Christiane Graetz

Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls

Multiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits.

Class II HLA interactions modulate genetic risk for multiple sclerosis.

Association studies have greatly refined the understanding of how variation within the human leukocyte antigen (HLA) genes influences risk of multiple sclerosis. However, the extent to which major effects are modulated by interactions is poorly characterized. We analyzed high-density SNP data on 17,465 cases and 30,385 controls from 11 cohorts of European ancestry, in combination with imputation of classical HLA alleles, to build a high-resolution map of HLA genetic risk and assess the evidence for interactions involving classical HLA alleles. Among new and previously identified class II risk alleles (HLA-DRB1*15:01, HLA-DRB1*13:03, HLA-DRB1*03:01, HLA-DRB1*08:01 and HLA-DQB1*03:02) and class I protective alleles (HLA-A*02:01, HLA-B*44:02, HLA-B*38:01 and HLA-B*55:01), we find evidence for two interactions involving pairs of class II alleles: HLA-DQA1*01:01–HLA-DRB1*15:01 and HLA-DQB1*03:01–HLA-DQB1*03:02. We find no evidence for interactions between classical HLA alleles and non-HLA risk-associated variants and estimate a minimal effect of polygenic epistasis in modulating major risk alleles.

MANBA, CXCR5, SOX8, RPS6KB1 and ZBTB46 are genetic risk loci for multiple sclerosis

A recent genome-wide association study reported five loci for which there was strong, but sub-genome-wide significant evidence for association with multiple sclerosis risk. The aim of this study was to evaluate the role of these potential risk loci in a large and independent data set of ≈ 20,000 subjects. We tested five single nucleotide polymorphisms rs228614 (MANBA), rs630923 (CXCR5), rs2744148 (SOX8), rs180515 (RPS6KB1), and rs6062314 (ZBTB46) for association with multiple sclerosis risk in a total of 8499 cases with multiple sclerosis, 8765 unrelated control subjects and 958 trios of European descent. In addition, we assessed the overall evidence for association by combining these newly generated data with the results from the original genome-wide association study by meta-analysis. All five tested single nucleotide polymorphisms showed consistent and statistically significant evidence for association with multiple sclerosis in our validation data sets (rs228614: odds ratio = 0.91, P = 2.4 × 10(-6); rs630923: odds ratio = 0.89, P = 1.2 × 10(-4); rs2744148: odds ratio = 1.14, P = 1.8 × 10(-6); rs180515: odds ratio = 1.12, P = 5.2 × 10(-7); rs6062314: odds ratio = 0.90, P = 4.3 × 10(-3)). Combining our data with results from the previous genome-wide association study by meta-analysis, the evidence for association was strengthened further, surpassing the threshold for genome-wide significance (P < 5 × 10(-8)) in each case. Our study provides compelling evidence that these five loci are genuine multiple sclerosis susceptibility loci. These results may eventually lead to a better understanding of the underlying disease pathophysiology.

Genome-wide significant association of ANKRD55 rs6859219 and multiple sclerosis risk

Multiple sclerosis (MS) is a genetically complex disease that shares a substantial proportion of risk loci with other autoimmune diseases.1 Along these lines, ANKRD55 , originally implicated in rheumatoid arthritis, was recently reported as a potential novel MS risk gene (rs6859219, p=1.9×10−7).2 Here, we comprehensively validated this effect in independent datasets comprising 8846 newly genotyped subjects from Germany and France as well as 5003 subjects from two genome-wide association studies (GWAS). Upon meta-analysis of all available data (19 686 subjects), ANKRD55 rs6859219 now shows compelling evidence for association with MS at genome-wide significance (OR=1.19, p=3.1×10−11). Our study adds ANKRD55 to the list of established MS risk loci and extends previous evidence suggesting an overlapping genetic foundation across autoimmune diseases. Ankyrin repeats are abundant in a large number of different proteins in humans and mediate protein–protein interactions. DNA-sequence variants in ankyrin repeat domain-containing proteins have been linked to a wide range of diseases; for example, KRIT1 mutations causative for cerebral cavernous malformations,3 NOTCH3 mutations in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, and RFXANK mutations in the bare lymphocyte syndrome.4 ANKRD55 (located on chromosome 5q11.2) encodes the ‘ankyrin repeat domain-containing protein 55’ the function of which is currently unknown. Single nucleotide polymorphism rs6859219 in ANKRD55 was implicated in a recent GWAS meta-analysis on rheumatoid arthritis.5 Furthermore, a joint analysis of datasets on rheumatoid arthritis and coeliac disease also indicated a role of ANKRD55 in the latter.6 Given the augmenting evidence suggesting an overlap in the genetic architecture of autoimmune diseases including MS, we have previously investigated 10 ‘autoimmune loci’ in 2895 Spanish MS cases and 2942 controls.2 In that study, rs6859219 emerged as a putative new MS locus albeit at subgenome-wide significance (p=1.9×10−7).2 Our failure to establish …

Novel multiple sclerosis susceptibility loci implicated in epigenetic regulation.

Genome-wide study in Germans identifies four novel multiple sclerosis risk genes and confirms already known gene loci. We conducted a genome-wide association study (GWAS) on multiple sclerosis (MS) susceptibility in German cohorts with 4888 cases and 10,395 controls. In addition to associations within the major histocompatibility complex (MHC) region, 15 non-MHC loci reached genome-wide significance. Four of these loci are novel MS susceptibility loci. They map to the genes L3MBTL3, MAZ, ERG, and SHMT1. The lead variant at SHMT1 was replicated in an independent Sardinian cohort. Products of the genes L3MBTL3, MAZ, and ERG play important roles in immune cell regulation. SHMT1 encodes a serine hydroxymethyltransferase catalyzing the transfer of a carbon unit to the folate cycle. This reaction is required for regulation of methylation homeostasis, which is important for establishment and maintenance of epigenetic signatures. Our GWAS approach in a defined population with limited genetic substructure detected associations not found in larger, more heterogeneous cohorts, thus providing new clues regarding MS pathogenesis.

New candidates for CD4 T cell pathogenicity in experimental neuroinflammation and multiple sclerosis

Multiple sclerosis is a chronic autoimmune demyelinating disease of the central nervous system, which is thought to be triggered by environmental factors in genetically susceptible individuals leading to activation of autoreactive T lymphocytes. Large multi-centre genome-wide association studies have identified multiple genetic risk loci in multiple sclerosis. In this study, we investigated T cell transcriptomic changes in experimental autoimmune encephalomyelitis, an animal model for multiple sclerosis. We correlated these findings with the multiple sclerosis risk genes postulated by the most recent Immunochip analysis and found that multiple sclerosis susceptibility genes were significantly regulated in experimental autoimmune encephalomyelitis. Our data indicate that nine distinct genes associated with multiple sclerosis risk, Bach2, Il2ra, Irf8, Mertk, Odf3b, Plek, Rgs1, Slc30a7 and Thada, can be confirmed to be differentially regulated in pathogenic CD4(+) T cells. During the effector phase within the inflamed CNS, CD4(+) T cells undergo comprehensive transformation and we identified key transcription factors and signalling networks involved in this process. The transformation was linked to metabolic changes with the involvement of liver X receptor/retinoid X receptor signalling and cholesterol biosynthesis, which might control the T cell effector function in the central nervous system. Thus, our study confirms the involvement of multiple sclerosis risk genes in the pathophysiology of the animal model and sheds light on additional disease-relevant inflammatory networks.

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.

Power estimation for non-standardized multisite studies

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfers segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

Successful Replication of GWAS Hits for Multiple Sclerosis in 10,000 Germans Using the Exome Array.

Genome‐wide association studies (GWAS) successfully identified various chromosomal regions to be associated with multiple sclerosis (MS). The primary aim of this study was to replicate reported associations from GWAS using an exome array in a large German study. German MS cases (n = 4,476) and German controls (n = 5,714) were genotyped using the Illumina HumanExome v1‐Chip. Genotype calling was performed with the Illumina Genome StudioTM Genotyping Module, followed by zCall. Single‐nucleotide polymorphisms (SNPs) in seven regions outside the human leukocyte antigen (HLA) region showed genome‐wide significant associations with MS (P values < 5 × 10−8). These associations have been reported previously. In addition, SNPs in three previously reported regions outside the HLA region yielded P values < 10−5. The effect of nine SNPs in the HLA region remained (P < 10−5) after adjustment for other significant SNPs in the HLA region. All of these findings have been reported before or are driven by known risk loci. In summary, findings from previous GWAS for MS could be successfully replicated. We conclude that the regions identified in previous GWAS are also associated in the German population. This reassures the need for detailed investigations of the functional mechanisms underlying the replicated associations.

Investigation of sex-specific effects of apolipoprotein E on severity of EAE and MS

BackgroundDespite pleiotropic immunomodulatory effects of apolipoprotein E (apoE) in vitro, its effects on the clinical course of experimental autoimmune encephalomyelitis (EAE) and multiple sclerosis (MS) are still controversial. As sex hormones modify immunomodulatory apoE functions, they may explain contentious findings. This study aimed to investigate sex-specific effects of apoE on disease course of EAE and MS.MethodsMOG35-55 induced EAE in female and male apoE-deficient mice was assessed clinically and histopathologically. apoE expression was investigated by qPCR. The association of the MS severity score (MSSS) and APOE rs429358 and rs7412 was assessed across 3237 MS patients using linear regression analyses.ResultsEAE disease course was slightly attenuated in male apoE-deficient (apoE−/−) mice compared to wildtype mice (cumulative median score: apoE−/− = 2 [IQR 0.0–4.5]; wildtype = 4 [IQR 1.0–5.0]; n = 10 each group, p = 0.0002). In contrast, EAE was more severe in female apoE−/− mice compared to wildtype mice (cumulative median score: apoE−/− = 3 [IQR 2.0–4.5]; wildtype = 3 [IQR 0.0–4.0]; n = 10, p = 0.003). In wildtype animals, apoE expression during the chronic EAE phase was increased in both females and males (in comparison to naïve animals; p < 0.001). However, in MS, we did not observe a significant association between MSSS and rs429358 or rs7412, neither in the overall analyses nor upon stratification for sex.ConclusionsapoE exerts moderate sex-specific effects on EAE severity. However, the results in the apoE knock-out model are not comparable to effects of polymorphic variants in the human APOE gene, thus pinpointing the challenge of translating findings from the EAE model to the human disease.