 - 0403 - 7）包含补充材料，授权用户可获取。
Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50–80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest. Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP SNPE network. The proposed framework is quite general and can be potentially applied to the study of other complex traits. The online version of this article (doi:10.1186/s12918-017-0403-7) contains supplementary material, which is available to authorized users.