BackgroundPanic disorder (PD) is one of the most common anxiety disorders with a lifetime prevalence of about 4%. The disorder is characterized by recurrent episodes of abrupt intense fear accompanied by additional physiological or cognitive symptoms. Although PD shows moderate heritability estimates of 30-54%, the specific genetic variants contributing to PD are largely unknown, with few and somewhat inconsistent loci reported to date. To address the challenge of underpowered individual studies, we conducted the largest genome-wide association study of PD to date comprising more than 8,700 individuals. MethodsIn our study we generated genome-wide SNP data from > 2,000 clinically well-characterized patients with PD and > 6,700 ethnically matched controls. The samples originate from five different European countries (Denmark, Estonia, Finland, Germany and Sweden). Standard GWAS quality control procedures were performed on each dataset individually. Imputation was performed using the 1000 Genomes Project reference panel. Only SNPs present in all datasets were kept for the meta-analysis using METASOFT. ResultsIn the single-marker analysis we identified several SNPs with PD association on the significance level of p < 10-05. Furthermore, using the recently published LD Score regression method on our GWAS data we estimated for the first time the PD heritability. In addition, we examined all previously reported genome-wide significant SNPs for bipolar disorder and schizophrenia in our PD GWAS results. Thus, we can estimate the degree of shared genetic risk factors between PD and bipolar disorder or schizophrenia. Finally, we performed pathway analyses using SNP sets with different PD association thresholds. DiscussionIn this collaborative study with sample sizes being larger than any other PD GWAS published to date, we identified PD associated loci across different European samples. LD Score regression analysis of our GWAS data provides robust evidence for a polygenic contribution of common SNPs to PD heritability. Replication of our top findings as well as further pathway and eQTL analyses are currently underway and will be presented.