CLUSTER HEADACHE GENOME-WIDE ASSOCIATION STUDY AND META-ANALYSIS IDENTIFIES EIGHT LOCI AND IMPLICATES SMOKING AS CAUSAL RISK FACTOR 155 7 Reference 1. May A, Schwedt TJ, Magis D, et al. Cluster headache. Nat Rev Dis Primers. 2018;4:18006. 2. Headache Classification Committee of the International Headache Society (IHS). The International Classification of Headache Disorders, 3rd edition. Cephalalgia. 2018;38(1):1211. 3. Lund N,Petersen A,Snoer A,Jensen RH,Barloese M. Cluster headache is associated with unhealthy lifestyle and lifestyle-related comorbid diseases: Results from the Danish Cluster Headache Survey. Cephalalgia. 2019;39(2):254-263. 4. Wei DY, Goadsby PJ. Cluster headache pathophysiology - insights from current and emerging treatments. Nat Rev Neurol. 2021;17(5):308-324. 5. Sjaastad O, Shen JM, Stovner LJ, Elsas T. Cluster headache in identical twins. Headache. 1993;33(4):214-217. 6. Harder AVE, Winsvold BS, Noordam R, et al. Genetic Susceptibility Loci in Genomewide Association Study of Cluster Headache. Ann Neurol. 2021;90(2):203-216. 7. O’Connor E, Fourier C, Ran C, et al. Genome-Wide Association Study Identifies Risk Loci for Cluster Headache. Ann Neurol. 2021;90(2):193-202. 8. Chen SP, Hsu CL, Wang YF, et al. Genomewide analyses identify novel risk loci for cluster headache in Han Chinese residing in Taiwan. J Headache Pain. 2022;23(1):147. 9. Bacchelli E, Cainazzo MM, Cameli C, et al. A genome-wide analysis in cluster headache points to neprilysin and PACAP receptor gene variants. J Headache Pain. 2016;17(1):114. 10. Headache Classification Subcommittee of the International Headache S. The International Classification of Headache Disorders: 2nd edition. Cephalalgia : an international journal of headache. 2004;24 Suppl 1:9-160. 11. Winkler TW, Day FR, Croteau-Chonka DC, et al. Quality control and conduct of genomewide association meta-analyses. Nat Protoc. 2014;9(5):1192-1212. 12. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190-2191. 13. Mägi R, Horikoshi M, Sofer T, et al. Transethnic meta-regression of genome-wide association studies accounting for ancestry increases power for discovery and improves fine-mapping resolution. Hum Mol Genet. 2017;26(18):3639-3650. 14. Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236-1241. 15. Taylor KE, Ansel KM, Marson A, Criswell LA, Farh KK. PICS2: next-generation fine mapping via probabilistic identification of causal SNPs. Bioinformatics. 2021;37(18):3004-3007. 16. Benner C, Spencer CC, Havulinna AS, et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics. 2016;32(10):14931501. 17. Hautakangas H, Winsvold BS, Ruotsalainen SE, et al. Genome-wide analysis of 102,084 migraine cases identifies 123 risk loci and subtype-specific risk alleles. Nat Genet. 2022; 54(2):152-160 18. Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712-1721. 19. Gusev A,Ko A,Shi H,et al.Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016;48(3):245-252. 20. Mancuso N, Freund MK, Johnson R, et al. Probabilistic fine-mapping of transcriptomewide association studies. Nat Genet. 2019;51(4):675-682. 21. Freytag V, Vukojevic V, Wagner-Thelen H, et al. Genetic estimators of DNA methylation provide insights into the molecular basis of polygenic traits. Transl Psychiatry. 2018;8(1):31.
RkJQdWJsaXNoZXIy MTk4NDMw