Abstract

Background

Migraine is a complex neurological disorder that is considered the most common disabling brain disorder affecting 14 % of people worldwide. The present study sought to infer potential causal relationships between self-reported migraine and other complex traits, using genetic data and a hypothesis-free approach.

Methods

We leveraged available summary statistics from genome-wide association studies (GWAS) of 1,504 phenotypes and self-reported migraine and inferred pair-wise causal relationships using the latent causal variable (LCV) method.

Results

We identify 18 potential causal relationships between self-reported migraine and other complex traits. Hypertension and blood clot formations were causally associated with an increased migraine risk, possibly through vasoconstriction and platelet clumping. We observed that sources of abdominal pain and discomfort might influence a higher risk for migraine. Moreover, occupational and environmental factors such as working with paints, thinner or glues, and being exposed to diesel exhaust were causally associated with higher migraine risk. Psychiatric-related phenotypes, including stressful life events, increased migraine risk. In contrast, ever feeling unenthusiastic / disinterested for a whole week, a phenotype related to the psychological well-being of individuals, was a potential outcome of migraine.

Conclusions

Overall, our results suggest a potential vascular component to migraine, highlighting the role of vasoconstriction and platelet clumping. Stressful life events and occupational variables potentially influence a higher migraine risk. Additionally, a migraine could impact the psychological well-being of individuals. Our findings provide novel testable hypotheses for future studies that may inform the design of new interventions to prevent or reduce migraine risk and recurrence.

Details

Title
Phenome-wide analysis highlights putative causal relationships between self-reported migraine and other complex traits
Author
García-Marín, Luis M 1   VIAFID ORCID Logo  ; Campos, Adrián I 1 ; Martin, Nicholas G 2 ; Cuéllar-Partida Gabriel 3 ; Rentería, Miguel E 1 

 QIMR Berghofer Medical Research Institute, Department of Genetics and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395); The University of Queensland, School of Biomedical Sciences, Faculty of Medicine, Brisbane, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537) 
 QIMR Berghofer Medical Research Institute, Department of Genetics and Computational Biology, Brisbane, Australia (GRID:grid.1049.c) (ISNI:0000 0001 2294 1395) 
 The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia (GRID:grid.1003.2) (ISNI:0000 0000 9320 7537); Present address: 23andMe, Inc, Sunnyvale, USA (GRID:grid.420283.f) (ISNI:0000 0004 0626 0858) 
Publication year
2021
Publication date
Dec 2021
Publisher
Springer Nature B.V.
ISSN
11292369
e-ISSN
11292377
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2549481921
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.