A Nonlinear Model for Gene-Based Gene-Environment Interaction.
A Nonlinear Model for Gene-Based Gene-Environment Interaction.
复制标题
基于基因的基因-环境相互作用的非线性模型
DOI：
10.3390/ijms17060882
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发表时间：
2016-06-04
影响因子：
5.6
通讯作者：
Cui Y
中科院分区：
文献类型：
--
作者：
Sa J;Liu X;He T;Liu G;Cui Y
大量文献证实了基因-环境（G×E）相互作用在人类复杂疾病病因学中的作用。传统的方法主要集中在分析单核苷酸多态性（SNP）和环境变量之间的相互作用。鉴于基因是功能单位，了解基因效应（而不是单个SNP效应）如何受到环境变量的影响以影响疾病风险至关重要。基于基因的关联分析的能力越来越强，在这项工作中，我们提出了一个稀疏的主成分回归（sPCR）模型来理解基于基因的G×E相互作用对复杂疾病的影响。我们首先提取基因中SNPs的稀疏主成分，然后用变系数（VC）模型对每个主成分的效应进行建模。该模型可以联合建模基因中的变异，其中它们的效应受到环境变量的非线性影响。此外，变系数sPCR（VC-sPCR）模型具有良好的解释性能，因为主成分载荷的稀疏性可以反映每个成分中相应SNPs的相对重要性。我们将我们的方法应用于泰国人口中的人类出生体重数据集。我们分析了22条染色体上的12，005个基因，并使用Bonferroni校正方法发现了一个显著的相互作用效应和一个暗示性相互作用。通过模拟研究进一步评估了模型性能。我们的模型提供了一种评估基于基因的G×E相互作用的系统方法。
A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.