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Figure 1.
Pleiotropic Enrichment of Parkinson Disease (PD) Conditioned on Association P Values of Autoimmune Diseases
Pleiotropic Enrichment of Parkinson Disease (PD) Conditioned on Association P Values of Autoimmune Diseases

Conditional quantile-quantile plots (nominal vs empirical –log10P values) are calculated from single-nucleotide polymorphism (SNP) populations of varying degrees of association with autoimmune diseases. Each population is composed of SNPs that pass certain significance of association (type 1 diabetes [T1D], Crohn disease [CD], ulcerative colitis [UC], rheumatoid arthritis [RA], celiac disease, psoriasis, and multiple sclerosis [MS]) at P ≤ 1 (All SNPs), P < 10−1, P < 10−2, and P < 10−3. All P values have been corrected for genomic inflation. Dotted lines indicate the expected line under the null hypothesis, and leftward deflection shows increasing degrees of enrichment.

Figure 2.
Conjunctional False Discovery Rate Manhattan Plot of − log10 Values for the Associated Autoimmune Phenotypes
Conjunctional False Discovery Rate Manhattan Plot of − log10 Values for the Associated Autoimmune Phenotypes

All single-nucleotide polymorphisms without pruning are plotted: enlarged points represent significant single-nucleotide polymorphisms with conjunction false discovery rate less than 0.05, and small points represent the nonsignificant single-nucleotide polymorphisms. The most significant single-nucleotide polymorphism in each linkage disequilibrium block is marked with black circles and annotated with its closest gene, showing the localization of 17 common loci (some loci may have multiple genes) between Parkinson disease and autoimmune diseases listed in Table 1. CD indicates Crohn disease; MS, multiple sclerosis; RA, rheumatoid arthritis; T1D, type 1 diabetes; and UC, ulcerative colitis.

Figure 3.
Functional Gene Network for Novel Pleiotropic Loci From the Present Analysis and Previously Confirmed Parkinson Disease
Functional Gene Network for Novel Pleiotropic Loci From the Present Analysis and Previously Confirmed Parkinson Disease

The protein-coding genes closest to the most associated single-nucleotide polymorphism in the pleiotropic loci and the previously confirmed Parkinson disease loci were used to construct a functional similarity network of genes (see the Methods section). Nodes are colored to show association. The thickness of lines connecting nodes indicates the strength of the association between nodes.

Table 1.  
Seventeen Independent Pleiotropic Loci in Parkinson Disease (PD) and Autoimmune Diseases With Conjunction False Discovery Rate (FDR) Less Than 0.05a
Seventeen Independent Pleiotropic Loci in Parkinson Disease (PD) and Autoimmune Diseases With Conjunction False Discovery Rate (FDR) Less Than 0.05a
Table 2.  
Overview of Expression Quantitative Trait Loci (eQTL) and Methylation Effects Related to the Immune System
Overview of Expression Quantitative Trait Loci (eQTL) and Methylation Effects Related to the Immune System
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Original Investigation
July 2017

Genome-wide Pleiotropy Between Parkinson Disease and Autoimmune Diseases

Author Affiliations
  • 1Norwegian Centre for Mental Disorders Research (NORMENT), K. G. Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo
  • 2Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
  • 3Department of Clinical Genetics, Vrije Universiteit (VU) University Medical Center, Amsterdam, the Netherlands
  • 4German Center for Neurodegenerative Diseases (DZNE), Tübingen
  • 5Multimodal Imaging Laboratory, University of California at San Diego, La Jolla
  • 6Department of Radiology and Biomedical Imaging, University of California, San Francisco
  • 7Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland
  • 8Department of Psychiatry, University of California at San Diego, La Jolla
  • 9Department of Psychiatry, University of Copenhagen, Copenhagen, Denmark
  • 10Department of Medical Genetics, University of Oslo, Oslo, Norway
  • 11Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
  • 12Sciences Graduate Program, University of California at San Diego, La Jolla
  • 13Department of Neurosciences, University of California at San Diego, La Jolla
  • 14Institute of Clinical Molecular Biology, Christian Albrechts University of Kiel, Kiel, Germany
  • 15K. G. Jebsen Inflammation Research Centre, Research Institute of Internal Medicine, Oslo, Norway
  • 16Division of Cancer Medicine, Surgery and Transplantation, Oslo University Hospital Rikshospitalet, Oslo, Norway
  • 17Section of Clinical Immunology and Infectious Diseases, Oslo University Hospital Rikshospitalet, Oslo, Norway
  • 18Division of Gastroenterology, Institute of Medicine, University of Bergen, Bergen, Norway
  • 19Norwegian Primary Sclerosing Cholangitis (PSC) Research Center, Department of Transplantation Medicine, Oslo
  • 20Sorbonne Universités, Université Pierre-et-Marie Curie (UPMC) Paris 06, UM 1127, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
  • 21Institut National de la Santé et de la Récherche Médicale (INSERM), Unité 1127, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
  • 22Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
  • 23Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France
  • 24Assistance Publique–Hôpitaux de Paris, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France
  • 25Department of Clinical Neuroscience, National Hospital for Neurology and Neurosurgery (NHNN), University College London, London, England
  • 26Department of Molecular Neurosciences, Institute of Neurology, University College London, London, England
  • 27Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
  • 28Rita Lila Weston Institute, University College London, London, England
  • 29Department of Radiology, University of California at San Diego, La Jolla
  • 30Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Tübingen, Germany
JAMA Neurol. 2017;74(7):780-792. doi:10.1001/jamaneurol.2017.0469
Key Points

Question  Are there genome-wide genetic risk factors for Parkinson disease that are shared with pathways of autoimmune diseases?

Findings  In this study of combined genome-wide association data with control replication, we identified 17 novel genetic loci shared between Parkinson disease and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis.

Meaning  Our findings identify a common genetic pathway between Parkinson disease and autoimmune diseases and suggest that the immune system influences Parkinson disease pathogenesis.

Abstract

Importance  Recent genome-wide association studies (GWAS) and pathway analyses supported long-standing observations of an association between immune-mediated diseases and Parkinson disease (PD). The post-GWAS era provides an opportunity for cross-phenotype analyses between different complex phenotypes.

Objectives  To test the hypothesis that there are common genetic risk variants conveying risk of both PD and autoimmune diseases (ie, pleiotropy) and to identify new shared genetic variants and their pathways by applying a novel statistical framework in a genome-wide approach.

Design, Setting, and Participants  Using the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, rheumatoid arthritis, celiac disease, psoriasis, and multiple sclerosis. NeuroX data (6927 PD cases and 6108 controls) were used for replication. The study investigated the biological correlation between the top loci through protein-protein interaction and changes in the gene expression and methylation levels. The dates of the analysis were June 10, 2015, to March 4, 2017.

Main Outcomes and Measures  The primary outcome was a list of novel loci and their pathways involved in PD and autoimmune diseases.

Results  Genome-wide conjunctional analysis identified 17 novel loci at false discovery rate less than 0.05 with overlap between PD and autoimmune diseases, including known PD loci adjacent to GAK, HLA-DRB5, LRRK2, and MAPT for rheumatoid arthritis, ulcerative colitis and Crohn disease. Replication confirmed the involvement of HLA, LRRK2, MAPT, TRIM10, and SETD1A in PD. Among the novel genes discovered, WNT3, KANSL1, CRHR1, BOLA2, and GUCY1A3 are within a protein-protein interaction network with known PD genes. A subset of novel loci was significantly associated with changes in methylation or expression levels of adjacent genes.

Conclusions and Relevance  The study findings provide novel mechanistic insights into PD and autoimmune diseases and identify a common genetic pathway between these phenotypes. The results may have implications for future therapeutic trials involving anti-inflammatory agents.

Introduction

Emerging evidence suggests a substantial genetic component underlying Parkinson disease (PD).1,2 Linkage analysis and genome-wide association studies (GWAS) confirmed the role of genes involved in familial and sporadic forms of PD.3,4 Genome-wide association studies are able to identify variants with strong genetic effects; however, true polygenic risk alleles with weaker evidence for association may be overlooked.5,6 The estimated heritability in PD GWAS substantially increases when weak effect loci are also considered,2 further emphasizing the involvement of a large proportion of genetic risk variants below standard genome-wide significance thresholds. Moreover, these studies2,5 nominate novel loci that have not been implicated in disease pathogenesis.

The association between inflammation and neurodegenerative diseases has long been observed in Alzheimer disease (AD),7,8 amyotrophic lateral sclerosis, and, highlighted in this work, PD.9-11 In an epidemiological study12 in Sweden, 6 of 33 studied types of autoimmune disorders were identified with an increased risk of developing PD, including amyotrophic lateral sclerosis, hyperthyroidism, hypothyroidism, multiple sclerosis, pernicious anemia, and polymyalgia rheumatica, although this finding was not observed in a population-based case-control study13 from Denmark. The association between PD and MS has been confirmed in other studies.14-16 Furthermore, in clinical studies,17,18 regular users of nonsteroidal anti-inflammatory drugs were found to have lowered risk of PD. It is still not clear whether immune dysfunction has an important role in early stages of PD or is simply the end product of a neuronal degeneration process.19

The occurrence of PD in patients with autoimmune diseases, or vice versa, could reflect genetically determined factors influencing both lipid metabolism and immune disorders that cannot be elucidated by epidemiological and clinical studies alone.19 Genome-wide–based pathway analyses in PD supported the association between PD and autoimmune diseases.5 Early independent studies showed that at least one gene, LRRK2, is statistically significant in both PD20 and Crohn disease.21 The results of a recently published study22 suggested that, along with known PD loci USP25, HLA-DRA, and LRRK2, additional genetic factors are present that contribute to genetic comorbidity shared by PD and CD. A systematic study is needed to decipher whether shared polygenetic risk variants (ie, genetic pleiotropy) exist between PD and autoimmune diseases and whether particular molecular biological pathways are involved.

An approach combining GWAS data from 2 disorders with shared pathways can significantly increase the power to discover novel loci and partly reveal the missing heritability in GWAS. Our group recently developed a novel statistical framework to identify single-nucleotide polymorphisms (SNPs) exhibiting genetic pleiotropy between multiple phenotypes and applied it to identify pleiotropy between AD and autoimmune diseases.23 This approach also identified novel loci between schizophrenia and cardiovascular diseases,24 psychiatric disorders,25 and neurological diseases.26

Herein, we applied this approach to investigate the potentially shared genetic basis for PD and autoimmune diseases. Autoimmune diseases were selected based on available large GWAS, including PD, type 1 diabetes, CD, ulcerative colitis, rheumatoid arthritis celiac disease, psoriasis, and multiple sclerosis.27-33 We used conditional and conjunction false discovery rate analyses to define SNPs associated with both groups of phenotypes (pleiotropic SNPs).

Methods
Participant Samples

Using the conjunction false discovery rate method, this study analyzed GWAS data from a selection of archetypal autoimmune diseases among 138 511 individuals of European ancestry and systemically investigated pleiotropy between PD and type 1 diabetes, Crohn disease, ulcerative colitis, RA, celiac disease, psoriasis, and multiple sclerosis. Genome-wide association studies summary statistic P values and z scores were obtained from the studies of PD,4 CD,27 ulcerative colitis,28 RA,29 type 1 diabetes,30 celiac disease,31 psoriasis,32 and multiple sclerosis33 (eTable 1 in the Supplement). Details of the inclusion criteria and phenotype characteristics of the GWAS are described in the original publications. The relevant institutional review boards or ethics committees approved the research protocol of the individual GWAS used in the present analysis, and all participants gave written informed consent. The dates of the analysis were June 10, 2015, to March 4, 2017. All P values were corrected for inflation using a genomic control procedure.24,25

Statistical Analysis
Conditional Quantile-Quantile Plots

The quantitative estimates of true associations and statistical enrichment were calculated from the distributions of summary statistics.34,35 We plotted conditional quantile-quantile (Q-Q) plots for a primary phenotype by filtering SNPs based on their level of association with a secondary phenotype. Pleiotropic enrichment between PD and an autoimmune disorder was evident if the degree of deflection of PD P values from the expected null line produced successive leftward deflection when conditioned on an autoimmune disease.24,25,36 To control for linkage disequilibrium (LD), we performed a random pruning procedure.37

Conditional and Conjunction False Discovery Rate

We defined conditional false discovery rate, denoted by FDRtrait1|trait2, as the posterior probability that a given SNP is null for the first trait given that the P values in both traits are smaller than their observed P values.24,25 We defined conjunction false discovery rate, denoted by FDRtrait1&trait2, as the posterior probability that a given SNP is null for both phenotypes simultaneously given that the P values for both traits are as small or smaller than the observed P values. We obtained a conservative estimate of conjunction false discovery rate by taking the minimum of FDRtrait1|trait2 and FDRtrait2|trait1. To control for LD, we applied a random pruning procedure.37 Detailed information on the methods can be found in prior studies.24,25

NeuroX Data

We replicated the top conjunction false discovery rate loci, highly associated with both PD and autoimmune disorders, in a second independent PD data set. The data set was generated with the NeuroX exome array,3,38 including 6927 PD cases and 6108 controls. Variants passing standard quality control (Hardy-Weinberg equilibrium P > 1 × 10−6 and maximum missingness rate of 5%) were tested for association with PD with a logistic model correcting for the first 4 multidimensional scaling components and sex.

Gene Expression and Methylation Changes

We determined the regional methylation and expression patterns within ±1 megabases of 103 SNPs of interest. We investigated frontal cortex and cerebellum microarray data from the North American Brain Expression Consortium (NABEC)39 and the United Kingdom Brain Expression Consortium (UKBEC)40 of 396 European samples without neuropathological evidence of disease. We also accessed a second expression quantitative trait loci (eQTL) data set based on cap analysis gene expression profiling technique of the frontal cortex of 119 NABEC samples.41 A total of 98 variants (3 not testable) and 83 variants (20 not testable) were studied for the microarray-based and cap analysis gene expression–based data sets, respectively. For details of these procedures, see the eMethods in the Supplement.

Genetic Correlations Among Implicated Loci

To investigate the genetic relatedness among implicated SNPs, we performed protein-protein interaction analyses using STRING version 10.42 The input consisted of novel loci as identified in pleiotropic analyses. We considered total scores above 0.400 (medium confidence) that correspond to the combination of the following 4 different scores: coexpression, experimental, knowledge, and text mining.

Results
Significant Genetic Overlap Between PD and Autoimmune Diseases

Conditional Q-Q plots for PD conditioned on association P values of autoimmune diseases showed strong enrichment for Crohn disease (Figure 1). Successive leftward shifts for decreasing nominal PD P values indicated that the proportion of non-null SNPs in PD increased considerably with higher levels of association with an autoimmune phenotype. For example, when conditioned on CD, the proportion of SNPs reaching a significance of PD P < 10−5 in the category Crohn disease P < 10−3 is 20 times greater than when all SNPs were examined (Figure 1 and eFigure 1 in the Supplement). Similar enrichment was found with ulcerative colitis and RA, and weaker enrichment was found with celiac disease and multiple sclerosis. The enrichment remained after removing the major histocompatibility complex and MAPT regions (eFigure 2 in the Supplement).

Shared Susceptibility Loci for PD and Autoimmune Disorders

We performed a conjunction false discovery rate analysis and visualized the pleiotropic loci between PD and autoimmune diseases in a Manhattan plot (Figure 2). Based on conjunction false discovery rate less than 0.05, we detected 17 independent pleiotropic loci for the 7 autoimmune diseases (Table 1). Nine loci remained after excluding the major histocompatibility complex and MAPT regions (eTable 2 in the Supplement). Of the 17 loci, the directions of PD effect given by z scores were mostly the same with Crohn disease, ulcerative colitis, and celiac disease and opposite with rheumatoid arthritisand psoriasis (Table 1 and eTable 3 in the Supplement). The conjunction false discovery rate analyses over multiple autoimmune phenotypes showed overlapping susceptibility loci between Crohn disease and ulcerative colitis and demonstrated some overlap between ulcerative colitis, RA, celiac disease, and multiple sclerosis (eFigure 3 in the Supplement). We were able to replicate 5 loci in our in-house independent NeuroX data at P < .05 (Table 1). In addition to the previously published HLA, LRRK2, and MAPT associations,4 we also identified 2 new loci adjacently located to TRIM10 and SETD1A.

Functional Interpretation of Shared Susceptibility Loci

A total of 103 associated variants resulting from conditional false discovery rate less than 0.01 and conjunction false discovery rate less than 0.05 were tested for being a methylation QTL (methQTL) or an eQTL. Table 2 summarizes the significant methQTL and eQTL in the brain in which the affected gene is implied by the literature (see the Discussion section) to have a function in the immune system. As expected, most hits are for variants located in the HLA locus and MAPT locus, both of which have been implicated in PD.3,37,43-45 Within the NABEC data set, 31 of the 103 variants were shown to have a significant effect on the methylation status of 16 genes (eTable 4 in the Supplement). Likewise, 29 variants were significantly associated with changes in expression of 14 genes in the NABEC, UKBEC, or in-house eQTL data set (eTable 5 in the Supplement).

In addition to the exploration for methQTL and eQTL within the described data sets, we compared a recent elaborate eQTL study46 of multiple immune cell types (B cells, CD4 T cells, CD8 T cells, monocytes, and neutrophils) in patients with inflammatory bowel disease and healthy controls. Our 103 candidate SNPs intersected with those authors’ significant (false discovery rate <0.05) eQTL results. eTable 6 in the Supplement lists significant eQTL for 10 variants influencing the expression of 8 genes, 5 of which (DGKQ, IDUA, BST1, CD38, and SNCA) have previously been discussed in the context of PD.4 These SNPs could contribute to PD risk through immune mechanisms by regulating the gene expression of these PD-related genes in these immune-specific cells. Six immune eQTL that regulate the expression of 2 genes (DGKQ and DMPK) were also observed in the brain eQTL and methQTL data, affirming the immune-related involvement of these genes in PD.

Shared Biological Pathways Between Significant Risk Loci

Using functional gene networks and protein interaction networks, the connectivity among the loci in the combined network increased considerably compared with the networks represented by pleiotropic and PD loci (Figure 3). The network analyses revealed interaction between the 17 loci identified in our study with nodes defined by PD loci (eg, GUCY1A3, KANSL1, CRHR1, WNT3, and BOLA2). This finding demonstrates biological relatedness between the pleiotropic loci from the present analysis and PD loci from previous reports.47

Discussion

Genetic comorbidities between PD and immune-related genes have only been explored for high-risk PD loci.5,6,22 Our study using a genome-wide unbiased statistical approach identified 17 shared loci between PD and 7 autoimmune diseases. This finding strengthens the hypothesis that known PD risk genes might contribute to PD through immune system defects (eg, LRRK2, GAK, HLA, and MAPT),21,48 while suggesting that additional loci with weaker associations also contribute to pleiotropy (Table 1). TRIM10 is closely positioned to the HLA region, and, although not extensively described, variants in SETD1A have also previously been associated with PD.49,50 While we found an overlap between genetic risk factors for autoimmune diseases and PD, the specific loci involved for each trait differed. This result is consistent with the findings that some susceptibility loci are associated with risk in multiple immune-mediated diseases.51

Because our method considers joint P values, some SNPs with strong association with PD might not pass the significance threshold if they only had marginal association with the autoimmune disease, and vice versa. For instance, a locus in SNCA has one of the strongest associations in PD4 (PD P = 3.67 × 10−26), but it is not associated with autoimmune diseases (RA P = .6184). Taking the 2 P values together, its conjunction false discovery rate is not significant (PD and rheumatoid arthritisconjunction false discovery rate of 0.9856). Among the 13 significant genes in our PD data set,4 we did not find significant conjunction false discovery rate loci among SYT11, ACMSD, STK39, MCCC1/LAMP3, BST1, SNCA, and CCDC62/HIP1R owing to weak association with autoimmune disorders (eTable 7 in the Supplement). Likewise, some known risk loci for immune diseases were not found in our results: one locus near CARD15/NOD2 was significant in Crohn disease (P = 1.21 × 10−58) but not in PD (P = .2163), and its conjunction false discovery rate of 0.8496 did not pass our threshold. MC1R has been reported to be associated with PD and immune-mediated diseases,52,53 but it had not been reported in our data sets of PD (P = .2936) or Crohn disease (P = .2936).

Our brain-based QTL analyses suggest immune function–related genes for which the expression or methylation level is changed by of one of our identified susceptibility loci. Most of these genes are located in the HLA locus or MAPT locus, and owing to the complex underlying LD structures, it is difficult to define the true causal genes. However, our analyses implicate that, in addition to the PD-associated HLA genes and MAPT in these loci, TRIM31, CRHR1, PLEKHM1, and NSF might also be related to PD through defective components of the immune system.54-56 For example, methylation levels of TRIM31 seem to be affected in the cerebellum by 2 susceptibility loci (rs9261531 and rs9261535) in TRIM10. It is hypothesized that TRIM family proteins are active in the innate immune response to intracellular infectious agents.57,58 In addition to known PD loci, there is one novel susceptibility locus that has an effect on methylation levels. This variant (rs7515174) is located in the third intron and affects the frontal cortex methylation state of FCGR2A. This gene encodes a protein belonging to the IgGFc receptor gene family in which the encoded proteins are located on the surface of many immune response cells and take part in clearing of immune complexes and phagocytosis.59-61 Variants in FCGR2A have been associated with inflammatory bowel disease, Crohn disease, and ulcerative colitis,62 and variants in other genes from the same family were associated with RA.63 Of further interest are the 8 identified genes in which the expression is regulated by 10 pleiotropic SNPs (from 5 loci) in several specific immune cell types.46 Five of the 8 genes are located in 3 loci previously associated with PD.3 For example, SNCA expression is regulated by a pleiotropic variant (rs2736990) in intron 2 of SNCA in monocytes. This finding seems in line with a previous study64 describing an increase of monocytes in peripheral blood of patients with PD, implying an immune-related manifestation of PD through monocytes. RNPS1, DMPK, and DMWD (with the latter 2 genes involved in myotonic dystrophy65) are 3 of the 8 immune-based eQTL that are newly associated with PD in the present study. The biological functions of these genes involve messenger RNA modification and intracellular trafficking or are unknown.66,67

We used pathway analyses to discern the underlying relevant pathways; however, functional studies are pertinent to provide biological insight. Performing downstream pathogenetic analyses using cell-based models is beyond the scope of the present work. Nevertheless, we anticipate that the genetic loci highlighted in our study will motivate the scientific community to pursue this line of research.

The strong pleiotropic enrichment observed between PD and Crohn disease suggests a common pathogenetic link between these 2 phenotypes. Previous studies21,48 highlighted LRRK2 as a significant risk factor for both of these phenotypes. LRRK2 has been identified as having 2 independent Crohn disease risk loci (rs11564258 and rs3761863)68: only one of them is in high LD to our shared susceptibility locus rs17467164 (r2 = 0.992 and r2 = 0.075, respectively). The observed association between PD and Crohn disease indicates that defects in cargo transport mechanism might underline the disease pathogenesis in both phenotypes.69 It is known that gastrointestinal tract dysfunction is associated with PD, perhaps even preceding the onset of central motor symptoms.70 Several of the identified overlapping genes (CCNY, LRRK2, MAPT, RSPH6A, and SYMPK) are involved in basic cellular functions that may be related to alterations in enteric neurotransmission or intestinal motility disturbances.71 Furthermore, the shared gene HLA-DQB1 has a central role in the immune system by introducing peptides derived from extracellular proteins, which implicate overlapping factors related to the immune system (CD4 T cells).

We found moderate polygenic pleiotropic enrichment between PD and ulcerative colitis or RA, whereas genetic enrichment with type 1 diabetes, celiac disease, psoriasis, and multiple sclerosis was weak. In comparison, in a population-based study,13 the risk for PD was observed to increase in a subset of the cohort with autoimmune diseases and ulcerative colitis and to decrease in those with a previous diagnosis of RA. The epidemiological data in that investigation are in agreement with a recently published study22 in which genetic comorbidities with PD were explored using top loci from diverse phenotypes. The authors observed a decreased risk for rheumatoid arthritisand psoriasis, but the findings were not statistically significant because of the small sample size. It is unlikely that patients with unrecognized immune-related disorders were included in the PD study13 population in large enough numbers to affect the results; however, some participants in the autoimmune disorders population13 could develop PD over time.

Inflammation of microglia, the major resident immune cells in the brain,72 has been involved in degeneration of dopaminergic neurons affecting PD.19,73 The extent of genetic pleiotropy observed between PD and autoimmune diseases will help us to understand novel pathogenetic aspects of neurodegeneration in PD, a chronic immune activation of microglia, which may cause or contribute to degeneration of neurons. For example, it has been shown that aggregated α-synuclein protein (by overexpression of SNCA) activates the microglia, which increases nitration of α-synuclein and maintains the proinflammatory innate immune response in PD.74 In this context, the present findings of a polygenic link between PD and inflammatory biological function are likely to be relevant. Furthermore, recent evidence suggests that immune factors are also involved in other neurodegenerative diseases, such as AD.23

Limitations and Strengths

Our pleiotropy-based statistical framework was limited to GWAS with a high coverage of SNPs (>500 000); therefore, autoimmune disorders selected for this study were based on available GWAS data that fit these criteria. With more extensive GWAS data, it would be worthwhile to study a larger set of immune disorders associated with PD from epidemiological or clinical studies (eg, thyroid disease).12 Our study is also limited in distinguishing between immune-mediated and autoimmune disease. It has been hypothesized that PD itself is an autoimmune disease.75 Although we have shown herein using GWAS of autoimmune disorders that PD has a strong immune component, the conclusion of the hypothesis that PD is an autoimmune disease should be investigated through further cell-based functional studies.

This work has clinical implications. Our data suggest more extensive clinical studies of patients with immune-mediated disorders for PD signs to develop possible screening schemes for PD, and vice versa, for monitoring immune and inflammatory status among individuals with an increased risk for PD.76 According to our study, apparently healthy individuals with a high load of these shared risk genotypes, predisposing them to inflammation disturbances, could be at particularly increased risk for developing PD. Further prospective studies in these individuals may clarify these issues. Our findings also suggest the need for further investigation of the role of immune-modulating agents in the treatment of PD. There is some evidence indicating that anti-immune drugs could be a viable option for therapeutic interventions in PD. A 2004 study76 showed that candesartan cilexetil, a drug used for hypertension, reduces the α-synuclein–induced microglia phenotype. Therefore, data generated from our study may facilitate novel treatment strategies by increasing our understanding of the pathogenetic mechanisms influenced by pleiotropic disease loci.

Conclusions

In summary, our study highlights the usefulness of cross-phenotype analyses to identify genetic overlap (ie, pleiotropic loci) between PD and a selection of autoimmune disorders. Our results suggest that these PD-associated loci contribute to PD through immune defects and that immune dysfunction is not simply the end product of the neurodegeneration process. The findings strongly support the presence of interaction between the immune system and neurodegeneration in PD.

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Article Information

Accepted for Publication: March 8, 2017.

Corresponding Author: Manu Sharma, PhD, Centre for Genetic Epidemiology, Institute for Clinical Epidemiology and Applied Biometry, University of Tübingen, Silcherstrasse 5, 72076 Tübingen, Germany (manu.sharma@uni-tuebingen.de).

Published Online: June 5, 2017. doi:10.1001/jamaneurol.2017.0469

Author Contributions: Dr Witoelar had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Witoelar, Desikan, Thompson, Wood, Dale, Gasser, Andreassen, Sharma.

Acquisition, analysis, or interpretation of data: Witoelar, Jansen, Wang, Gibbs, Blauwendraat, Hernandez, Djurovic, Schork, Bettella, Ellinghaus, Franke, Lie, McEvoy, Karlsen, Lesage, Morris, Brice, Heutink, Hardy, Singleton, Dale, Andreassen, Sharma.

Drafting of the manuscript: Witoelar, Jansen, Desikan, Andreassen, Sharma.

Critical revision of the manuscript for important intellectual content: Witoelar, Wang, Desikan, Gibbs, Blauwendraat, Thompson, Hernandez, Djurovic, Schork, Bettella, Ellinghaus, Franke, Lie, McEvoy, Karlsen, Lesage, Morris, Brice, Wood, Heutink, Hardy, Singleton, Dale, Gasser, Andreassen, Sharma.

Statistical analysis: Witoelar, Jansen, Wang, Thompson, Ellinghaus, Lie, Dale.

Obtained funding: Heutink, Hardy, Dale, Gasser, Andreassen, Sharma.

Administrative, technical, or material support: Wang, Desikan, Gibbs, Hernandez, Djurovic, Franke, Singleton, Dale, Andreassen.

Study supervision: Wang, Desikan, Wood, Heutink, Hardy, Dale, Gasser, Andreassen, Sharma.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (SysINFLAME grant 01ZX1306A). This project received infrastructure support from the Deutsche Forschungsgemeinschaft (DFG) Excellence Cluster 306 “Inflammation at Interfaces.” Dr Jansen receives funding from Prinses Beatrix Fonds. Dr Franke receives an endowment professorship by the Foundation for Experimental Medicine (Zurich, Switzerland). Dr Andreassen and his team are supported by The Research Council of Norway (213837, 225989, 223273, and 475 237250/EU Joint Programme–Neurodegenerative Disease Research [EU-JPND]), the South East Norway Health Authority (2013-123), the Norwegian Health Association, and the K. G. Jebsen Foundation. Dr Sharma receives funding from The Michael J Fox Foundation for Parkinson’s Research and the EU-JPND program (Comprehensive Unbiased Risk Factor Assessment for Genetics and Environment in Parkinson’s Disease [COURAGE-PD]).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Information: The International Parkinson’s Disease Genomics Consortium (IPDGC), North American Brain Expression Consortium (NABEC), and United Kingdom Brain Expression Consortium (UKBEC) investigators are as follows:

IPDGC: Mike A. Nalls (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Vincent Plagnol (UCL Genetics Institute, London, England), Dena G. Hernandez (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland; and Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Manu Sharma (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and DZNE, German Center for Neurodegenerative Diseases, Tübingen), Una-Marie Sheerin (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Mohamad Saad (INSERM U563, CPTP, Toulouse, France; and Paul Sabatier University, Toulouse, France), Javier Simón-Sánchez (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany), Iris E. Jansen (Department of Clinical Genetics, VU University Medical Center, Amsterdam, the Netherlands), Claudia Schulte (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany), Suzanne Lesage (INSERM, UMR_S975 [formerly UMR_S679], Paris, France; Université Pierre et Marie Curie–Paris, Centre de Recherche de l’Institut du Cerveau et de la Moelle Épinière, Paris, France; and CNRS, Paris, France), Sigurlaug Sveinbjörnsdóttir (Department of Neurology, Landspítali University Hospital, Reykjavík, Iceland; Department of Neurology, MEHT Broomfield Hospital, Chelmsford, Essex, England; and Queen Mary College, University of London, London, England), Sampath Arepalli (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), Roger Barker (Department of Neurology, Addenbrooke’s Hospital, University of Cambridge, Cambridge, England), Yoav Ben-Shlomo (School of Social and Community Medicine, University of Bristol, Bristol, England), Henk W. Berendse (Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands), Daniela Berg (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and DZNE, German Center for Neurodegenerative Diseases, Tübingen), Kailash Bhatia (Department of Motor Neuroscience, UCL Institute of Neurology, London, England), Rob M. A. de Bie (Department of Neurology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands), Alessandro Biffi (Center for Human Genetic Research and Department of Neurology, Massachusetts General Hospital, Boston; and Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts), Bas Bloem (Department of Neurology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands), Zoltan Bochdanovits (Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, the Netherlands), Michael Bonin (Department of Medical Genetics, Institute of Human Genetics, University of Tübingen, Tübingen, Germany), Jose M. Bras (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Kathrin Brockmann (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany), Janet Brooks (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), David J. Burn (Newcastle University Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle Upon Tyne, England), Elisa Majounie (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), Gavin Charlesworth (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Codrin Lungu (National Institutes of Health Parkinson Clinic, NINDS, National Institutes of Health, Bethesda, Maryland), Honglei Chen (Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina), Patrick F. Chinnery (Neurology M4104, The Medical School, Framlington Place, Newcastle Upon Tyne, England), Sean Chong (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), Carl E. Clarke (School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, England; and Department of Neurology, City Hospital, Sandwell and West Birmingham Hospitals NHS Trust, Birmingham, England), Mark R. Cookson (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), J. Mark Cooper (Department of Clinical Neurosciences, UCL Institute of Neurology, London, England), Jean Christophe Corvol (INSERM, UMR_S975, Paris, France; Université Pierre et Marie Curie–Paris, Paris, France; CNRS, Paris, France; and INSERM CIC-9503, Hôpital Pitié-Salpêtrière, Paris, France), Carl Counsell (University of Aberdeen, Division of Applied Health Sciences, Population Health Section, Aberdeen, Scotland), Philippe Damier (CHU Nantes, CIC0004, Service de Neurologie, Nantes, France), Jean-François Dartigues (INSERM U897, Université Victor Segalen, Bordeaux, France), Panos Deloukas (Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge, England), Günther Deuschl (Klinik für Neurologie, Universitätsklinikum Schleswig-Holstein, Campus Kiel, Christian Albrechts University of Kiel, Kiel, Germany), David T. Dexter (Parkinson’s Disease Research Group, Faculty of Medicine, Imperial College London, London, England), Karin D. van Dijk (Department of Neurology and Alzheimer Center, VU University Medical Center, Amsterdam, the Netherlands), Allissa Dillman (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), Frank Durif (Service de Neurologie, Hôpital Gabriel Montpied, Clermont-Ferrand, France), Alexandra Dürr (INSERM, UMR_S975, Paris, France; Université Pierre et Marie Curie–Paris; CNRS, Paris, France; and AP-HP, Pitié-Salpêtrière Hospital, Paris, France), Sarah Edkins (Wellcome Trust Sanger Institute, Cambridge, England), Jonathan R. Evans (Cambridge Centre for Brain Repair, Cambridge, England), Thomas Foltynie (UCL Institute of Neurology, London, England), Jing Dong (Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina), Michelle Gardner (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), J. Raphael Gibbs (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland; and Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Alison Goate (Departments of Psychiatry and Neurology, Washington University School of Medicine in St Louis, St Louis, Missouri), Emma Gray (Wellcome Trust Sanger Institute, Cambridge, England), Rita Guerreiro (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Clare Harris (University of Aberdeen, Aberdeen, Scotland), Jacobus J. van Hilten (Department of Neurology, Leiden University Medical Center, Leiden, the Netherlands), Albert Hofman (Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands), Albert Hollenbeck (AARP, Washington, DC), Janice Holton (Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, London, England), Michele Hu (Department of Clinical Neurology, John Radcliffe Hospital, Oxford, England), Xuemei Huang (Departments of Neurology, Radiology, Neurosurgery, Pharmacology, Kinesiology, and Bioengineering, Pennsylvania State University–Milton S. Hershey Medical Center, Hershey), Isabel Wurster (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and German Center for Neurodegenerative Diseases, Tübingen, Germany), Walter Mätzler (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; and German Center for Neurodegenerative Diseases Tübingen, Germany), Gavin Hudson (Neurology M4104, The Medical School, Newcastle Upon Tyne, England), Sarah E. Hunt (Wellcome Trust Sanger Institute, Cambridge, England), Johanna Huttenlocher (deCODE genetics, Reykjavík, Iceland), Thomas Illig (Institute of Epidemiology, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg), Pálmi V. Jónsson (Department of Geriatrics, Landspítali University Hospital, Reykjavík, Iceland), Jean-Charles Lambert (INSERM U744, Lille, France; and Institut Pasteur de Lille, Université de Lille Nord, Lille, France), Cordelia Langford (Cambridge Centre for Brain Repair, Cambridge, England), Andrew Lees (Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, London, England), Peter Lichtner (Institute of Human Genetics, Helmholtz Zentrum München, German Research Centre for Environmental Health, Neuherberg), Patricia Limousin (Institute of Neurology, Sobell Department, Unit of Functional Neurosurgery, London, England), Grisel Lopez (Section on Molecular Neurogenetics, Medical Genetics Branch, NHGRI, National Institutes of Health, Bethesda, Maryland), Delia Lorenz (Klinik für Neurologie, Universitätsklinikum Schleswig-Holstein, Kiel, Germany), Codrin Lungu (National Institutes of Health Parkinson Clinic, NINDS, National Institutes of Health, Bethesda, Maryland), Alisdair McNeill (Department of Clinical Neurosciences, UCL Institute of Neurology, London, England), Catriona Moorby (School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, England), Matthew Moore (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), Huw R. Morris (National Hospital for Neurology and Neurosurgery, University College London, London, England), Karen E. Morrison (School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, England; and Neurosciences Department, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, England), Valentina Escott-Price (MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, England), Ese Mudanohwo (Neurogenetics Unit, UCL Institute of Neurology, London, England; and National Hospital for Neurology and Neurosurgery, University College London, London, England), Sean S. O’Sullivan (Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, London, England), Justin Pearson (MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, England), Joel S. Perlmutter (Departments of Neurology, Radiology, and Neurobiology at Washington University in St Louis, St Louis, Missouri), Hjörvar Pétursson (deCODE genetics, Reykjavík, Iceland; and Department of Medical Genetics, Institute of Human Genetics, University of Tübingen, Tübingen, Germany), Pierre Pollak (Service de Neurologie, CHU de Grenoble, Grenoble, France), Bart Post (Department of Neurology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands), Simon Potter (Wellcome Trust Sanger Institute, Cambridge, England), Bernard Ravina (Translational Neurology, Biogen Idec, Cambridge, Massachusetts), Tamas Revesz (Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, London, England), Olaf Riess (Department of Medical Genetics, Institute of Human Genetics, University of Tübingen, Tübingen, Germany), Fernando Rivadeneira (Departments of Epidemiology and Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands), Patrizia Rizzu (Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, the Netherlands), Mina Ryten (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Stephen Sawcer (University of Cambridge, Department of Clinical Neurosciences, Addenbrooke’s Hospital, Cambridge, England), Anthony Schapira (Department of Clinical Neurosciences, UCL Institute of Neurology, London, England), Hans Scheffer (Department of Human Genetics, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands), Karen Shaw (Queen Square Brain Bank for Neurological Disorders, UCL Institute of Neurology, London, England), Ira Shoulson (Department of Neurology, University of Rochester, Rochester, New York), Joshua Shulman (Baylor College of Medicine, Houston, Texas), Ellen Sidransky (Section on Molecular Neurogenetics, Medical Genetics Branch, NHGRI, National Institutes of Health, Bethesda, Maryland), Colin Smith (Department of Pathology, University of Edinburgh, Edinburgh, Scotland), Chris C. A. Spencer (Wellcome Trust Centre for Human Genetics, Oxford, England), Hreinn Stefánsson (deCODE genetics, Reykjavík, Iceland), Francesco Bettella (deCODE genetics, Reykjavík, Iceland), Joanna D. Stockton (School of Clinical and Experimental Medicine, University of Birmingham, Birmingham, England), Amy Strange (Wellcome Trust Centre for Human Genetics, Oxford, England), Kevin Talbot (University of Oxford, Department of Clinical Neurology, John Radcliffe Hospital, Oxford, England), Carlie M. Tanner (Clinical Research Department, The Parkinson’s Institute and Clinical Center, Sunnyvale, California), Avazeh Tashakkori-Ghanbaria (Wellcome Trust Sanger Institute, Cambridge, England), François Tison (Service de Neurologie, Hôpital Haut-Lévêque, Pessac, France), Daniah Trabzuni (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Bryan J. Traynor (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland), André G. Uitterlinden (Departments of Epidemiology and Internal Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands), Daan Velseboer (Department of Neurology, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands), Marie Vidailhet (INSERM, UMR_S975, Université Pierre et Marie Curie–Paris, CNRS, UMR 7225, Paris, France), Robert Walker (Department of Pathology, University of Edinburgh, Edinburgh, Scotland), Bart van de Warrenburg (Department of Neurology, Radboud University Nijmegen Medical Centre, Nijmegen, the Netherlands), Mirdhu Wickremaratchi (Department of Neurology, Cardiff University, Cardiff, England), Nigel Williams (MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, England), Caroline H Williams-Gray (Department of Neurology, Addenbrooke’s Hospital, University of Cambridge, Cambridge, England), Sophie Winder-Rhodes (Department of Psychiatry and Medical Research Council and Wellcome Trust Behavioural and Clinical Neurosciences Institute, University of Cambridge, Cambridge, England), Kári Stefánsson (deCODE genetics, Reykjavík, Iceland), Maria Martinez (INSERM UMR 1043, Paul Sabatier University, Toulouse, France), Nicholas W. Wood (UCL Genetics Institute, London, England; and Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), John Hardy (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Peter Heutink (DZNE, German Center for Neurodegenerative Diseases, Tübingen; and Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany), Alexis Brice (INSERM, UMR_S975, Université Pierre et Marie Curie–Paris, CNRS, UMR 7225, AP-HP, Pitié-Salpêtrière Hospital, Paris, France), Thomas Gasser (Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, and DZNE, German Center for Neurodegenerative Diseases, Tübingen, Germany), and Andrew B. Singleton (Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland).

NABEC: Sampath Arepalli (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Mark R. Cookson (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Allissa Dillman (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Luigi Ferrucci (Clinical Research Branch, National Institute on Aging, Baltimore, Maryland), J. Raphael Gibbs (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland; and Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Dena G. Hernandez (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland; and German Center for Neurodegenerative Diseases (DZNE)–Tübingen, Tübingen, Germany), Robert Johnson (NICHD Brain and Tissue Bank for Developmental Disorders, University of Maryland Medical School, Baltimore), Dan L. Longo (Lymphocyte Cell Biology Unit, Laboratory of Immunology, National Institute on Aging, National Institutes of Health, Baltimore, Maryland), Michael A. Nalls (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Richard O’Brien (Brain Resource Center, The Johns Hopkins University, Baltimore, Maryland), Andrew Singleton (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Bryan Traynor (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland), Juan Troncoso (Brain Resource Center, The Johns Hopkins University, Baltimore, Maryland), Marcel van der Brug (Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland; and ITGR Biomarker Discovery Group, Genentech, South San Francisco, California), H. Ronald Zielke (NICHD Brain and Tissue Bank for Developmental Disorders, University of Maryland Medical School, Baltimore), and Alan Zonderman (Research Resources Branch, National Institute on Aging, National Institutes of Health, Bethesda, Maryland).

UKBEC: John A. Hardy (Department of Molecular Neuroscience, UCL Institute of Neurology, London, England), Mina Ryten (Department of Molecular Neuroscience, UCL Institute of Nerurology, London, England), Colin Smith (Department of Pathology, The University of Edinburgh, Edinburgh, Scotland), Daniah Trabzuni (Department of Molecular Neuroscience, UCL Institute of Nerurology, London, England), Robert Walker (Department of Pathology, The University of Edinburgh, Edinburgh, Scotland), and Mike Weale (Department of Medical & Molecular Genetics, King’s College London, London, England).

Additional Contributions: We thank Cisca Wijmenga, PhD (Department of Genetics, University of Groningen), David van Heel, PhD (Centre for Genomics and Child Health, Queen Mary University of London), Annegret Fischer, PhD (Sarcoidosis Research, Christian Albrechts University of Kiel), Eva Ellinghaus, PhD (Genetics & Bioinformatics, Christian Albrechts University of Kiel), the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC), and the Psoriasis Association Genetics Extension (PAGE) for access to summary results data. No compensation was received.

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