Genome-wide significant association with seven novel multiple sclerosis risk loci

Objective A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases.

[1]  M. Daly,et al.  Genetic and Epigenetic Fine-Mapping of Causal Autoimmune Disease Variants , 2014, Nature.

[2]  U. Lindenberger,et al.  Assessment of microRNA-related SNP effects in the 3′ untranslated region of the IL22RA2 risk locus in multiple sclerosis , 2014, neurogenetics.

[3]  J. Shendure,et al.  A general framework for estimating the relative pathogenicity of human genetic variants , 2014, Nature Genetics.

[4]  M. Pirinen,et al.  Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis , 2013, Nature Genetics.

[5]  M. Peters,et al.  Systematic identification of trans eQTLs as putative drivers of known disease associations , 2013, Nature Genetics.

[6]  M. Ban,et al.  MANBA, CXCR5, SOX8, RPS6KB1 and ZBTB46 are genetic risk loci for multiple sclerosis. , 2013, Brain : a journal of neurology.

[7]  J. Ioannidis,et al.  Meta-analysis methods for genome-wide association studies and beyond , 2013, Nature Reviews Genetics.

[8]  Y. Aulchenko,et al.  Association of SNPs of CD40 Gene with Multiple Sclerosis in Russians , 2013, PloS one.

[9]  Shu-Chen Li,et al.  Genome-wide significant association of ANKRD55 rs6859219 and multiple sclerosis risk , 2013, Journal of Medical Genetics.

[10]  J. Todd,et al.  Seven newly identified loci for autoimmune thyroid disease , 2012, Human molecular genetics.

[11]  M. Pirinen,et al.  Including known covariates can reduce power to detect genetic effects in case-control studies , 2012, Nature Genetics.

[12]  C. Gieger,et al.  Replication study of multiple sclerosis (MS) susceptibility alleles and correlation of DNA-variants with disease features in a cohort of Austrian MS patients , 2012, neurogenetics.

[13]  Chuong B. Do,et al.  Comprehensive Research Synopsis and Systematic Meta-Analyses in Parkinson's Disease Genetics: The PDGene Database , 2012, PLoS genetics.

[14]  Manolis Kellis,et al.  HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants , 2011, Nucleic Acids Res..

[15]  John P A Ioannidis,et al.  Comprehensive field synopsis and systematic meta-analyses of genetic association studies in cutaneous melanoma. , 2011, Journal of the National Cancer Institute.

[16]  Simon C. Potter,et al.  Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis , 2011, Nature.

[17]  Kasper Lage,et al.  Pervasive Sharing of Genetic Effects in Autoimmune Disease , 2011, PLoS genetics.

[18]  M. González-Gay,et al.  Correlation between endothelial function and carotid atherosclerosis in rheumatoid arthritis patients with long-standing disease , 2011, Arthritis research & therapy.

[19]  M. Brown,et al.  Promise and pitfalls of the Immunochip , 2011, Arthritis research & therapy.

[20]  Atul J. Butte,et al.  Autoimmune Disease Classification by Inverse Association with SNP Alleles , 2009, PLoS genetics.

[21]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[22]  G. Stewart,et al.  Association of common T cell activation gene polymorphisms with multiple sclerosis in Australian patients , 2004, Journal of Neuroimmunology.

[23]  Douglas G Altman,et al.  Interaction revisited: the difference between two estimates , 2003, BMJ : British Medical Journal.

[24]  A. Compston,et al.  Recommended diagnostic criteria for multiple sclerosis: Guidelines from the international panel on the diagnosis of multiple sclerosis , 2001, Annals of neurology.

[25]  D. Silberberg,et al.  New diagnostic criteria for multiple sclerosis: Guidelines for research protocols , 1983, Annals of neurology.

[26]  Linda,et al.  MANBA , CXCR 5 , SOX 8 , RPS 6 KB 1 and ZBTB 46 are genetic risk loci for multiple sclerosis , 2013 .

[27]  K. Mossman The Wellcome Trust Case Control Consortium, U.K. , 2008 .

[28]  Pak Chung Sham,et al.  Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits , 2003, Bioinform..