Potential Drug Targets for Migraine Identified Through Mendelian Randomization Analysis of the Human Plasma Proteome
Abstract
Migraine is a common chronic condition with a clear need for innovative treatment approaches. Protein quantity trait loci (pQTLs), which specifically regulate protein expression levels, are the closest and most stable QTLs to the phenotypes under study. In this context, we employ plasma proteomes data to conduct Mendelian randomization and colocalization analysis to identify potential drug targets for migraine. Plasma proteome data were sourced from the Decode, UKB-PPP, and Interval databases. We selected cis-SNPs associated with plasma protein levels at a genome-wide significance level (p 0.05) and colocalization analysis (PP.H4 > 0.7). External validation using an alternate migraine GWAS dataset (non-cancer illness code, self-reported: migraine ukb-b-16868) confirmed the findings for 5 proteins. Both HEIDI test and Bayesian colocalization suggested robust positive results for CALCB and TBC1D17. Specifically, CALCB is associated with an increased risk of migraine, whereas TBC1D17 is associated with a reduced risk. Our research provides strong evidence for two proteins and suggestive evidence for three additional proteins as potential targets for migraine treatment. Notably, CALCB is already under development as a therapeutic target for migraines, lending credibility to the promising drug development prospects for TBC1D17. Future studies are needed to confirm the causal role of TBC1D17 and explore the underlying mechanisms in migraine.
Read the paper
Related studies
- Calcium Ions in the Physiology and Pathology of the Central Nervous System
- AI-powered in silico twins: redefining precision medicine through simulation, personalization, and predictive healthcare
- In silico functional, structural and pathogenicity analysis of missense single nucleotide polymorphisms in human MCM6 gene
- In Silico drug repurposing pipeline using deep learning and structure based approaches in epilepsy
- Targeting serotonin receptors with phytochemicals – an in-silico study
- In silico exploration of anti-prostate cancer compounds from differential expressed genes
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.