Genetic overlap between Alzheimer’s disease and immune-mediated diseases: An atlas of shared genetic determinants and biological convergence

The occurrence of immune disease comorbidities in Alzheimer’s disease (AD) has been observed in both epidemiological and molecular studies, suggesting a neuroinflammatory basis in AD. However, their shared genetic components have not been systematically studied. Here, we composed an atlas of the shared genetic associations between 11 immune-mediated diseases and AD by analyzing genome-wide association studies (GWAS) summary statistics. Our results unveiled a significant genetic overlap between AD and 11 individual immune-mediated diseases despite negligible genetic correlations, suggesting a complex shared genetic architecture distributed across the genome. The shared loci between AD and immune-mediated diseases implicated several genes, including GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, all of which are protein-coding genes and thus potential drug targets. Top biological pathways enriched with these identified shared genes were related to the immune system and cell adhesion. In addition, in silico single-cell analyses showed enrichment of immune and brain cells, including neurons and microglia. In summary, our results suggest a genetic relationship between AD and the 11 immune-mediated diseases, pinpointing the existence of a shared however non-causal genetic basis. These identified protein-coding genes have the potential to serve as a novel path to therapeutic interventions for both AD and immune-mediated diseases and their comorbidities.


Introduction
Alzheimer's disease (AD) is a neurodegenerative brain disease that affects cognition, behavior and social skills 1 .Twin studies have shown AD to be a highly heritable disease 2 .As the older population is increasing, AD is becoming an emerging global public health challenge in our century, with an estimated 6.2 million Americans aged 65 and older living with AD.This number is projected to increase to 13.8 million by 2060 1 .An increasing body of evidence indicates that the pathogenesis of AD is not con ned to the neurons but instead that neuroin ammation plays a signi cant role in the disease, with an interplay between the brain and the immune system 3 .In particular, the microglia, the brain-resident immune cells that contribute to the regulation of the brain immune response, is considered to have a pivotal role in the pathophysiology of AD, as a core alteration in AD 4,5 or at least as a particular endophenotype in what is thought to be a heterogeneous disease 6 .Other studies pinpoint to alterations in peripheral immune cells in AD and a role for innate immune cells in the development of AD 7,8 .Chronic in ammation, which is often associated with immune-mediated diseases, has been implicated in the development and progression of AD.The comorbidities between immune diseases, showing immune system dysregulation, and AD have been widely reported.Epidemiological studies show that the overall incidence of AD is higher among individuals with in ammatory bowel disease, other in ammatory polyarthropathies and systematic connective tissue disorders, psoriasis, rheumatoid arthritis (RA), or multiple sclerosis compared to the age and sex-matched comparison groups free from in ammatory diseases [9][10][11] .A previous study investigated the pleiotropy between immune-mediated diseases and AD 12 .However, it is still unclear to what extent a multitude of immune-mediated diseases and AD share genetic underpinnings, and now larger and more powerful GWAS data are available.
In this study, we aimed to comprehensively investigate the genetic relationship, if any, between immune-mediated diseases and AD.We hypothesized that the genetic determinants contributing to AD overlap with those contributing to these immune-mediated diseases.To achieve this goal, we probed the shared genetic architecture between AD and a plethora of immune-mediated diseases.Speci cally, we collected the genome-wide association studies (GWAS) summary statistics data from AD and 11 immune-mediated diseases: allergic rhinitis (AR), asthma, atopic dermatitis (AtD), celiac disease (CeD), Crohn's disease (CD), hypothyroidism, primary sclerosing cholangitis (PSC), RA, systemic lupus erythematosus (SLE), ulcerative colitis (UC), and vitiligo.We identi ed the genes linked to the associated single nucleotide polymorphisms (SNPs) for each of the 11 immune-mediated diseases and AD.Moreover, we characterized the shared loci and their implications on genes, tissues, cell types, and biological functions between immune-mediated diseases and AD.Lastly, we ascertained their possible causal relationship, shedding light on the intricate interplay between these immune-mediated diseases and AD.

Linkage disequilibrium score regression between AD and immune-mediated diseases
We calculated the genetic correlation between AD and 11 immune-mediated diseases using the cross-trait linkage disequilibrium score regression (LDSR) method 23 .Pre-estimated linkage disequilibrium (LD) scores provided by LDSC developers were obtained from the 1,000 Genomes Project (1kGP) European reference population; we preprocessed the summary statistics using LDSC munge_sumstats.pyand used the odds ratio (OR) as the signed summary statistic.
We calculated the genetic correlation by employing HapMap3 SNPs only with LD reference panel SNPs to minimize potential bias due to differences in LD structure.

Genetic overlap between AD and immune-mediated diseases
To complement the genetic correlation analysis, we used the statistical tool MiXeR (https://github.com/precimed/mixer). MiXeR quanti es polygenic overlap irrespective of genetic correlation using GWAS summary statistics, and is agnostic to effect directions 24 .This method estimates the total number of both shared and trait-speci c causal variants (i.e., variants with nonzero additive genetic effects on a given trait).We applied MiXeR to verify genetic overlap between AD and each immune-mediated disease.To evaluate model t (the ability of the MiXeR model to predict the actual GWAS data), we constructed modeled vs. actual conditional quantile-quantile (Q-Q) plots, log-likelihood plots, and calculated the Akaike information criterion (AIC).A negative AIC value indicates that the MiXeR model cannot be adequately differentiated from a scenario of maximum possible overlap and a scenario of minimum overlap, while a positive AIC value indicates adequate t for the MiXeR analyses.The major histocompatibility complex (MHC) region was excluded from the MiXeR analyses for all the considered traits.
Conditional Q-Q plots to assess pleiotropic enrichment We created conditional Q-Q plots to assess the cross-trait genetic enrichment, conditioning AD on the 11 immune-mediated diseases and one negative control, and we did this analysis inversely as well.The genetic enrichment can be referred to as a leftward shift in the Q-Q curve, indicating a considerable number of SNPs with their p-values greater than or equal to a given threshold.Q-Q plot is used to visualize the quantile distribution of the statistics for the observed result with respect to the expected quantile distribution under the null hypothesis, where the theoretical distribution is uniform on the interval [0,1].In this case, enrichment is considered to occur when an increased proportion of SNPs associated with one phenotype or disease (AD) is observed as a function of the strength of the association with a second phenotype or disease, and enrichment is considered to occur in case the degree of the shift of the Q-Q curve to the left from the expected null line for the rst disease is dependent on the magnitude of the association with the second disease 25 .

Conditional and conjunctional false discovery rate analysis
Conditional false discovery rate (CondFDR) anlaysis was used to detect SNPs associated with AD given their association with each immune-mediated disease, and vice versa.CondFDR is de ned as "the posterior probability that a given SNP is null (i.e., no SNPs associated with the rst trait) for the primary trait (i.e., AD) given that the p-values for both traits are as small or smaller than the observed p-values" 25 .ConjFDR is de ned as "the posterior probability that a given SNP is null for either phenotype or both simultaneously, given the p-values for both traits are as small or smaller than the observed p-values" 25,26 .The enrichment veri ed in the conditional Q-Q plots is shown for each individual SNP according to the FDR.In this project, we used the conjFDR analysis with the threshold value 0.05 to identify the shared loci between AD and each of the 11 immune-mediated diseases.To examine the chromosomal location of SNPs in combination with AD and these immune-mediated diseases, we constructed a Manhattan plot using the bioinfokit package 27 .

Local genetic correlation analysis to assess local genetic correlation
We used Local Analysis of covariant Association (LAVA) to further calculate the local genetic correlation between AD and immune-mediated diseases across the pleiotropic SNPs identi ed from conjFDR analysis.Using the pre-de ned 2,495 genomic regions (2,492 after excluding the APOE region) provided by the LAVA developers, which minimize the local LD in those blocks 28 , the pleiotropic SNPs from conjFDR results were mapped and grouped into LAVA genomic regions.The genomic partition was performed using the 1kGP European individuals.To identify the presence of genetic signals, we initially performed a univariate test to estimate local heritability across each phenotype with a p-value < 0.05 being considered as a signi cant locus to evaluate for the bivariate local genetic correlation.For the loci with signi cant heritability in both disorders, we calculated the bivariate local genetic correlation.We adjusted the pvalues using Bonferroni correction between AD and each immune-mediated disease based on the number of genomic loci tested in local univariate and bivariate analyses.

Enrichment and assessment of SNP novelty
SNPs within the MHC region (de ned as chr6: 25,119,106 − 33,854,733), the 8p23 inversion (chr8:7,200,000-12,500,000), and apolipoprotein E (APOE) gene (chr19:44,000,000-47,000,000) were excluded from the analyses due to known association to AD 29,30 or a very complex LD structure 31,32 .For the conjFDR analysis, we used a conjFDR p-value < 0.05 as statistically signi cant.A GWAS p-value (AD & ID) > 5 x 10 − 8 cutoff in the original GWAS was used to identify the shared SNPs between two phenotypes as novel.In other words, to be considered 'novel', a SNP identi ed using the conjFDR should not have been found statistically signi cant in the original GWAS.For the local genetic correlation, we used a p-value < 0.05 as cutoff in the univariate and bivariate test with Bonferroni corrected p-value for AD and each immune-mediated disease.To check the novelty of the SNPs and loci identi ed in our study, we examined previously reported GWAS associations in the National Human Genome Research Institute (NHGRI)-EBI GWAS Catalog 33 .We further identi ed if the gene was novel to any of our traits based on the traits reported in the NHGRI-EBI GWAS Catalog 33 and previous AD studies based on conjFDR analysis 25,[34][35][36] .

De nition of genomic loci
Independent genomic loci were identi ed based on the FUMA protocol, an online tool for functional mapping of genetic variants (http://fuma.ctglab.nl/) 37.SNPs with conjFDR < 0.1 were identi ed as independently signi cant and LD r 2 < 0.6 with each other.A subset of these independent SNPs (LD r 2 < 0.1) was then considered as the lead SNPs.The boundaries for genomic loci were then determined by identifying all candidate SNPs which were in LD (r 2 ≥ 0.6) with the lead SNP.Then, loci were combined if they were separated by less than 250 kb.These separate regions, containing all of these candidate SNPs, were treated as a single independent genomic locus.All LD information was calculated based on the EA reference panel from 1kGP 38 .

Functional annotation and tissue and cell-speci city analyses
We annotated the genes to the lead SNPs based on positional mapping using ANNOtate VARiation (ANNOVAR) 39 in FUMA.We mapped all the candidate SNPs with conjFDR value < 0.1 and having an LD r 2 ≥ 0.6 with one of the independently signi cant SNPs using ANNOVAR.In addition, these SNPs were annotated with Combined Annotation Dependent Depletion (CADD) scores 40 to predict how certain the SNP effect is on protein structure or function and possible contribution to genetic disease.Similarly, we used RegulomeDB (RDB version 1.1) scores 41 to predict the likelihood of regulatory functionality, and chromatin states, to predict transcription and also regulatory effects from chromatin states at the SNP locus.RegulomeDB (version 1.1) is a database that scores SNP's functionality based upon its presence in a DNase hypersensitive site or transcription factor binding site.RDB ranks the SNPs with a score from 1 to 6.The SNP with strong evidence of being a regulatory variant is given the score of 1 and one with the least evidence is scored as 6 41 .SNPs with RDB scores of 1 (1a − 1f) were queried for known eQTLs in brain (GTEx v8, Braineac, CMC, PsychENCODE, xQTLServer), blood (GTEx v8, Blood eQTL, BIOS QTL, eQTLGen,) and immune system tissues (GTEx v8, DICE, scRNA eQTLs) along with other lead SNPs.An RDB score of 1 suggests the presence of the variant in multiple data regions such as combination of eQTL + TF binding + matched TF motif + matched DNase Footprint + DNase peak (for more details about the RDB score, see https://regulomedb.org/regulome-help/).
In addition, we ranked the tissues implicated by the SNPs using our in-house method DeepFun 42 .The DeepFun web server (https://bioinfo.uth.edu/deepfun/) is a convolutional neural network framework that was trained on ~ 8,000 chromatin pro les of 225 tissues or cell types from ENCODE and Roadmap projects.We imported all independent SNPs shared between AD and immune-mediated diseases to query the pretrained model on the web server and compute the SNP Activity Difference (SAD, ranging from − 1 to 1) between SNP alleles, indicating the accessibility or binding probability difference between reference and alternative alleles.We took the mean of absolute SAD of each SNP for each of the 50 unique tissues and cell lines and counted the number of tissues or cell types with the largest absolute SAD for each of the considered SNPs.The tissue and cell lines with most counts were de ned as the most related tissue and cell lines to those independent SNPs shared between AD and immune-mediated diseases.We used all the shared SNPs between AD and immune-mediated diseases as the imputed SNPs.Then, we used Web-based Cell-type Speci c Enrichment Analysis of Genes (WebCSEA, available at https://bioinfo.uth.edu/webcsea/) 43to assess the tissue and cell type enrichment of all the shared SNPs.WebCSEA curated a total of 111 single cell RNA-seq (scRNA-seq) panels of human tissues and 1,355 tissue-cell types (TCs) from 61 different general tissues across 11 human organ systems and uses decoding tissue-speci city (deTS) algorithm to measure the enrichment for each tissue-cell type 43 .In the WebCSEA analyses, a nominal p-value < 1 x 10 − 3 was considered statistically signi cant.
Finally, we used FUMA to analyse the Gene Ontology (GO) terms in three domains (biological processes, molecular functions, and cellular components terms) and biological pathways from KEGG (Kyoto encyclopedia of genes and genomes), Panther BD, Reactome, and Wikipathways.An FDR corrected by Benjamini and Hochberg (BH) procedure 44 < 0.05 was considered signi cant for the GO and pathway analyses.

Causal relationship between AD and immune-mediated diseases
We evaluated the potential causal relationship between AD and immune-mediated diseases using Mendelian randomization (MR) analysis.MR uses SNPs as instrument variables (IV) to assess causality and these IVs are de ned by three assumptions 45 .First, the selected IVs are signi cantly associated (p-value GWAS < 5 x 10 − 8 ) with the exposure variable.Second, the IVs are independent between the exposure and the outcome.Third, the effect of IVs on the outcome must precede through the exposure.We used the two-sample MR (2SMR) method (https://mrcieu.github.io/TwoSampleMR/articles/introduction.html).Initially, independent (r 2 < 0.001) genome-wide signi cant SNPs (p-value GWAS < 5 × 10 − 8 ) associated with the exposure (immune-mediated diseases) were considered as IVs and assessed against outcome variables (AD).We used the ve MR methods [MR Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode] implemented in the 2SMR R package 46 .

Results
The general work ow of our study is illustrated in Fig. 1.We developed a pipeline to perform the global and local genetic correlations between AD and 11 immune-mediated diseases and to quantify the shared loci among them using MiXeR.We used the conditional/comjunctional FDR to identify the shared loci and, nally, performed bi-directional MR to assess the causal relationship between AD and the 11 immune-mediated diseases.
To capture mixtures of effect directions across shared genetic variants, we performed MiXeR analysis with AD and immune-mediated diseases to verify genetic overlap beyond genetic correlation by determining the number of overlapping variants between trait pairs.The observed small genetic overlap in the Venn diagrams with nonexistent genetic correlations suggests a balanced mixture of concordant and discordant genetic effects across shared loci between AD and immune-mediated diseases, with adequate quality of model t as suggested by positive AIC values.The predicted number of shared causal loci between AD and immune-mediated diseases varied from 6 (between AD and PSC; AD and SLE) to 40 (AD and AR).There was complete genetic overlap between AD and the negative control HCM.However, the AIC values were all negative, indicating a poor model t and, thus, this particular analysis is unreliable (Fig. 2, Figure S1, and Table S3).

Pleiotropic enrichment
The enrichment observed in QQ-plots can be translated to FDR for each SNP.The genetic enrichment can be identi ed from the Q-Q plot with a leftward shift in the Q-Q curve, indicating a considerable number of SNPs with p-value greater than or equal to a given threshold.There was an enrichment of associations with AD given increasing SNP associations with each immune-mediated disease and conversely in the conditional Q-Q-plots, with a leftward shift of decreasing values of empirical − log10(q AD ), showing polygenic overlap between AD and each immune-mediated disease across common genetic variants.No enrichment was veri ed between AD and the negative control HCM (Fig. 3, Figure S1).

Shared and novel loci for AD and immune-mediated diseases
We employed conjFDR to ascertain SNPs mutually associated with AD and each immune-mediated disease; in order to determine the allelic direction of effects in the diseases, we used the z-scores from the original GWAS.Considering a conjFDR < 0.05, we identi ed 76 shared genomic loci (Table S4), of which 6 loci were jointly associated with more than one pair of diseases.Five of the 6 multi-trait shared loci were identi ed as novel loci for AD (Table S5).After excluding these 6 overlapping SNPs, we identi ed 70 unique shared genomic loci (71 unique SNPs) between AD and immune-mediated diseases, out of which 38 genomic loci were novel for AD only and 25 genomic loci were novel for immune-mediated diseases only (Tables 1 and S4); 17 genomic loci were identi ed as novel to both AD and immune-mediated diseases (Table 2).We then constructed conjFDR Manhattan plots to visualize all the novel and all shared loci between these diseases (Figure S2), and, based on the z-scores, we evaluated the directionality of allelic effects in the loci shared between AD and the immune-mediated diseases.All the shared loci had a mixed direction of effect, except for AtD and AD, which had complete concordance effect (Tables 1 and  S4).The shared loci (1q23.3,7p15.1, 22q13.2,1q32.1, 4p14, 6q23.3) and their respective genes (ADAMTS4, JAZF1, Y_RNA, C1orf106, AC195454.1,and RP11-95M15.1) were mapped to more than one immune-mediated disease.No shared loci were identi ed between AD and the negative control HCM (Figures S2 and  4 and Table S5).Local genetic correlation analysis across the LAVA prede ned genomic loci and conjFDR results Regional genetic correlations may be masked when r g is assessed on a genome-wide level.To investigate whether there are any genomic loci with pronounced genetic correlations despite negligible genome-wide r g, we estimated regional genetic correlations using LAVA.Regional genetic correlations are a better tool to capture genetic associations with mixed effect directions.That is, a pair of traits may exhibit no global genetic correlations as a result of an equal number of positive and negative (opposite effect directions) local genetic correlations of similar magnitude.We initially performed LAVA on the pre-de ned 2,492 genmic loci to calculate the pairwise local genetic correlation across AD and 11 immune-mediated diseases.We used a nominal p-value < 0.05 to detect the univariate signals and used the Bonferroni corrected p-value between AD and each of the 11 immune-mediated diseases for bivariate analysis.The LAVA results were summarized in Table S6.The overall average concordant effect in univariate analysis was 7%, and 46% of these loci had 95% con dence intervals (CIs) for the variance that included 1.The overall average concordant effect in bivariate analysis was 53%, and 81% of these loci had 95% CIs for the variance that included 1.The overall average concordant effect in Bonferroni adjusted bivariate analysis was 56%, and 87% of these loci had 95% CIs for the variance that included 1. Overall 1,765 unique loci found to univariately heritable (Table S8) and 745 unique loci found signi cant for bivariate analysis at p-value < 0.05 for AD and 11 immune-mediated diseases (Table S9) and 220 unique loci are shared between more than one immune-mediated disease with AD (Table S10).
After adjusting for Bonferroni correction between AD and each of the 11 immune-mediated disease, we found 39 genomic loci shared between AD and 11 immune-mediated diseases (Table S11).
Based on the conjFDR analysis results, we further performed local genetic correlation using LAVA on the 17 novel and 59 previously reported shared loci (6 of these loci are shared between multiple pair of traits, see Table S5) (Tables S4 and S7) identi ed between AD and immune-mediated diseases.Among the 17 novel loci, 11 were signi cantly heritable at univariate p-value < 0.05, and 3 were bivariate signi cant at p-value < 0.05 and 5 of those loci had 95% con dence intervals (CIs) for the variance that included 1 (Table S12).After adjusting for Bonferroni correction, 2 out of 3 loci were statistically signi cant for correlation.Thirty-four of the 59 loci had signi cant heritability for both diseases, and 7 of those loci had a bivariate p-value < 0.05; 7 of those loci had 95% CIs for the variance that included 1, suggesting a shared signal between the diseases and that the local genetic signal of those phenotypes is completely shared even though they are not statistically signi cant (Tables S7 and S12).After adjustment using Bonferroni correction, 6 out of 7 loci were statistically signi cant for correlation.Overall, 8 out of 76 loci had a Bonferroni adjusted bivariate p-value < 0.05.These results provide further evidence of pleiotropy with mixed effect direction between AD and immune-mediated diseases with 3 positively correlated and 5 negatively correlated at Bonferroni adjusted bivariate p-value < 0.05.We found only one genomic locus (chr 2:64696090-65938002) shared between LAVA and ConjFDR results.

Exploration of shared biological mechanisms: Functional annotation and tissue and cellspeci city analyses
Functional annotation for all the lead SNPs at conjFDR < 0.05 within the loci shared between AD and immune-mediated diseases showed that the majority of the lead SNPs are intronic or intergenic (Table S4).Two lead SNPs were reported to have a CADD score > 12.37, suggesting deleteriousness (rs12790721 and rs602662, GRAMD1B and FUT2, respectively) 40 .Seven lead SNPs reported a low RegulomeDB scores of 1d or 1f, suggesting regulatory functionality (rs4233366, rs739954, rs2074404, rs10748526, rs3764310, rs4275659 and rs4958435, nearest genes ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, respectively) 41 (Table S13).These SNPs were associated with eQTL functionality in different brain, blood, and immune system regions.For a few examples, we found eQTL functionality for genes NDUFS2, PCP4L, PCDHA8, SRA1, PCDHA10, VTRNA1-3, PCDHA3, PCDHA6, PCDHB15, WDR55, CYSTM1 and 739 unique genes (Table S14).Functional annotation for all SNPs associated with lead SNPs at conjFDR < 0.1 and r 2 > 0.6 were extracted from GWAS summary statistics, between AD and immune-mediated diseases.The majority of the candidate SNPs are either intronic or intergenic (Table S15).In Figures S2 and 4, the shared genes between AD and each speci c immune-mediated disease are shown.
Functional annotation of the lead SNPs using positional, eQTL and chromatin interaction mapping was performed to understand the biological mechanism affecting AD and immune-mediated diseases.Based on the three gene mapping techniques, we identi ed 142 unique credible genes mapped to the lead SNPs between AD and immune-mediated diseases (Table S16).The credible genes were mapped to the selected eQTLs available in FUMA.Out of the 70 unique shared loci (71 unique SNPs) identi ed using the conjFDR method, 50 loci were identi ed as eQTLs covering 750 unique genes across the different brain, blood, and immune system tissues in the GTEx database (Table S16).
To understand the tissues that these SNP loci tend to manifest their impacts, we leveraged the in-house method DeepFun and identi ed that three out of the top-ranked ve tissues were immune and brain tissues (thymus, cerebellum, and frontal cortex) (Figure S3).We mapped these SNP loci to the proximity gene using the online tool WebCSEA, and found enrichment in the lymphatic system.There was a signal in the nervous, cardiovascular, and endocrine systems (Figure S4); the top enriched cell types were mostly related to the immune and nervous systems (neutrophil, macrophage, monocytes, innate lymphoid cell, plasma cell, excitatory neuron, purkinje cell, microglia, dendritic and glial cells) (Figure S5) (nominal signi cance p-value < 1 x 10 − 3 ).Finally, the top biological pathways were related to cell adhesion and the immune system (Table S17).

Lack of signi cant causal association observed between AD and immune-mediated diseases
We evaluated the potential causal relationship between immune-mediated diseases and AD using ve MR methods (MR Egger, weighted median, inverse variance weighted (IVW), simple mode, and weighted mode) implemented in the 2SMR package.There was no su cient evidence for causal relationship between immune-mediated diseases and AD in all these tests, except for asthma which had a signi cant causal relationship with AD based on weighted median and weighted mode methods (Table S18).

Discussion
In this study, we performed a comprehensive assessment of the shared genetic architecture between AD and immune-mediated diseases by analyzing largescale GWAS summary data using different but complementary genetic approaches in order to shed light into their shared underlying molecular biology mechanisms.The ndings show extensive genetic overlap between AD and immune-mediated diseases regardless of genetic correlation.We identi ed several shared and novel loci using the conjFDR approach and identi ed several pathways and immunological signatures enriched in the brain and immune system.
In our analyses, LDSR did not show signi cant genome-wide genetic correlation between AD and each immune-mediated trait.Still, the evidence of signi cant genetic overlap veri ed by MiXeR in tandem with nonsigni cant genetic correlation re ects shared genetic etiologies with mixed effect directions, a suggestion which is corroborated by the local genetic correlation performed using LAVA; this balanced mixture of concordant and discordant shared loci distributed across the genome indicates that some genetic variants can increase the risk of one disorder while decreasing the risk of the other.In addition, all MR analyses, with the exception of perhaps asthma, gave us no support for a signi cant causal relationship between AD and immune-mediated diseases, indicating that pleiotropic and common biological pathways may be a better explanation for their association.A similar study showed no signi cant causal relationship between AD and immune-mediated diseases with the exception of multiple sclerosis and Sjogren's syndrome 47 .In our study, a total of 70 unique shared genomic loci were identi ed between AD and immune-mediated diseases by the conjFDR analyses employing GWAS data, being enriched in biological pathways related to cell adhesion and the immune system.They were mostly enriched in the lymphatic system, with a signal being seem in the central nervous system; the top enriched cell types were also related to the immune and nervous systems, including neurons and microglia.
Two SNPs were suggestive of deleteriousness, rs12790721 and rs646327.The rst is an intron variant and eQTL of GRAMD1B (11q24.1) in blood, and the second is an exonic variant of FUT2 (19q13.33 ).Both genes are protein-coding genes, for the proteins aster-B and galactoside alpha-(1, 2)-fucosyltransferase 2, respectively.GRAMD1B is expressed in the brain, in astrocytes, oligodendrocytes precursor cells, and in inhibitory and excitatory neurons, mainly maintaining synaptic function, and is also expressed in the immune system.It is predicted to be involved in cellular response to cholesterol and cholesterol homeostasis.Aster-B, as a novel regulator of mitochondrial cholesterol and fatty acid transport, and excess mitochondrial cholesterol, has also been a biomarker in AD.Because abnormal mitochondrial cholesterol is a common phenotype in AD, Aster-B has been suggested as a target for the development of therapeutics for AD 48 .Regarding FUT2, fucosylated host glycoproteins or glycolipids mediate interaction with intestinal microbiota, in uencing its composition [49][50][51] .There is evidence suggesting a link between gut microbiota dysbiosis and the pathogenesis of AD, with the gut microbiota modulating neuroin ammation indirectly by impacting microglia and having effects on synaptic neurotransmission dysfunction, with a cross-talk between peripheral and central in ammation through microbiota-mediated microglial alterations 52,53 .
In addition to deleteriousness, seven lead SNPs were considered to have important regulatory functionality, mapped to the genes ADAMTS4 (UTR3 region), HBEGF (intergenic), WNT3 (intronic), TSPAN14 (intronic), DHODH (intronic), ABCB9 (intronic) and TNIP1 (intronic), all of each are protein-coding genes.For instance, the protein coded by ADAMTS4 is expressed in the cytoplasm in several tissues, being most abundant in the central nervous system.Its RNA is mostly expressed in oligodendrocytes, and is related to myelination.The enzyme coded by ADAMTS4 is responsible for amyloid deposition in AD 54,55 .HBEGF has been associated with AD, with its overexpression resulting in increased APP protein level 56 .It is expressed in the cortex and hippocampus, mostly in neurons and astrocytes, and also in the immune system.Genetic deletion of HBEGF cause cognitive dysfunction, which is reversed by NMDA receptor agonists 57 .Heparin-binding epidermal growth factor-like growth factor (HB-EGF), coded by HBEGF, also restores neurogenesis in the hippocampus of aged mice 58 .It also contributes to the proliferation of glial cells and to the survival of dopaminergic neurons 59 .DHODH, it is detected in the immune system and is related to mitochondrial function and cellular homeostasis and also to alterations in reactive oxygen species levels 60 .TNIP1 is related to in ammatory response, including neuroin ammation and microglia activation, in addition to regulating nuclear factor kappa-B activation 61 , being related to AD 62 .Finally, in the loci to eQTL analyses, several SNPs were found associated with eQTL functionality in brain tissue and in the immune system.For instance, NDUFS2 gene is a protein-coding gene that is associated with mitochondrial complex I alterations.A transcriptome-wide association study of AD using brain tissue found that NDUFS2 is one of the putative causal genes in AD 63, 64 .
Six genes were found to be shared between AD and at least two immune-related diseases: ADAMTS4, JAZF1, Y_RNA, C1orf106, AC195454.1,and RP11-95M15.1.As discussed above, ADAMTS4 is responsible for amyloid deposition in AD 54,55 .JAZF1 is a protein-coding gene which functions as a transcriptional repressor; it is involved in glucose and lipid metabolisms 65 .Y_RNA is a small non-coding RNA gene.It has been implicated in cellular processes such as DNA replication and RNA quality control.Y_RNA has been found in extracellular vesicles (EV) from multiple different cell lines, and EV-associated Y-RNA may be involved in a range of immune-related processes, including in ammation and immune suppression, being regulated in immune cells by Toll-like receptor (TLR) signaling 66 .Dysregulation of Y-RNA also has been found to cause alternative splicing in neurons of individuals with AD 67 .C1orf106, also known as CALHM6, encodes the protein calcium homeostasis modulator, which may play an important role in several immune in ammatory responses.It is upregulated by interferon-gamma and tumor necrosis factor alpha 68 .AC195454.1 is a long non-coding gene with unknown functional category.It is overexpressed in the brain and has been implicated in SLE 69,70 .Finally, RP11-95M15.1, also a long non-coding gene, has been associated with PSC 71 .
One of the major strengths of our study is the employment of diverse albeit complementary statistical genetic approaches, enabling an extensive analysis of the genetic associations between AD and immune-mediated diseases.In addition, the fact that we included generally well-powered GWAS suggests that our results are mostly not due to small sample sizes.Another strength is that we included only diagnosed cases of AD and not by proxy subjects.Having said that, our study has inherent limitations that should be considered alongside the present ndings.The analyses were restricted to participants of European ancestry; thus, our ndings may not be generalizable to populations of other ancestries.In addition, since we used the most comprehensive GWAS data available, replication analyses in independent samples were not possible.Also, AD is not a highly polygenic disease, which might have underestimate the number of variants found in the MiXeR analyses.Further, the AIC values from the MiXeR analysis for the negative control HCM were all negative, showing poor t and that the results of this particular analysis are unreliable.Finally, the gene mapping strategy was based on statistical analyses, and should be validated with experimental studies.
In summary, our study provides genetic insights into the observed epidemiological relationship between AD and immune-mediated diseases, exposing shared genetic susceptibility extensively distributed across the genome.Our results support a signi cant genetic association between AD and immune-mediated diseases with mixed effect directions, with some genes increasing the risk of one disease but decreasing the risk of the other.Furthermore, we pinpoint shared loci and genes between AD and immune-mediated diseases that have the potential to be intended for further research, such as GRAMD1B, FUT2, ADAMTS4, HBEGF, WNT3, TSPAN14, DHODH, ABCB9 and TNIP1, all of which are protein-coding, and thus show promise as potential drug targets, and showed deleteriousness or regulatory functionality.We also highlight the signi cance of the immune system as a shared mechanism, albeit a non-causal one, in AD and immune-mediated diseases, providing further support for the immunoin ammatory hypothesis in AD.Overall, our results provide an atlas of the shared genetic architecture of AD and immune-mediated diseases, and their long-noticed comorbidity association, with implications for personalized interventions for the prevention and treatment of AD and immune-mediated diseases.

Declarations Figures
Work ow for the study AD and 11 immune-mediated diseases.

Figure 2 Genetic
Figure 2Genetic overlap between AD and 11 immune-mediated diseases using MiXeR.Venn diagrams represent the genetic overlap, regardless of direction, between AD and each immune-mediated disease.The size of the circles represents the polygenicity of the phenotype.Numeric values represent the estimated number of variants.Abbreviations: AD: Alzheimer's disease; AR: allergic rhinitis; AtD: atopic dermatitis; CeD: celiac disease; CD: Crohn's disease; HCM: hypertrophic cardiomyopathy; PSC: primary sclerosing cholangitis; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; and UC: ulcerative colitis.

Figure 3 Conditional
Figure 3Conditional quantile-quantile plots of nominal vs. empirical -log 10 p-values in AD below the standard genome-wide association study threshold of p-value < 5 x 10 -8 as a function of signi cance of association with immune-mediated diseases at the level of -log 10 p-values of 1, 2, or 3, corresponding to p-value = 0.1, pvalue = 0.01, and p-value = 0.001, respectively.Signi cant association was found between AD and all immune-mediated diseases.No signi cant association was found between AD and HCM (negative control).The null hypothesis is indicated by the dashed lines.Abbreviations: AD: Alzheimer's disease; AR: allergic rhinitis; AtD: atopic dermatitis; CeD: celiac disease; CD: Crohn's disease; HCM: hypertrophic cardiomyopathy; PSC: primary sclerosing cholangitis; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; and UC: ulcerative colitis.

Table 2
Novel loci associated with AD and Immune-mediated diseases based on conjunctional false discovery rate analysis.