Ghosh J, Pradhan S, Mittal B
Department of Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, India.
Eur J Neurol. 2014 Jul;21(7):1011-20. doi: 10.1111/ene.12427. Epub 2014 Apr 2.
Migraine pathophysiology involves a complex interplay of processes wherein the hormonal, neurotransmitter and inflammatory pathways interact to influence the migraine phenotype. However, all studies pertaining to the role of genetic variants in migraine have been restricted to a specific pathway and none of the studies has looked into inter-pathway genetic analysis. Our aim was to combine all the genetic variants from our previously reported studies to conduct higher order gene-gene interaction analysis using different multi-analytical approaches.
The study group included 324 migraine patients and 134 healthy controls. The study included 20 polymorphisms from hormonal, neurotransmitter, inflammatory and genome-wide associated variants from our published reports. Univariate and multivariate analyses were carried out by logistic regression. Classification and regression tree (CART) analysis was performed to build a decision tree via recursive partitioning. The high order genetic interactions associated with migraine risk were analyzed using multifactor dimensionality reduction (MDR).
Univariate analysis revealed significant associations of polymorphisms in CYP19A1, ESR1, TNFA and PRDM16 genes with migraine susceptibility. Multiple regression analysis found significant results for four markers in CYP19A1, TNFA, ESR1 and LRP1 genes. In CART, the most prominent splitting variable was CYP19A1 polymorphism followed by TNFA, ESR1 and PRDM16 markers. The MDR analysis identified markers of CYP19A1, CYP19A1- TNFA, CYP19A1- ESR1- TNFA and CYP19A1- ESR1- TRPM8- PRDM16 as best models for one, two, three and four factors, respectively.
The present study suggests interactions amongst hormonal, inflammatory and genome-wide associated variants but not with neurotransmitter pathway variants in migraine susceptibility.
偏头痛的病理生理学涉及多种复杂的相互作用过程，其中激素、神经递质和炎症途径相互作用，影响偏头痛的表型。然而，所有关于基因变异在偏头痛中作用的研究都局限于特定途径，没有一项研究进行过跨途径基因分析。我们的目的是整合我们之前报道的研究中的所有基因变异，使用不同的多分析方法进行高阶基因-基因相互作用分析。
研究组包括324例偏头痛患者和134例健康对照。该研究纳入了来自我们已发表报告中的激素、神经递质、炎症和全基因组关联变异的20个多态性位点。通过逻辑回归进行单变量和多变量分析。进行分类与回归树（CART）分析，通过递归划分构建决策树。使用多因素降维法（MDR）分析与偏头痛风险相关的高阶基因相互作用。
单变量分析显示，CYP19A1、ESR1、TNFA和PRDM16基因的多态性与偏头痛易感性显著相关。多变量回归分析发现，CYP19A1、TNFA、ESR1和LRP1基因中的四个标记具有显著结果。在CART分析中，最显著的分裂变量是CYP19A1多态性，其次是TNFA、ESR1和PRDM16标记。MDR分析分别确定CYP19A1、CYP19A1-TNFA、CYP19A1-ESR1-TNFA和CYP19A1-ESR1-TRPM8-PRDM16标记为一、二、三、四个因素的最佳模型。
本研究提示，在偏头痛易感性中，激素、炎症和全基因组关联变异之间存在相互作用，但与神经递质途径变异无关。
