(2018) Headache Classification Committee of the International Headache Society (IHS) The International Classification of Headache Disorders, 3rd edition. Cephalalgia 38(1):1–211. https://doi.org/10.1177/0333102417738202

GBD Neurology Collaborators (2019) Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 18(5):459–480. https://doi.org/10.1016/s1474-4422(18)30499-x

Article  Google Scholar 

Choquet H, Yin J, Jacobson AS, Horton BH, Hoffmann TJ, Jorgenson E et al (2021) New and sex-specific migraine susceptibility loci identified from a multiethnic genome-wide meta-analysis. Commun Biol 4(1):864. https://doi.org/10.1038/s42003-021-02356-y

Article  PubMed  PubMed Central  Google Scholar 

Tanaka M, Szabó Á, Körtési T, Szok D, Tajti J, Vécsei L (2023) From CGRP to PACAP, VIP, and beyond: unraveling the next chapters in migraine treatment. Cells 12(22). http://doi.org/10.3390/cells12222649.

Hautakangas H, Winsvold BS, Ruotsalainen SE, Bjornsdottir G, Harder AVE, Kogelman LJA et al (2022) Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet 54(2):152–160. https://doi.org/10.1038/s41588-021-00990-0

Article  CAS  PubMed  PubMed Central  Google Scholar 

Qi T, Song L, Guo Y, Chen C, Yang J (2024) From genetic associations to genes: methods, applications, and challenges. Trends Genet. https://doi.org/10.1016/j.tig.2024.04.008

Article  PubMed  Google Scholar 

Namba S, Konuma T, Wu KH, Zhou W, Okada Y (2022) A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis. Cell Genom 2(10):100190. https://doi.org/10.1016/j.xgen.2022.100190

Article  CAS  PubMed  PubMed Central  Google Scholar 

Burgess S, Timpson NJ, Ebrahim S, Davey Smith G (2015) Mendelian randomization: where are we now and where are we going? Int J Epidemiol 44(2):379–388. https://doi.org/10.1093/ije/dyv108

Article  PubMed  Google Scholar 

Storm CS, Kia DA, Almramhi MM, Bandres-Ciga S, Finan C, Hingorani AD et al (2021) Finding genetically-supported drug targets for Parkinson’s disease using Mendelian randomization of the druggable genome. Nat Commun 12(1):7342. https://doi.org/10.1038/s41467-021-26280-1

Article  CAS  PubMed  PubMed Central  Google Scholar 

Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ et al (2021) Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res 49(D1):D1144–d1151. https://doi.org/10.1093/nar/gkaa1084

Article  CAS  PubMed  Google Scholar 

Finan C, Gaulton A, Kruger F A, Lumbers R T, Shah T, Engmann J, et al. (2017) The druggable genome and support for target identification and validation in drug development. Sci Transl Med 9(383). http://doi.org/10.1126/scitranslmed.aag1166.

Su WM, Gu XJ, Dou M, Duan QQ, Jiang Z, Yin KF et al (2023) Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer’s disease. J Neurol Neurosurg Psychiatry 94(11):954–961. https://doi.org/10.1136/jnnp-2023-331142

Article  PubMed  Google Scholar 

Võsa U, Claringbould A, Westra HJ, Bonder MJ, Deelen P, Zeng B et al (2021) Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat Genet 53(9):1300–1310. https://doi.org/10.1038/s41588-021-00913-z

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang D, Liu S, Warrell J, Won H, Shi X, Navarro F C P, et al. (2018) Comprehensive functional genomic resource and integrative model for the human brain. Science 362(6420). http://doi.org/10.1126/science.aat8464.

Hemani G, Zheng J, Elsworth B, Wade K H, Haberland V, Baird D, et al. (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7. http://doi.org/10.7554/eLife.34408.

Consortium. G P, Auton A, Brooks L D, Durbin R M, Garrison E P, Kang H M, et al 2015 A global reference for human genetic variation. Nature 526(7571):68-74. http://doi.org/10.1038/nature15393

Staley JR, Blackshaw J, Kamat MA, Ellis S, Surendran P, Sun BB et al (2016) PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32(20):3207–3209. https://doi.org/10.1093/bioinformatics/btw373

Article  CAS  PubMed  PubMed Central  Google Scholar 

Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM et al (2019) Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 4:186. https://doi.org/10.12688/wellcomeopenres.15555.3

Article  PubMed  Google Scholar 

Greco MF, Minelli C, Sheehan NA, Thompson JR (2015) Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 34(21):2926–2940. https://doi.org/10.1002/sim.6522

Article  Google Scholar 

Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080

Article  PubMed  PubMed Central  Google Scholar 

Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C et al (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10(5):e1004383. https://doi.org/10.1371/journal.pgen.1004383

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16(5):284–287. https://doi.org/10.1089/omi.2011.0118

Article  CAS  PubMed  PubMed Central  Google Scholar 

Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R et al (2023) The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res 51(D1):D638–d646. https://doi.org/10.1093/nar/gkac1000

Article  CAS  PubMed  Google Scholar 

Yoo M, Shin J, Kim J, Ryall KA, Lee K, Lee S et al (2015) DSigDB: drug signatures database for gene set analysis. Bioinformatics 31(18):3069–3071. https://doi.org/10.1093/bioinformatics/btv313

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kim S, Chen J, Cheng T, Gindulyte A, He J, He S et al (2023) PubChem 2023 update. Nucleic Acids Res 51(D1):D1373–d1380. https://doi.org/10.1093/nar/gkac956

Article  PubMed  Google Scholar 

Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS et al (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zobdeh F, Ben Kraiem A, Attwood MM, Chubarev VN, Tarasov VV, Schiöth HB et al (2021) Pharmacological treatment of migraine: drug classes, mechanisms of action, clinical trials and new treatments. Br J Pharmacol 178(23):4588–4607. https://doi.org/10.1111/bph.15657

Article  CAS  PubMed  Google Scholar 

Pandit R, Chen L, Götz J (2020) The blood-brain barrier: physiology and strategies for drug delivery. Adv Drug Deliv Rev 165–166:1–14. https://doi.org/10.1016/j.addr.2019.11.009

Article  CAS  PubMed  Google Scholar 

Guo Y, Daghlas I, Gormley P, Giulianini F, Ridker PM, Mora S et al (2021) Phenotypic and Genotypic Associations Between Migraine and Lipoprotein Subfractions. Neurology 97(22):e2223–e2235. https://doi.org/10.1212/wnl.0000000000012919

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hong P, Han L, Wan Y (2024) Mendelian randomization study of lipid metabolism characteristics and migraine risk. Eur J Pain. https://doi.org/10.1002/ejp.2235

Article  PubMed  Google Scholar 

Buettner C, Nir RR, Bertisch SM, Bernstein C, Schain A, Mittleman MA et al (2015) Simvastatin and vitamin D for migraine prevention: A randomized, controlled trial. Ann Neurol 78(6):970–981. https://doi.org/10.1002/ana.24534

Article  CAS  PubMed  PubMed Central  Google Scholar