Background Myasthenia Gravis (MG) is a rare autoimmune disorder affecting the neuromuscular junction. Here, we investigate the genetic architecture of MG performing a genomewide association study (GWAS) of the largest MG dataset analyzed to date. Methods We integrated GWAS from three different datasets (1,401 cases, 3,508 controls) and performed MG GWAS and onset-specific analyses. We also carried out HLA fine-mapping, gene-based, gene ontology and tissue enrichment analyses and investigated genetic correlation to other autoimmune disorders. Findings We observed the strongest MG association to TNFRSF11A (rs4369774, p=1.09x10-13; OR=1.4). Gene-based analysis revealed AGRN as a novel MG susceptibility gene. HLA fine-mapping pointed to two independent loci significantly associated with MG: HLA-DRB1 (with a protective role) and HLA-B. MG onset-specific analysis, reveals differences in the genetic architecture of Early-Onset vs Late-Onset MG. Furthermore, we find MG to be genetically correlated with Type 1 Diabetes, Rheumatoid Arthritis and late-onset Vitiligo. Interpretation Overall, our results are consistent with previous studies highlighting the role of the HLA and TNFRSF11A in MG etiology and different risk genes in EOMG vs LOMG. Furthermore, our gene-based analysis implicates, for the first time, AGRN as a MG susceptibility locus. AGRN encodes agrin, which is involved in neuromuscular junction formation. Mutations in AGRN have been found to underlie congenital myasthenic syndrome. Gene ontology analysis suggests an intriguing role for symbiotic processes in MG etiology. We also uncover genetic correlation of MG to Type 1 Diabetes, Rheumatoid Arthritis and late-onset Vitiligo, pointing to shared underlying genetic mechanisms. Funding This work was supported by NSF award #1715202, the European Social Fund and Greek funds through the National Strategic Reference Framework (NSRF) THALES Programme 2012-2015 and the NSRF ARISTEIA II Programme 2007-2013 to PP, and grants from the Association Francaise contre les Myopathies (AFM, Grant No. 80077) to ST.

BackgroundMyasthenia gravis (MG) is a rare autoimmune disorder affecting the neuromuscular junction (NMJ). Here, we investigate the genetic architecture of MG via a genome-wide association study (GWAS) of the largest MG data set analysed to date.MethodsWe performed GWAS meta-analysis integrating three different data sets (total of 1401 cases and 3508 controls). We carried out human leucocyte antigen (HLA) fine-mapping, gene-based and tissue enrichment analyses and investigated genetic correlation with 13 other autoimmune disorders as well as pleiotropy across MG and correlated disorders.ResultsWe confirmed the previously reported MG association with TNFRSF11A (rs4369774; p=1.09×10−13, OR=1.4). Furthermore, gene-based analysis revealed AGRN as a novel MG susceptibility gene. HLA fine-mapping pointed to two independent MG loci: HLA-DRB1 and HLA-B. MG onset-specific analysis reveals differences in the genetic architecture of early-onset MG (EOMG) versus late-onset MG (LOMG). Furthermore, we find MG to be genetically correlated with type 1 diabetes (T1D), rheumatoid arthritis (RA), late-onset vitiligo and autoimmune thyroid disease (ATD). Cross-disorder meta-analysis reveals multiple risk loci that appear pleiotropic across MG and correlated disorders.DiscussionOur gene-based analysis identifies AGRN as a novel MG susceptibility gene, implicating for the first time a locus encoding a protein (agrin) that is directly relevant to NMJ activation. Mutations in AGRN have been found to underlie congenital myasthenic syndrome. Our results are also consistent with previous studies highlighting the role of HLA and TNFRSF11A in MG aetiology and the different risk genes in EOMG versus LOMG. Finally, we uncover the genetic correlation of MG with T1D, RA, ATD and late-onset vitiligo, pointing to shared underlying genetic mechanisms.

Autoimmune diseases (ADs) are a group of more than 80 heterogeneous disorders that occur when there is a failure in the self-tolerance mechanisms triggering self-attacking autoantibodies. Most autoimmune disorders are polygenic and associated with genes in the human leukocyte antigen (HLA) region. However, additional non-HLA genes are also found to be associated with different ADs, and often these are also implicated in more than one disorder. Previous studies have observed associations between various health-related and lifestyle phenotypes and ADs. Polygenic risk scores (PRS) allow the calculation of an individual's genetic liability to a phenotype and are estimated as the sum of the risk alleles weighted by their effect sizes in a genome-wide association study (GWAS). Here, for the first time, we conducted a comparative PRS-PheWAS analysis for 11 different ADs (Celiac Disease, Juvenile Idiopathic Arthritis, Multiple Sclerosis, Myasthenia Gravis, Primary Sclerosing Cholangitis, Psoriasis, Rheumatoid Arthritis, Systemic Lupus Erythematosus, Type 1 Diabetes, Vitiligo Early Onset, Vitiligo Late Onset) and 3,281 outcomes available in the UK Biobank that cover a wide range of lifestyle, socio-demographic and health-related phenotypes. We also explored the genetic relationships of the studied ADs, estimating their genetic correlation and performing cross-disorder GWAS meta-analyses for the identified AD clusters. In total, we observed 554 outcomes significantly associated with at least one disorder PRS, and 300 outcomes were significant after variants in the HLA region were excluded from the PRS calculations. Based on the genetic correlation and genetic factor analysis, we observed five genetic factors among studied ADs. Cross-disorder meta-analyses in each factor revealed genome-wide significant loci that are pleiotropic across multiple ADs. Overall, our analyses confirm the association of different factors with genetic risk for ADs and reveal novel observations that warrant further exploration.

BackgroundComplex disorders are caused by a combination of genetic, environmental and lifestyle factors, and their prevalence can vary greatly across different populations. Genome wide association studies (GWAS) can help identify common variants that underlie disease risk. However, despite their increasing number, the vast majority of studies focuses on European populations, leading to questions regarding the transferability of findings to non-Europeans. Here, we investigated whether polygenic risk scores (PRS) based on European GWAS correlate to disease prevalence within Europe and around the world.ResultsGWAS summary statistics of 20 different disorders were used to estimate PRS in nine European and 24 worldwide reference populations. We estimated the correlation between average genetic risk for each of the 20 disorders and their prevalence in Europe and around the world. A clear variation in genetic risk was observed based on ancestry and we identified populations that have a higher genetic liability for developing certain disorders both within European and global regions. We also found significant correlations between worldwide disease prevalence and PRS for 13 of the studied disorders with obesity genetic risk having the highest correlation to disease prevalence. For these 13 disorders we also found that the loci used in PRS are significantly more conserved across the different populations compared to randomly selected SNPs as revealed by Fst and linkage disequilibrium structure.ConclusionOur results show that PRS of world populations calculated based on European GWAS data can significantly capture differences in disease risk and identify populations with the highest genetic liability to develop various conditions. Our findings point to the potential transferability of European-based GWAS results to non-European populations and provide further support for the validity of GWAS.