ARTICLE OPEN ACCESS

Genome-wide Association Study Identifies 2 NewLoci Associated With Anti-NMDAR EncephalitisAnja K Tietz MSc Klemens Angstwurm MD Tobias Baumgartner MD Kathrin Doppler MD

Katharina Eisenhut MD Martin Elisak MD Andre Franke PhD Kristin S Golombeck MD Robert Handreka

Max Kaufmann MD Markus Kraemer MD Andrea Kraft MD Jan Lewerenz MD Wolfgang Lieb MD

Marie Madlener MD Nico Melzer MD Hana Mojzisova MD Peter Moller MD Thomas Pfefferkorn MD

Harald Pruss MD Kevin Rostasy MD Margret Schnegelsberg MD Ina Schroder Kai Siebenbrodt MD

Kurt-Wolfram Suhs MD Jonathan Wickel MD Klaus-Peter Wandinger MD Frank Leypoldt MD and

Gregor Kuhlenbaumer MD PhD on behalf of the German Network for Research on Autoimmune

Encephalitis (GENERATE)

Neurol Neuroimmunol Neuroinflamm 20218e1085 doi101212NXI0000000000001085

Correspondence

Dr Kuhlenbaumer

gkuhlenbaeumer

neurologieuni-kielde

AbstractBackground and ObjectivesTo investigate the genetic determinants of the most common type of antibody-mediatedautoimmune encephalitis anti-NMDA receptor (anti-NMDAR) encephalitis

MethodsWe performed a genome-wide association study in 178 patients with anti-NMDAR encephalitisand 590 healthy controls followed by a colocalization analysis to identify putatively causal genes

ResultsWe identified 2 independent risk loci harboring genome-wide significant variants (p lt 5 times 10minus8OR ge 22) 1 on chromosome 15 harboring only the LRRK1 gene and 1 on chromosome 11centered on the ACP2 and NR1H3 genes in a larger region of high linkage disequilibriumColocalization signals with expression quantitative trait loci for different brain regions andimmune cell types suggested ACP2 NR1H3MADD DDB2 and C11orf49 as putatively causalgenes The best candidate genes in each region are LRRK1 encoding leucine-rich repeat kinase1 a protein involved in B-cell development and NR1H3 liver X receptor alpha a transcriptionfactor whose activation inhibits inflammatory processes

DiscussionThis study provides evidence for relevant genetic determinants of antibody-mediated auto-immune encephalitides outside the human leukocyte antigen (HLA) region The results sug-gest that future studies with larger sample sizes will successfully identify additional geneticdeterminants and contribute to the elucidation of the pathomechanism

From the Department of Neurology (AKT FL GK) Kiel University Department of Neurology (KA) University Hospital Regensburg Department of Epileptiology (TB) UniversityHospital Bonn Department of Neurology (KD) University Hospital Wurzburg Institute of Clinical Neuroimmunology (KE) Biomedical Center and University Hospital LudwigMaximilians University Munich Germany Department of Neurology (ME HM) Charles University Second Faculty of Medicine and Motol University Hospital Prague CzechRepublic Institute of Clinical Molecular Biology (AF) Kiel University Department of Neurology (KSG) University Hospital Munster Department of Neurology (RH) Carl-Thiem-Klinikum Cottbus Institute of Neuroimmunology and Multiple Sclerosis (INIMS) (Max Kaufmann) University Medical Center Hamburg-Eppendorf Department of Neurology (MarkusKraemer) Alfried Krupp Hospital Essen Department of Neurology (Markus Kraemer NM) Medical Faculty Heinrich-Heine University Dusseldorf Department of Neurology (AK)Martha-Maria Hospital Halle Department of Neurology (JL) University of Ulm Institute of Epidemiology (WL) Kiel University Department of Neurology (MM) University HospitalCologne Department of Neurology and Clinical Neurophysiology (PM) Klinikum Weimar Department of Neurology (TP) Klinikum Ingolstadt Department of Neurology andExperimental Neurology (HP) Charite - Universitatsmedizin Berlin and German Center for Neurodegenerative Diseases (DZNE) Berlin Department of Pediatric Neurology (KR)Childrenrsquos Hospital Datteln WittenHerdecke University Department of Neurology (MS) Asklepios Hospitals Schildautal Seesen Neuroimmunology (IS K-PW FL) Institute ofClinical Chemistry University Hospital Schleswig-Holstein KielLubeck Epilepsy Center Frankfurt Rhine-Main and Department of Neurology (KS) Unversity Hospital and GoetheUniversiy Frankfurt Department of Neurology (K-WS) Hannover Medical School and Section Translational Neuroimmunology (JW) Department of Neurology University HospitalJena Germany

Go to NeurologyorgNN for full disclosures Funding information is provided at the end of the article

German Network for Research on Autoimmune Encephalitis (GENERATE) coinvestigators are listed in Appendix 2 at linkslwwcomNXIA587

The Article Processing Charge was funded by BMBF via UKSH Kiel

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License 40 (CC BY-NC-ND) which permits downloadingand sharing the work provided it is properly cited The work cannot be changed in any way or used commercially without permission from the journal

Copyright copy 2021 The Author(s) Published by Wolters Kluwer Health Inc on behalf of the American Academy of Neurology 1

Antibody-mediated encephalitides are a recently discoveredgroup of rare diseases caused by autoantibodies against CNSsystem antigens1 Subgroups are defined by the respectivetarget antigens The most common subgroup is caused byimmunoglobulin G (IgG) class antibodies against the GluN1subunit of the NMDA receptor (NMDAR) the most im-portant excitatory neurotransmitter in the CNS NMDARantibodies cause internalization of surface NMDAR therebyreducing signal transduction Anti-NMDAR encephalitis af-fects children and adults with a female preponderance Theestimated prevalence is 06100000 population2 It manifestswith behavioral changes psychiatric symptoms epileptic sei-zures memory dysfunction movement disorders and loss ofconsciousness and often responds favorably to immunother-apy3 A definite diagnosis requires the detection of anti-NMDAR antibodies of the IgG class in serum andor CSFKnown trigger factors include ovarian teratomas with ectopicexpression of NMDAR and viral (mostly herpes simplex virustype 1 [HSV-1]) encephalitis1 In a first genome-wide asso-ciation study (GWAS) of antibody-mediated encephalitidesin a much smaller sample we found no variants showinggenome-wide significant association with anti-NMDAR en-cephalitis4 For this study we doubled the sample sizemodified the analysis parameters and added colocalizationanalysis to identify putatively causal genes

MethodsStudy PopulationIn this case-control study we analyzed 212 samples frompatients with NMDAR antibodies (197 German and 15Czech patients) collected in the years 2015ndash2020 Anti-NMDAR encephalitis was classified according to consensuscriteria based on a compatible clinical syndrome together withdetection of IgG-NMDAR antibodies in serum andor CSF3

One hundred seven patients were already included in ourprevious GWAS4 The additional 105 individuals were eitherrecruited via the German Network for Research on Autoim-mune Encephalitis (GENERATE n = 80) or specifically forthis study (n = 25) For patient recruitment we contacted thecenters of the GENERATE network as well as other neuro-logic departments caring for patients with antibody-mediatedencephalitides All contributing scientists are listed in

eTable 1 linkslwwcomNXIA587 Healthy control samples(n = 1219) were obtained from the PopGen study apopulation-based biobank from northern Germany56

GenotypingGenomic DNA was isolated from blood (n = 150) or saliva(n = 62) using standard procedures The samples were gen-otyped in 3 batches at the Institute of Clinical MolecularBiology Kiel University on Infinium Global Screening Array(GSA Illumina) Array version 20 was used for cases andversion 10 for healthy controls Genotypes were called usingIllumina GenomeStudio 20 according to the manufacturerrsquosinstructions using in-house clusterfiles We previously de-termined a gt 998 genotype concordance for DNA isolatedfrom blood and saliva genotyped on the GSA array in 8individuals

Quality Control and ImputationWe used PLINK v197 R v3638 and the IlluminaGenomeStudio for genotype quality control First we ex-cluded all nonoverlapping variants between the 2 differentversions of the GSA chip variants with multicharacter allelecodes insertions deletions duplicated markers and ambig-uous AT and GC variants We determined genotyping sexby the X-chromosome inbreeding coefficients with F lt 02being female and F gt 08 being male and excluded sampleswith discordance between reported and imputed sex Afterthat we filtered first variants and then individuals with a re-laxed threshold for a call rate of less than 85 followed by astringent threshold of 98 We applied a minor allele fre-quency (MAF) filter of 1 as well as filters for significantdeviation from Hardy-Weinberg equilibrium (HWE p lt 1 times10minus6) in controls informative missingness (p lt 1 times 10minus5) andoutlying heterozygosity rate (mean plusmn 3 SDs) To determineduplicated or cryptically related individuals we used pairwisegenome-wide estimates of the proportion of identity by de-scent (IBD) on a pruned data set containing only markers inlow linkage disequilibrium (LD) regions (pairwise r2 lt 02)and excluded those more closely related than third-degreerelatives (IBD gt 0125) Of each identified sample pair wekept the individual with a higher call rate To identify ethnicoutliers we used a procedure similar to the one suggested inthe R package plinkQC9 we combined the genotype datawith the samples of the publicly available 1000 Genomes

GlossaryAAO = age at onset eQTL = expression quantitative trait lociGENERATE = German Network for Research on AutoimmuneEncephalitis GSA = global screening array GT = genotyped GTEx = Genotype-Tissue Expression GWAS = genome-wideassociation study HLA = human leukocyte antigen HSV-1 = herpes simplex virus type 1 (HSV-1) HWE = Hardy-Weinbergequilibrium IBD = identity by descent IM = imputed IgG = immunoglobulin G LD = linkage disequilibrium LDSC = LDscore regression LXRα = liver X receptor alphaMADD = mitogen-activated protein kinase activating death domainMAF =minor allele frequency MAP = mitogen-activated protein NMDAR = NMDA receptor PC = principal component PP =posterior probability SNP = single nucleotide polymorphism TNF-α = tumor necrosis factor alpha TOPmed = Trans-Omicsfor Precision medicine

2 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 6 | November 2021 NeurologyorgNN

Project10 and performed a principal component (PC) analysison the merged data set A European center was determined bythe first 2 PCs of known European samples and the Euclideandistance from this center determined the ethnical assignmentwith samples more than 15 times the maximal EuropeanEuclidean distance away from the center being excluded Theremaining individuals were used for preliminary associationanalysis based on which we visually inspected the cluster plotsof all variants with a p value lt 10ndash4 and discarded variantswithout adequate cluster separation To overcome issues withpopulation stratification wematched controls by ancestry andsex to cases with the R package PCAmatchR11 leading to 590control samples for the analysis and approximately 3 controlsper case An exact match on sex was used because there weresignificantly more female samples in the case samples than inthe control samples

Imputation was performed on the quality-assured data setcontaining 768 individuals (590 controls and 178 cases) and446353 variants Subsequently 26356529 variants wereimputed based on the Trans-Omics for Precision Medicine(TOPmed) r2 panel12 using the TOPMed ImputationServer13 which uses (mini Markov-chain haplotyper 4) forimputation14 A quality check was performed including var-iants with anMAF gt 1 an imputation quality score R2 gt 07and no significant deviation from HWE (p lt 1 times 10minus6) incontrols resulting in 8073349 variants

Association AnalysisWe conducted an association analysis on the whole data setusing a genome-wide significance threshold of p lt 5 times 10minus8We applied an additive logistic regression model includingsex and PCs to estimate the association of each single nu-cleotide polymorphism (SNP) with the disease status Thenumber of PCs was chosen using scree plot analysis15 Pop-ulation stratification was examined using the inflation factor λand the visual inspection of quantile-quantile plots To furtherdistinguish between confounding factors like populationstratification and polygenicity of the anti-NMDAR encepha-litis trait we performed LD score regression (LDSC) usingthe LDHub web interface16 Conditional analyses in which suc-cessively each genome-wide significant variant was included as acovariate were conducted to identify adjacent independent sig-nals We used 7122 genotyped and quality controlled variantsfrom the human major histocompatibility complex region onchromosome 6 to impute four-digit human leukocyte antigen(HLA) alleles using the R package HLA imputation using at-tribute bagging17 It uses attribute bagging to impute genotypesand we chose a prediction model specifically for European an-cestry and the Illumina GSA chip We performed the associationanalysis with python HLA18 using an additive logistic modelincluding sex and the first PC as covariates and adjusted p valueswith the Benjamini-Hochberg false discovery rate step-upmethod

To examine the origin of the variant-trait association signalsmore closely we analyzed the subsamples of patients with

earlylate disease onset (lt or ge25 years) and patients withwithout a tumor To prioritize genes putatively involved in thedisease etiology we investigated the overlap of expressionquantitative trait loci (eQTL) from the Genotype-TissueExpression (GTEx) project19 as well as immune cell eQTLfrom the BLUEPRINT (A BLUEPRINT of haematopoieticepigenomes) project20 and variants in the risk loci identifiedby this GWAS We investigated whether these 2 independentsignals might stem from the same causal variant by colocali-zation analysis conducted with coloc21 Coloc uses approxi-mate Bayes factors to estimate posterior probabilities (PP) forcommon variants causal in the GWAS as well as the eQTLstudy We studied all variants present in the GWAS results aswell as in GTEx V7 for the 13 available brain tissues or presentin the BLUEPRINT immune cell eQTL data within 100 kbup- and downstream of each gene in the 2 encephalitis riskloci Coloc estimates PPs for 4 different scenarios PP4 de-notes the posterior probability that both traitsmdashthe diseaseassociation and the eQTLmdashare caused by the same variant APP4 over 70 was considered as evidence for colocalizationWe used LocusZoom22 and R to visualize the associationresults All analyses and the presentation of the results in thisarticle are based on the human genome version 38 (GRCh38hg38)

Standard Protocol Approvals Registrationsand Patient ConsentsAll participants gave written informed consent Institutionalreview board approval was obtained from the ethical advisoryboards of the Universities of Kiel and Luebeck (B3371313-162)

Data AvailabilitySummary level genetic data for all variants with p values lt 1 times10minus4 are available from the corresponding author on reason-able request to any qualified investigator

ResultsTable 1 summarizes the clinical features of patients andcontrol individuals demonstrating that patients included inour first GWAS4 are comparable to the additional patients inthis study regarding age sex and clinical features Howeverthe control individuals were much older than the patientsGenotype data for 212 individuals with anti-NMDAR en-cephalitis and 1219 controls were available for analysis Afterquality control procedures and control matching 178 casesand 590 healthy controls remained (eTable 2 linkslwwcomNXIA588) Imputation resulted in 8073349 quality-assuredvariants with an MAF gt 1 We incorporated sex and the firstdimension of the PC analysis as indicated by scree plotanalysis as covariates In contrast to our first GWAS of anti-NMDAR encephalitis we did not include age as a covariate(for rationale see Discussion) The genomic inflation factorof λ = 103 indicated a low degree of population stratification(Figure 1A) The LDSC intercept was 101 (standard error =

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001) with 44 of the genomic inflation attributable toconfounding bias including population stratification andcryptic relatedness This indicates that the majority of in-flation is caused by polygenicity We found 13 genetic variantsin 2 distinct loci below the threshold of p = 5 times 10minus8 forgenome-wide significance (Figure 1 B-D Table 2) withleading variants rs10902588 on chromosome 15 (OR = 224[95 CI = 170ndash295] p = 178 times 10minus8) and rs75393320 onchromosome 11 (OR = 220 [166ndash292] p = 378 times 10minus8) aswell as only 14 Kb further downstream rs11039155 with thesame p value and OR Conditional analysis including the topvariants at each locus argues against the presence of any in-dependent secondary signals (eFigure 1 linkslwwcomNXIA586) The significant variants on chromosome 15 are lo-cated in the leucine rich repeat region 1 (LRRK2) gene In thechromosome 11 locus rs75393320 lies in the lysosomal acidphosphatase 2 (ACP2) gene and rs11039155 is located in thenuclear receptor subfamily 1 group H member 3 (NR1H3)gene

Additional analyses of subpopulations defined by tumor statusand age at onset yielded no genome-wide significant associ-ations In the previous GWAS we observed a weak associationbetween patients with anti-NMDAR encephalitis and theHLA-B0702 allele preferentially in the patients with latedisease onset We did not confirm this association in thecurrent GWAS We were not able to analyze the recentlyreported association with HLA-DRB11602 in a Chinesepopulation23 because the frequency of this allele is very low inthe German population and no patient in our sample and only

1 control individual carried this allele We did not detect anynovel significant HLA associations We performed an addi-tional analysis splitting the patient sample into the samplesalready included in our prior GWAS4 and the newly acquiredsamples only (eTable 3 linkslwwcomNXIA589) For allgenome-wide significant variants in the complete sample wefound an identical direction and comparable magnitude of theORs as well as p values between 137 times 10minus7 and 113 times 10minus3

demonstrating that both subsamples contributed to the finalresult As readily apparent in Figure 1C the significant vari-ants on chromosome 11 are located in a gene-rich area withnumerous further variants in high LD with the leading variantand p values less than 1 times 10minus5 Coloc analysis showedcolocalization with a PP4 gt 07 between the sum of GWASvariants and GTEx eQTLs for the 3 genesNR1H3 ACP2 andmitogen-activated protein (MAP) kinase activating deathdomain (MADD) on chromosome 11 in brain tissues(Figure 2) and for the 4 genes NR1H3 ACP2 damage-specific DNA binding protein 2 (DDB2) and chromosome 11open reading frame 49 (C11orf49) in various immune cells(Figure 3) We did not identify any single variant with a PP4gt 07 In contrast we found no colocalizing eQTL signals forthe GWAS signal on chromosome 15

DiscussionExcept for the HLA complex the genetic determinants ofantibody-mediated encephalitides are unknown The collec-tion of sufficiently large sample sizes for genetic analyses is

Table 1 Sample Characteristics

Patients from first GWAS4 New patients in this GWAS Combined patient sample Control individuals

Number 91 87 178 590

Female 824 724 775 707

Mean AAO (cases)age (controls) (plusmnSD) 235 (plusmn133) 295 (plusmn194) 25 (plusmn167) 57 (plusmn146)

Tumor 157 (93 teratoma) 171 (85 teratoma) 164 (89 teratoma) NA

Prodromal symptomsa 383 431 405 NA

Epileptic seizuresa 696 528 629 NA

Psychiatric symptomsa 951 717 858 NA

Movement disordersa 461 264 380 NA

Autonomic dysfunctiona 389 314 358 NA

Abnormal EEGa 696 674 687 NA

Abnormal MRIa 557 420 504 NA

CSF Pleocytosisa 732 714 725 NA

Oligoclonal bandsa 678 692 684 NA

Abbreviations AAO = age at onset GENERATE = German Network for Research on Autoimmune Encephalitis GWAS = genome-wide association study NA =not applicableDescriptive statistics for the overall patient sample for patients from the first GWAS4 for newly recruited patients and for healthy control individualsa Only available for GENERATE samples

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hampered by the low disease prevalence of anti-NMDARencephalitis which is estimated to be around 06100000population2 Despite the small sample size we were able tofind 2 distinct genomic regions on chromosomes 11 and 15

harboring genome-wide significant disease-associated vari-ants We did not identify additional loci containing variantswith suggestive p values very close to genome-wide signifi-cance The locus on chromosome 15 encompasses100000

Figure 1 Association Plots for Anti-NMDAR Encephalitis

(A) Quantile-quantile plot of associationanalysis for 8073349 variants The plotshows deviation from the null distributionin the upper tail which corresponds tovariants with the strongest evidence forassociation (B) Manhattan plot of the as-sociation results The plot shows minuslog10marker-wise p values against their genomicbase pair position The red line indicatesthe genome-wide significance threshold of5 times 10minus8 (C) LocusZoom plot for the asso-ciation between anti-NMDA receptor en-cephalitis and variants on chromosome 11in the genomic region from 466 to 482MbA circle represents a genotyped and a plussymbol an imputed variant The r2 metricdisplays the pairwise LD between theleading and the respective variant Genepositions are present in the bottom part(D) LocusZoom plot for associations onchromosome 15 in the genomic regionfrom 1009 to 1011 Mb

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bp and contains only 1 gene LRRK1 Although genetic vari-ants in the homolog leucine rich repeat region 2 (LRRK2) arethe most common cause of autosomal dominant Parkinsondisease no neurologic diseases are currently linked to LRRK1In mice LRRK1 and LRRK2 complement each other at leastpartially in the nervous system because only deficiency of bothproteins causes a neurodegenerative phenotype and bothproteins regulate autophagy24 LRRK1 is expressed in B cellsand monocytes suggesting a role in the immune system25

Indeed LRRK1-deficient mice show alterations of B-cell de-velopment failure to produce IgG3 class antibodies in re-sponse to nonndashT-cell dependent antigens and a proliferationand survival defect on B-cell receptor stimulation26 Yet thereis currently no known connection between LRRK1 and au-toimmunity However it is intriguing to speculate thatLRRK1-mediated control of nonndashT-cell dependent B-cellactivation could be dysregulated in patients with anti-NMDAR encephalitis Indeed this could explain the obser-vation of frequent nonmutated germ-line encoded NMDARantibodies in patients27 the childhood and early adult mani-festation and the coexistence of additional autoantibodies28

The borders of the genomic region on chromosome 11harboring the second association signal are less defined Theregion is much larger exceeding 1 Mb and comprisesmultiple genes To generate a hypothesis concerning pu-tatively causal genes in this region we used colocalizationanalysis between eQTL data from GTEx for different brainregions as well as immune cell eQTL from the BLUEPRINTproject In brain tissues we found evidence for

colocalization between the genes ACP2 and MADD witheQTL for cerebellum and NR1H3 with eQTL for the hy-pothalamus Although it is well known that the hippocam-pus is a prime target of anti-NMDAR encephalitis theubiquitous expression of NMDA receptors containing theGluN1 subunit in the brain the manifold symptoms of anti-NMDAR encephalitis and pathologic studies suggest aninvolvement of most if not all brain regions29 Therefore wethink that the cerebellum and hypothalamus are valid targetregions In immune cells we detected colocalization of thegenes NR1H3 ACP2 DDB2 and C11orf49 with eQTL invarious immune cells including T-lymphocytesNR1H3 andACP2 show evidence for colocalization in both brain andimmune cells Unfortunately B-lymphocytesplasma cellsthe producers of antibodies are not represented in theBLUEPRINT data Of the genes identified in the colocali-zation analysis NR1H3 encoding the liver X receptor alpha(LXRα) is the best functional candidate LXRα is a tran-scription factor whose activation inhibits inflammatoryprocesses30 In the CNS LXRα agonists inhibit proin-flammatory cytokine production by microglia and astro-cytes31 Knockout of LXRα in brain endothelial cells led toblood-brain barrier dysfunction inflammation and in-creased transendothelial mononuclear cell migration32

ACP2 is a lysosomal acid phosphatase used in lysosomalprotein degradation MADD is an adaptor protein involvedin transmitting tumor necrosis factor alpha (TNF-α)-in-duced apoptotic signals DDB2 is involved in DNA repaireg after ultraviolet light damage and C11orf49 encodes aprotein of unknown function

Table 2 Identified Associations With a p Value lt 5 times 10minus8

CHR BP [GRCh38] dbSNP ID MAF affected MAF control OR (95 CI) p Value IMGT Gene

15 100978492 rs10902588 033 018 224 (170ndash295) 118e-8 IM LRRK1

15 100985970 rs2412001 034 019 218 (166ndash285) 139e-8 IM LRRK1

15 100996156 rs4995826 038 022 214 (164ndash278) 153e-8 IM LRRK1

15 100996211 rs4352030 038 022 214 (164ndash278) 153e-8 IM LRRK1

15 100996820 rs966292 038 022 214 (164ndash278) 153e-8 IM LRRK1

15 100998427 rs66793839 038 022 214 (164ndash278) 153e-8 IM LRRK1

15 100986608 rs11636885 032 018 219 (166ndash289) 266e-8 IM LRRK1

15 100996725 rs966293 032 018 219 (166ndash289) 287e-8 GT LRRK1

15 101000513 rs55785108 032 018 219 (166ndash289) 287e-8 IM LRRK1

15 100980449 rs12442816 032 018 217 (165ndash286) 360e-8 IM LRRK1

11 47244920 rs75393320 029 016 220 (166-292) 378e-8 IM ACP2

11 47259211 rs11039155 029 016 220 (166-292) 378e-8 IM NR1H3

15 101003755 rs55759655 034 019 212 (162ndash278) 396e-8 IM LRRK1

Abbreviations BP = base-pair gene dbSNP = database of single nucleotide polymorphisms GT = genotyped IM = imputed MAF= minor allele frequencyThe top-SNPs at each locus are highlighted in bold with rs75393320 and rs11039155 on chromosome 11 having the same p value

6 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 6 | November 2021 NeurologyorgNN

In contrast to our first GWAS of antibody-mediated en-cephalitis we identified 2 independent genome-wide sig-nificant associations in this study There are 3 importantdifferences between our previous GWAS and the currentone First doubling of the sample size led to larger statisticalpower second we carefully removed population outliers

and third we chose a different set of covariates in the logisticregression model In contrast to the first GWAS we includedonly sex and the first PC in the current analysis Scree plotanalysis suggested using only 1 PC which might in part bedue to the stringent exclusion of ethnic outliers and carefulcontrol matching Another difference to the first GWAS is

Figure 2 Colocalization Results for Brain Tissues

Gene- and SNP-wise results of the colocalization analysis for brain tissues represented in Genotype-Tissue Expression types Only genes with a PP4 gt 07 andvariants with a p value lt 10minus5 are shown ACP2 = acid phosphatase 2 lysosomal GWAS = genome-wide association study MADD =mitogen-activated proteinkinase activating death domain NR1H3 = nuclear receptor subfamily 1 group H member 3

NeurologyorgNN Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 6 | November 2021 7

the exclusion of age as a covariate Genetic variants are stablethroughout life For common late-onset diseases signifi-cantly younger controls than patients warrant inclusion of

age as a covariate because many controls will still develop thedisease during their lifetime However in this study thedisease is rare and the controls are significantly older than

Figure 3 Colocalization Results for Immune Cells

Gene- and SNP-wise results of the colocalization for immune cells represented in the BLUEPRINT data set Only genes with a PP4 gt 07 and variants with a pvalue lt 10minus5 are shown ACP2 = acid phosphatase 2 lysosomal C11orf49 = chromosome 11 open reading frame 49 DDB2 = damage-specific DNA bindingprotein 2 GWAS = genome-wide association study NR1H3 = nuclear receptor subfamily 1 group H member 3

8 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 6 | November 2021 NeurologyorgNN

the patients Including age as a covariate led to partialmasking of the signals contributing to the effects in thisGWAS The major shortcoming of this study is its smallsample size which on the one hand limits the power todetect true variant-disease associations and on the otherhand did not allow to include an independent replicationsample thereby increasing the likelihood of false positivesIn our opinion increasing the sample size by internationalcooperation as well as locus fine-mapping by sequencing andanalysis of multiethnic samples will be key in future studiesThe history of GWAS has shown that in most diseases in-creasing sample size was more important than more detailedphenotyping This might be particularly true in antibody-mediated encephalitis because the antigen specificity itselfleads to a rather homogenous biologic disease entity com-pared with most other neurologic diseases eg polygenicneurodegenerative disorders In summary we performed aGWAS of anti-NMDA receptor encephalitis and identified 2independent genome-wide significant association signalsBoth genomic regions contain putative functional candidategenes In addition eQTL for 5 genes show significantcolocalization with the association signal on chromo-some 11

AcknowledgmentThe work was supported by members of the GENERATEnetwork who contributed to patient recruitment dataacquisition and entry All members of the GENERATEnetwork as ofMarch 2021 are indicated in eTable 1 linkslwwcomNXIA587 Patient recruitment in the Czech Republicwas supported by the Charles University project GA UK No746120 Petr Marusic (Department of Neurology CharlesUniversity Second Faculty ofMedicine andMotol UniversityHospital Prague Czech Republic) helped with patientselection and clinical data collection for the Czech partici-pants The popgen 20 network (P2N controls) is supportedby the Medical Faculty of the University of Kiel TheGenotype-Tissue Expression (GTEx) Project was supportedby the Common Fund of the Office of the Director of theNational Institutes of Health and by NCI NHGRI NHLBINIDA NIMH and NINDS The data used for the analysesdescribed in this article were obtained from dbGaP accessionnumber phs000424v7p2

Study FundingThis work was in part funded by the Federal Ministry of Ed-ucation andResearch (BMBF) through a grant to FL andGKwithin the scope of the project CONNECT-GENERATEgrant code 01GM1908A

DisclosureThe authors report no disclosures relevant to the manuscriptGo to NeurologyorgNN for full disclosures

Publication HistoryReceived by Neurology Neuroimmunology amp NeuroinflammationMarch 16 2021 Accepted in final form July 9 2021

Appendix 1 Authors

Name Location Contribution

Anja K TietzMSc

Kiel University Kiel Germany Acquisition and analysisof the data drafted themanuscript andrevised the finalmanuscript

KlemensAngstwurm MD

University HospitalRegensburg RegensburgGermany

Major role in theacquisition of data andrevised the finalmanuscript

TobiasBaumgartnerMD

University Hospital BonnBonn Germany

Major role in theacquisition of data andrevised the finalmanuscript

KathrinDoppler MD

University Hospital WurzburgWurzburg Germany

Major role in theacquisition of data andrevised the finalmanuscript

KatharinaEisenhut MD

Biomedical Center andUniversity Hospital LudwigMaximilians UniversityMunich Germany

Major role in theacquisition of data andrevised the finalmanuscript

Martin ElisakMD

Charles University and MotolUniversity Hospital PragueCzech Republic

Major role in theacquisition of data andrevised the finalmanuscript

Andre FrankePhD

Kiel University Kiel Germany Major role in theacquisition of data andrevised the finalmanuscript

Kristin SGolombeck MD

University Hospital MunsterMunster Germany

Major role in theacquisition of data andrevised the finalmanuscript

RobertHandreka

Carl-Thiem-Klinikum CottbusCottbus Germany

Major role in theacquisition of data andrevised the finalmanuscript

Max KaufmannMD

University Medical CenterHamburg‐EppendorfHamburg Germany

Major role in theacquisition of data andrevised the finalmanuscript

MarkusKraemer MD

Alfried Krupp Hospital Essenand Heinrich-Heine UniversityDusseldorf DusseldorfGermany

Major role in theacquisition of data andrevised the finalmanuscript

Andrea KraftMD

Martha-Maria Hospital HalleHalle Germany

Major role in theacquisition of data andrevised the finalmanuscript

Jan LewerenzMD

University of Ulm UlmGermany

Major role in theacquisition of data andrevised the finalmanuscript

Wolfgang LiebMD

Kiel University Kiel Germany Major role in theacquisition of data andrevised the finalmanuscript

MarieMadlener MD

University Hospital CologneCologne Germany

Major role in theacquisition of data andrevised the finalmanuscript

Continued

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References1 Dalmau J Armangue T Planaguma J et al An update on anti-NMDA receptor

encephalitis for neurologists and psychiatrists mechanisms and models LancetNeurol 201918(11)1045-1057

2 Dubey D Pittock SJ Kelly CR et al Autoimmune encephalitis epidemiology and acomparison to infectious encephalitis Ann Neurol 201883(1)166-177

3 Graus F Titulaer MJ Balu R et al A clinical approach to diagnosis of autoimmuneencephalitis Lancet Neurol 201615(4)391-404

4 Mueller SH Farber A Pruss H et al Genetic predisposition in anti-LGI1 and anti-NMDA receptor encephalitis Ann Neurol 201883(4)863-869

5 Nothlings U Krawczak M PopGen A population-based biobank with prospectivefollow-up of a control group Bundesgesundheitsblatt Gesundheitsforschung Gesund-heitsschutz 201255(6-7)831-835

6 Krawczak M Nikolaus S von Eberstein H Croucher PJ El Mokhtari NE Schreiber SPopGen population-based recruitment of patients and controls for the analysis ofcomplex genotype-phenotype relationships Community Genet 20069(1)55-61

7 Purcell S Neale B Todd-BrownK et al PLINK a tool set for whole-genome associationand population-based linkage analyses Am J Hum Genet 200781(3)559-575

8 R Core Team R A Language and Environment for Statistical Computing R Foundationfor Statistical Computing 2020

9 Meyer HV plinkQC Genotype Quality Control in Genetic Association Studies 202010 1000 Genomes Project Consortium A global reference for human genetic variation

Nature 2015526(7571)6811 Brown DW Myers TA Machiela MJ PCAmatchR a flexible R package for optimal

case-control matching using weighted principal components Bioinformatics 202012 Taliun D Harris DN Kessler MD et al Sequencing of 53831 diverse genomes from

the NHLBI TOPMed Program Nature 2021590(7845)290-29913 Das S Forer L Schonherr S et al Next-generation genotype imputation service and

methods Nat Genet 201648(10)1284-128714 Fuchsberger C Abecasis GR Hinds DA minimac2 faster genotype imputation

Bioinformatics 201531(5)782-78415 Cattell RB The scree test for the number of factors Multivariate Behav Res 1966

1(2)245-27616 Zheng J Erzurumluoglu AM Elsworth BL et al LD Hub a centralized database and

web interface to perform LD score regression that maximizes the potential of sum-mary level GWAS data for SNP heritability and genetic correlation analysis Bio-informatics 201733(2)272-279

17 Zheng X Shen J Cox C et al HIBAGndashHLA genotype imputation with attributebagging Pharmacogenomics J 201414(2)192-200

18 Fan Y Song YQ PyHLA tests for the association between HLA alleles and diseasesBMC Bioinformatics 201718(1)90-95

19 GTEx Consortium Genetic effects on gene expression across human tissues Nature2017550(7675)204

20 Chen L Ge B Casale FP et al Genetic drivers of epigenetic and transcriptionalvariation in human immune cells Cell 2016167(5)1398e24-1414e24

21 Giambartolomei C Vukcevic D Schadt EE et al Bayesian test for colocalisationbetween pairs of genetic association studies using summary statistics Plos Genet 201410(5)e1004383

22 Pruim RJ Welch RP Sanna S et al LocusZoom regional visualization of genome-wide association scan results Bioinformatics 201026(18)2336-2337

23 Shu YQ Qiu W Zheng JF et al HLA class II allele DRB11602 is associated withanti-NMDAR encephalitis J Neurol Neurosur Psychiatry 201990(6)652-658

24 Giaime E Tong Y Wagner LK Yuan Y Huang G Shen J Age-dependent dopami-nergic neurodegeneration and impairment of the autophagy-lysosomal pathway inLRRK-deficient mice Neuron 201796(4)796-e6

25 Thevenet J Pescini Gobert R Hooft van Huijsduijnen R Wiessner C Sagot YJRegulation of LRRK2 expression points to a functional role in human monocytematuration PLoS One 20116(6)e21519

26 Morimoto K Baba Y Shinohara H et al LRRK1 is critical in the regulation of B-cellresponses andCARMA1-dependent NF-κB activation Sci Rep 20166(1)25738-25813

27 Wenke NK Kreye J Andrzejak E et al N-methyl-D-aspartate receptor dysfunction byunmutated human antibodies against the NR1 subunit Ann Neurol 201985(5)771-776

28 Martinez-Hernandez E Guasp M Garcia-Serra A et al Clinical significance of anti-NMDAR concurrent with glial or neuronal surface antibodies Neurology 202094(22)e2302-e2310

29 Hirano M Itoh T Fujimura H et al Pathological findings in male patients with anti-N-methyl-D-Aspartate receptor encephalitis J Neuropathol Exp Neurol 201978(8)735-741

30 Zhao L Lei W Deng C et al The roles of liver X receptor α in inflammation andinflammation‐associated diseases J Cell Physiol 2020

31 Zhang-Gandhi CX Drew PD Liver X receptor and retinoid X receptor agonistsinhibit inflammatory responses of microglia and astrocytes J Neuroimmunol 2007183(1-2)50-59

32 Wouters E de Wit NM Vanmol J et al Liver X receptor alpha is important inmaintaining blood-brain barrier function Front Immunol 2019101811

Appendix 1 (continued)

Name Location Contribution

NicoMelzer MD Heinrich-Heine UniversityDusseldorf DusseldorfGermany

Major role in theacquisition of data andrevised the finalmanuscript

HanaMojzisova MD

Charles University and MotolUniversity Hospital PragueCzech Republic

Major role in theacquisition of data andrevised the finalmanuscript

Peter MollerMD

Klinikum Weimar WeimarGermany

Major role in theacquisition of data andrevised the finalmanuscript

ThomasPfefferkorn MD

Klinikum IngolstadtIngolstadt Germany

Major role in theacquisition of data andrevised the finalmanuscript

Harald PrussMD

CharitemdashUniversitatsmedizinBerlin and German Centerfor NeurodegenerativeDiseases (DZNE) BerlinBerlin Germany

Major role in theacquisition of data andrevised the finalmanuscript

Kevin RostasyMD

Childrenrsquos Hospital DattelnWittenHerdecke UniversityDatteln Germany

Major role in theacquisition of data andrevised the finalmanuscript

MargretSchnegelsbergMD

Asklepios HospitalsSchildautal Seesen Germany

Major role in theacquisition of data andrevised the finalmanuscript

Ina Schroder University Hospital Schleswig-Holstein KielLubeckGermany

Major role in theacquisition of data andrevised the finalmanuscript

KaiSiebenbrodtMD

Unversity Hospital andGoethe Universiy FrankfurtFrankfurt am Main Germany

Major role in theacquisition of data andrevised the finalmanuscript

Kurt-WolframSuhs MD

Hannover Medical SchoolHannover Germany

Major role in theacquisition of data andrevised the finalmanuscript

JonathanWickel MD

University Hospital Jena JenaGermany

Major role in theacquisition of data andrevised the finalmanuscript

Klaus-PeterWandinger MD

University Hospital Schleswig-Holstein KielLubeckGermany

Major role in theacquisition of dataand revised the finalmanuscript

Frank LeypoldtMD

University Hospital Schleswig-Holstein KielLubeck and KielUniversity Kiel Germany

Conceptualized thestudy acquisitionand interpretationof the data andrevised the finalmanuscript

GregorKuhlenbaumerMD PhD

Kiel University Kiel Germany Conceptualized thestudy drafted themanuscript analysisand interpretationof the data andrevised the finalmanuscript

Appendix 2 Coinvestigators

Coinvestigators are listed in Appendix 2 at linkslwwcomNXIA587

10 Neurology Neuroimmunology amp Neuroinflammation | Volume 8 Number 6 | November 2021 NeurologyorgNN

DOI 101212NXI000000000000108520218 Neurol Neuroimmunol Neuroinflamm

Anja K Tietz Klemens Angstwurm Tobias Baumgartner et al Encephalitis

Genome-wide Association Study Identifies 2 New Loci Associated With Anti-NMDAR

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