Multilocus analysis of hormonal, neurotransmitter, inflammatory pathways and genome-wide associated variants in migraine susceptibility

Authors

  • J. Ghosh,

    1. Department of Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, India
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  • S. Pradhan,

    1. Department of Neurology, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, India
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  • B. Mittal

    Corresponding author
    1. Department of Genetics, Sanjay Gandhi Postgraduate Institute of Medical Sciences (SGPGIMS), Lucknow, UP, India
    • Correspondence: B. Mittal, Department of Genetics, SGPGIMS, Raebareli Road, Lucknow 226014, UP, India (tel.: +91 522 2494322; fax: +91 522 266897; e-mail: bml_pgi@yahoo.com).

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Abstract

Background and purpose

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.

Methods

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).

Results

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.

Conclusions

The present study suggests interactions amongst hormonal, inflammatory and genome-wide associated variants but not with neurotransmitter pathway variants in migraine susceptibility.

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