Genetics Journal Club Cara Skraban, MD Clinical Genetics Fellow February 12, 2015 JAMA Neurology Feb 2015 Myasthenia Gravis • Autoimmune disorder of neuromuscular transmission • Characterized by muscle fatigability • Typically mediated by antibodies against nicotinic acetylcholine receptors (AChRs) or against related proteins at NM junction – Muscle-specific tyrosine kinase (MuSK) – Lipoprotein receptor-related protein 4 – Agrin • Bimodal affected populations – Young women – Older men Myasthenia Gravis Genetics Factors of MG • HLA locus is the most strongly associated risk factor for disease • Previous GWAS studies – Major histocompatibility complex class II – Protein tyrosine phosphatase nonreceptor type 22 (PTPN22) – TNFAIP3 interacting protein 1 (TNIP1) • Gene studies have suggested association of cytotoxic Tlymphocyte-associated protein 4 gene (CTLA4) • Patients often have a family history of autoimmune disease • 5% of patients have a positive family history of MG following an AD inheritance Patients • Patients attending MG clinics at 14 centers throughout North America (972 patients) – – – – Diagnosed by a neurologist specializing in MG Onset of symptoms after 18 yo Non-Hispanic white race Diagnosed clinically and confirmed with anti-AChR antibodies – Samples collected using Oragene DNA Saliva Collection kits • Control Cohort (1977 patients) – Downloaded genotype data from dbGAP – Neurologically normal individuals – Matched for race and ethnic group, not age and sex Replication Cohort • 423 Italian patients with AChR-positive MG • 467 Italian neurologically normal controls • Matched to the case cohort for race/ethnic group but not for age or sex • Blood samples collected Patients Genome-wide Genotyping • Genotyped in the Laboratory of Neurogenetics, National Institute of Aging, using HumanOmniExpress BeadChips (Illumina) – Assay 730,525 SNPs across the genome • Control cohort previously genotyped at the Center for Inherited Disease Research at Hopkins on HumanOmni1-Quad BeadChips (Illumina) • Analyses were confined to the 677,673 autosomal SNPs that were common to both chips Genotyping Bias • To exclude possibility of genotyping bias arising from different sources of DNA, they compared from two patients: – whole genome genotyped data (Illumina) generated using paired DNA samples extracted from blood – DNA extracted from saliva using Oragene DNA Saliva Collection system – Concordance rate >99.99% – None of discordant SNPs were located within significantly associated loci • Exclude genotyping bias from using amplified DNA, they compared from 94 samples: – Sanger sequencing data generated using DNA samples that were amplified – Data generated using genomic, unamplified DNA – Concordance rate 100% for both rs601006 and rs9271850 Genotyping in the Replication Cohort • RS231770, rs4263037, rs9270986 – Taqman genotyping assays – Scanned on an ABI 7900HT Real-Time PCR • Rs601006 and rs9271850 – Sequencing using Big-Dye Terminator version 3.1 sequencing kit – Run on an ABI 3730xl DNA analyzer – Analyzed with Sequencher software and Mutation Surveyor Statistical Analysis: Genome-wide Association • Statistical analyses were performed using R statistical software • Standard quality-control procedures; Exclusion of the following: – SNP call rates of less than 95% – Non-European ancestry – Cryptic relatedness- identity-by-descent > 0.1 – Minor allele frequency <0.01 in the control cohort – Hardy-Weinberg equilibrium P < 0.001 in the control cohort Imputation • Markov chain-based Haplotyper to impute genotypes – Imputed by a two-stage design – Confirmed accuracy of imputation for most associated SNPs for the 972 MG patients • Taqman genotyping for rs231770 • Sanger sequencing for rs601006 and rs9271850 • High concordance for all: 99.8%, 98%, 100% • 8,114,394 SNPs available for analysis • 513,081 genotyped SNPs • 7,601,313 imputed SNPs Statistical Analysis Continued • P values calculated using logistic regression modeling – First two principle components used as covariates to compensate for any residual population stratification. – Principle components were generated using Genome-wide Complex trait analysis software package implementation of eigenstrat – Threshold of 5.0 x 10-8 for genome wide significance after Bonferroni correction Probability Analysis and Heritability Estimates • Density estimation was used to generate posterior probabilities of developing MG based on sex and age • Genome-wide Complex Trait Analysis – Used to compare each case series to control individuals (all cases, early-onset, late-onset) – Compared two separate sets of SNPs • All genotyped SNPs • Only those within 1 MB from the loci identified as genomewide significant in the discovery phase • Only SNPs passing quality control were used to evaluate the heritability Loci Showing Genome-Wide Association with Myasthenia Gravis Quartile-Quartile Plot All cases Bimodal Sex Distribution Early and Late Onset Cases Replication Cohort • 3 SNPs from the risk loci identified in the overall cohort for genotyping in the replication cohort of 423 Italian AChR antibody-positive MG cases and 467 controls. • Strongest signals – rs9270986 in the intergenic region between HLADRB1 and HLA-DQA1 – rs231770 located 3.3 kb upstream of CTLA4 Combined Analysis Early-onset Replication Cohort Late-onset Replication Cohort Summary of Results • Overall case-control cohort – CTLA4 (rs231770) – HLA-DQA1 (rs9271871) – TNFRSF11A (rs4263037) • Replicated for CTLA4 and HLA-DQA1 in the Italian cohort Summary of Results • Early and late-onset disease have distinct, but overlapping, genetic architecture – Genetic variation within TNFSRF11A locus drives susceptibility to disease among older cases – Different haplotypes across the same HLA region on chromosome 6 were identified in early and late-onset cases – CTLA4 exerts significant effect regardless of age at symptom onset, suggesting it plays a central role in generating the aberrant autoimmune response that leads to neuromuscular junction dysfunction HLA-DQA1 CTLA4 TNFSF11A • 4.5-kDa receptor activator of nuclear factor-K B expressed on the surface of antigenpresenting dendritic cells. • Important regulator of the interaction between T cells and dendritic cells that is essential for immune surveillance and regulation of specific immunity Study Limitations • • • • Sample size Possible population stratification Lack of age and sex match controls AChR antibody positive only patients (85% MG) • Different results from previous studies – Different signal in MHC region – No association of PTPN22 and TNIP1