Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits

Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits Fotios Koskeridis, Nurun Fancy, Pei Fang Tan, Evangelos Evangelou, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3851905/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Nov, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Several cardiovascular (CV) traits and diseases co-occur with Alzheimer’s disease (AD). We mapped their shared genetic architecture using multi-trait genome-wide association studies. Subsequent fine-mapping and colocalisation highlighted 19 genetic loci associated with both AD and CV diseases. We prioritised rs11786896, which colocalised with AD, atrial fibrillation (AF) and expression of PLEC in the heart left ventricle, and rs7529220, which colocalised with AD, AF and expression of C1Q family genes. Single-cell RNA-sequencing data, co-expression network and protein-protein interaction analyses provided evidence for different mechanisms of PLEC , which is upregulated in left ventricular endothelium and cardiomyocytes with heart failure (HF) and in brain astrocytes with AD. Similar common mechanisms are implicated for C1Q in heart macrophages with HF and in brain microglia with AD. These findings highlight inflammatory and pleomorphic risk determinants for the co-occurrence of AD and CV diseases and suggest PLEC, C1Q and their interacting proteins as novel therapeutic targets. Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease Health sciences/Medical research/Epidemiology Health sciences/Medical research/Genetics research Health sciences/Diseases/Cardiovascular diseases Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Alzheimer’s disease, the most common cause of dementia, is a leading health challenge for our times. More than 55 million people worldwide were estimated to be living with dementia in 2020 with 60%-70% of them being AD cases 1 , 2 . Alzheimer’s disease (AD) has been considered a brain-specific disease whose primary pathology is confined to the brain. However, epidemiological association and more recent causal genetic analyses have suggested mechanistic links between cardiovascular abnormalities and AD 3 – 5 . Several hypotheses have been proposed to explain this. AD and cardiovascular (CV) disease-related traits share common risk factors such as obesity, diabetes and inflammation, which may increase the risk of both diseases through independent (horizontal pleiotropy) or common biological pathways 6 . Other hypotheses include the indirect influence of impaired vascular functions that initiate or accelerate the progression of AD 7 . They also may share common genetic determinants 8 . Genome-wide association studies (GWAS) have identified genetic risk loci for both AD and pathological CV traits and identified common genetic factors that may refer to the shared underlying pathways. One example of such genes is apolipoprotein E ( APOE ), which encodes a lipid-transport protein involved in cholesterol metabolism 9 , that is the strongest genetic risk factor for AD 10 , 11 and a risk factor for adverse CV traits, including coronary artery disease 12 and myocardial infarction 13 . A deeper understanding of the shared genetic architecture between AD and CV traits will provide insights into potentially shared and distinct aetiologies of these phenotypes. Identification of shared targets and mechanisms by which they confer functional effects can be used to discover those whose modulation could address both neurodegenerative and cardiovascular diseases. Here, we further investigated the commonalities in the genetic architecture of AD and CV traits and identified pleiotropic loci affecting multiple traits aiming to define common targets for therapeutic modulation. We performed a large-scale multi-trait GWAS analysis on AD and several CV traits, followed by genetic colocalization analysis to highlight candidate pleiotropic genes and their tissue sites of action. To further characterise biological pathways involved in both diseases, we leveraged data from single-cell RNA-seq for differential gene expression with disease to explore relevant gene co-expression networks and protein-protein interactions in the brain and cardiovascular tissues. A schematic overview of the study is presented in Fig. 1 . Results Multi-trait genetic association analysis identifies 44 shared loci between AD and CV traits We performed five pairwise multi-trait analyses of GWAS (MTAG) 14 on AD and coronary artery disease (CAD), atrial fibrillation (AF), stroke, carotid intima-media thickness (cIMT), and systolic and diastolic blood pressure (SBP, DBP). The analyses identified 62 unique genetic loci associated with AD at genome-wide significance (GWS) level ( P < 5×10 − 8 ) across these pairwise MTAG analyses, of which 22 were novel (not within ± 500 kilobases (kb) of the previously known AD loci ( Supplementary Table 1 ). Among the 62 AD genetic loci, there were 169 unique single-nucleotide polymorphisms (SNPs) (126 independent signals, linkage disequilibrium, LD < 0.1) associated with AD at GWS level. Furthermore, we found 1,223 top signals associated with different CV traits at GWS level in 740 genetic loci ( Supplementary Table 2 ). Overall, 66 of the unique AD SNPs (44 loci) were additionally associated at GWS level with at least one of the examined CV traits ( Supplementary Table 3 ). A colocalisation analysis defines genetic loci shared by AD and different CV traits Using the Hypothesis Prioritisation in multi-trait Colocalization (HyPrColoc) 15 method on 847 MTAG-reported loci (62 AD + 785 CV), we identified 26 loci which colocalised between AD and CV traits with a posterior probability (PP) > 0.5 (Fig. 2 , Supplementary Table 4 ). Most colocalised loci were found either between AD and AF (10 loci) or between AD and DBP (7 loci). The most substantial evidence for colocalisation was observed for a locus at chr8:124,608,614 ± 200kb ( RN7SKP155 ) associated with AD and cIMT (PP = 1) and a locus at chr11:47,391,948 ± 200kb ( SPI1 ) associated with AD and DBP (PP = 0.95). Among the 26 loci with evidence for colocalization, there were three loci for which a single candidate causal variant explained a large proportion of the association: rs11786896 (mapped in PLEC ; colocalised with AD-AF; PP = 0.97; 86% of PP explained by SNP), rs7529220 (mapped in HSPG2 ; colocalised with AD-AF; PP = 1; 90% of PP explained by SNP) and rs429358 ( APOE ; colocalised with AD-CAD; PP = 0.57; 93% of PP explained by SNP). Gene expression colocalisation analysis prioritises causal genes shared by AD and CV traits To identify potential pleiotropic causal genes for the colocalised loci, we tested the colocalisation of AD and CV traits with the expression of nearby genes in 48 tissues using expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression (GTEx) in the 26 colocalised loci. We found that AD and at least one CV trait colocalised in 19 loci with expression of one or more genes in the same tissue, for a total of 106 associations with 61 genes ( Supplementary Fig. 1, Supplementary Table 5 ). Of these, 60 associations were found for AF (in 8 loci with 30 genes), 22 for DBP (6 loci with 16 genes), 16 for stroke (2 loci with 11 genes) and 8 for cIMT (3 loci with 6 genes). In two loci, a single candidate causal variant explained the colocalisation of AD, CV trait and tissue-specific gene expression: rs11786896 ( PLEC ) and rs7529220 ( HSPG2 ). The intronic variant rs11786896 ( PLEC ) explained the colocalisation of AD and AF with expression levels of PLEC in the cardiac left ventricle (PP = 0.99, %PP explained by SNP = 99%) and skeletal muscle (PP = 0.92, %PP explained by SNP = 98%) and with expression levels of DGAT1 in oesophageal mucosa (PP = 0.93, %PP explained by SNP = 85%) (Fig. 3 ). rs11786896 was associated with increased risk of AD (Odds Ratio, OR = 1.02, P = 9×10 − 5 ), increased risk of AF (OR = 1.09, P = 1.3×10 − 6 ) and lower expression of PLEC in cardiac left ventricle (Beta = − 0.71, P = 5.9×10 − 13 ), as well as in skeletal muscle (Beta = − 0.3, P = 7.7×10 − 7 ). The same variant was associated with higher expression of DGAT1 in oesophageal mucosa (Beta = 0.29, P = 3.1×10 − 5 ). The intergenic variant rs7529220 ( HSPG2 ) explained the colocalisation of AD and AF with expression levels of C1QA (PP = 0.85, %PP = 82%), C1QB (PP = 0.83, %PP = 97%) and C1QC (PP = 0.61, %PP = 99%) in breast mammary tissue. The variant was associated with higher risk of AD (OR = 1.01, P = 1.4×10 − 3 ), higher risk of AF (OR = 1.06, P = 2.3×10 − 10 ) and increased expression of C1QA (Beta = 0.19, P = 2.4×10 − 4 ), C1QB (Beta = 0.17, P = 2.6×10 − 4 ), and C1QC (Beta = 0.15, P = 8.1×10 − 4 ) in mammary tissue. PLEC and C1Q are differentially expressed in the left ventricle with heart failure Defining the cells in which target genes are expressed and directions of expression associated with disease risk and with disease is important for predicting the directions of effect for potential therapeutic modulation. Therefore, we tested the expression patterns of PLEC and C1Q genes across cell types in single-cell RNA from the heart to discover whether differences in differential expression with disease were consistent with those predicted for disease risk. PLEC was expressed in all cell types found in the cardiac left ventricle, while C1Q was expressed only in macrophages (Fig. 4 A, 4 B). PLEC was differentially expressed with heart failure (HF) relative to healthy controls with upregulated expression in the endothelium (log2 fold change, log2FC = 0.40, P = 0.015) but downregulated in macrophages (log2FC = − 0.59, P = 6.7×10 − 5 ). There was only a trend for differential expression with HF in cardiomyocytes (log2FC = 0.91, P = 0.06). All C1Q associated genes were downregulated in cardiac macrophages ( C1QA , log2FC = − 1.39, P = 1×10 − 6 ; C1QB , log2FC = − 1.34, P = 3.5×10 − 5 ; C1QC , log2FC = − 1.28, P = 1.4×10 − 4 ). We explored differential expression with HF further by constructing high dimensional weighted gene co-expression networks (WGCN) for PLEC and C1Q to identify modules of highly correlated genes across cell types. We generated 12 gene co-expression modules in cardiac endothelial cells, 14 in cardiomyocytes and 6 in macrophages ( Supplementary Table 6 ). A differential module eigengene analysis indicated that the module including PLEC was upregulated in cardiac vascular endothelial cells and cardiomyocytes in HF cases relative to the healthy controls and downregulated in macrophages with dilated cardiomyopathy (dCM) cases relative to healthy controls ( Supplementary Table 7 ). The C1Q -containing module also was downregulated in cardiomyocytes in HF cases relative to healthy controls and downregulated in macrophages in dCM cases relative to healthy controls. Gene-set enrichments for biological processes defined the pathways most highly enriched in PLEC -containing modules ( Supplementary Table 8 ), which included the “vascular endothelial growth factor receptor-2 signalling” and “endothelium development” pathways in endothelial cells (Fig. 4 C) and many pathways related to mitochondrial oxidative metabolism in cardiomyocytes ( Supplementary Fig. 2 ). In macrophages, the module including C1Q genes was enriched for “complement activation” and “synapse pruning” pathways, among others ( Supplementary Fig. 3, Supplementary Table 9 ). PLEC and C1Q interactomes are enriched in cardiomyocytes, cardiac vascular endothelial cells and macrophages To gain insights into the potential functional roles of proteins, we performed cell-specific protein-protein interaction (PPI) analyses on the set of colocalised candidate genes by constructing their protein interactomes across cell types of human cardiovascular tissue ( Supplementary Tables 10 and 11 ). The PLEC interactome was upregulated in a pathway related to “ribosomal small subunit assembly” in endothelial cells (Fig. 4 D) and upregulated in a “SRP-dependent co-translational protein targeting to membrane” pathway in cardiomyocytes ( Supplementary Fig. 4 ). Additionally, an interactome containing both PLEC and NDUFS3 was enriched with HF and was upregulated in the pathways related to “aerobic electron transport chain” in endothelial cells (Fig. 4 D) and “acetyl-CoA biosynthetic process from pyruvate” and “energy coupled proton transport” in cardiomyocytes ( Supplementary Fig. 4 ). In macrophages, the PLEC-NDUFS3 interactome in HF was enriched for “mitochondria electron transport of cytochrome c to oxygen” while C1Q interactome was enriched for “cell junction disassembly” ( Supplementary Fig. 5 ). PLEC is differentially expressed in brain astrocytes and is upregulated in AD We also tested the expression of PLEC and C1Q across different cell types in post-mortem human brain samples. PLEC was highly expressed in astrocytes and (to a lesser degree) in neurons. C1Q genes were expressed primarily in microglia (Fig. 5 A, 5 B). PLEC was significantly upregulated in astrocytes from AD donors relative to non-diseased control donors (log 2 FC = 1.01, P = 0.003). C1Q genes were not significantly differentially expressed in microglia but consistently showed lower mean expression with AD ( C1QA : log 2 FC = − 0.46, P = 0.23; C1QB : log 2 FC = − 0.5, P = 0.3; C1QC : log 2 FC = 0.14, P = 0.9). We explored the cell-specific differential expression of these and co-expressed genes further with WGCNA, which identified 14 gene co-expression modules in astrocytes and 8 in microglia ( Supplementary Table 12 ). Differential module eigengene analyses showed that the PLEC -containing module was upregulated in astrocytes and the C1Q -containing module was downregulated in microglia ( Supplementary Table 13 ). Gene-set enrichment for biological processes identified 25 associated pathways for the PLEC -containing module in astrocytes including “neuron projection morphogenesis” and “extracellular structure organisation” and 25 associated pathways for the C1Q gene module in microglia including the “positive regulation of intrinsic apoptotic signalling” ( Supplementary Fig. 6, Supplementary Table 14 ). We further explored the discordant directions in C1Q expression between eQTLs and post-mortem brain single nuclei data in relation to the beta-amyloid pathology load in microglia. For subjects with lower beta-amyloid load in the brain, we observed an increasing expression of C1Q in concordance with eQTL but subjects with higher beta-amyloid load showed decreased expression ( Supplementary Fig. 7 ). PLEC, NDUFS3 and C1Q interactomes are enriched in astrocytes and microglia A PPI analysis on candidate gene networks highlighted that the PLEC-NDUFS3 interactome also was enriched in both astrocytes and microglia in AD cases compared to controls ( Supplementary Tables 10, 15 and 16 ). Associated functional pathways were enriched for the “aerobic electron transport chain” and “SRP − dependent co-translational protein targeting to membrane” pathways in the astrocytes (Fig. 5 D). In microglia, the C1Q interactome was enriched in “SRP − dependent co-translational protein targeting to membrane” pathway ( Supplementary Fig. 8 ). Discussion We adopted a novel approach to understanding the co-occurrence of AD and CV disease and traits based on several multi-trait GWAS to characterise the shared genetic architecture of AD with CV traits. Convergent evidence from colocalisation between AD, CV traits and eQTLs prioritised two genetic regions that each included a single candidate causal variant (rs11786896 expressed via PLEC and rs7529220 expressed via C1QA , C1QB , and C1QC ) shared between AD and AF. Single-cell RNA-sequence data, co-expression network and protein-protein interaction analyses together were consistent in showing that PLEC is upregulated in left ventricular endothelium and cardiomyocytes with HF and in brain astrocytes with AD. By contrast, while C1Q genes are predicted to be upregulated with greater disease risk in cardiac macrophages for HF and in brain microglia for AD, we found opposite directions of difference with disease for both. We explored differences in the direction of changes from early disease (low beta-amyloid pathology load) to late (high beta-amyloid pathology load) for microglia and found the congruence with directions predicted for disease risk in early disease that was lost with in later disease progression. Our findings provide new insights into genetic pleiotropic effects and potential shared mechanisms causally related to both AD and CVD. Out of the several CV traits and diseases examined with AD, AF showed the largest number of pleiotropic signals with AD. Numerous observational studies, provide growing evidence that AF is associated with cognitive impairment, risk of AD and other dementias 16 . However, it has been unclear whether the diseases have a shared pathophysiology or whether the relationship arises as downstream consequences of AD (e.g., stroke). Here we provide evidence defining common genetic determinants for the two diseases. The colocalised intronic variant rs11786896 within the plectin gene ( PLEC ) was associated with lower expression of PLEC in the cardiac left ventricle (and skeletal muscle) and increased the risk of both AD and AF. PLEC is a member of a protein family, named plakins, with a crucial structural role in the cytoskeleton including cell architecture and tissue integrity and a partially functional role in the assembly, positioning, and regulation of signalling complexes 17 , 18 . Plectin is expressed as various tissue-dependent protein isoforms in several tissues with each tissue to be characterised by different proportion and composition of plectin isoforms and each distinct isoform to be characterised by specific functions 19 – 23 . However, the precise role of plectin in macrophages is unknown and thus, further study is needed to determine its specific functions and interactions in macrophages. Previous studies of human tissues or preclinical models provide independent evidence for an association of plectin with diseases including AD and AF 24 , 25 . Our new data and analyses provide evidence that risk in AD is affected via functions of plectin in astrocytes 26 . Astrocytes play multiple roles, central to the pathology of AD, including metabolic support for neurons, modulation of brain microvascular function and, through activities associated with those of microglia, inflammatory responses 26 , 27 . We hypothesise that these functional roles are mediated in part by interactions of plectin with intermediate filaments (IFs), microtubules and actin filaments 26 . IFs are important structural components of the cytoskeleton with crucial roles in synaptic activity, neurogenesis and repair after brain injury 28 . Differences in expression of plectins modulate neuronal function and vesicular trafficking generally and interactions with tau suggest potential roles specific to AD 29 – 31 . PLEC may play related roles in cardiomyocytes for assembling and mobilizing the intermediate filaments and their networks, effects that both modulate contractile function in cardiomyocytes and inflammatory responses in macrophages 32 . Another colocalised variant between AD and AF, the intergenic rs7529220, which is located 19k upstream from Heparan Sulfate Proteoglycan 2 ( HSPG2 ) and 21k downstream from Chymotrypsin Like Elastase 3B ( CELA3B ), was associated with increased risk of AD and AF and higher expression of three genes of the Complement Component 1, Q Subcomponent ( C1Q ) family ( C1QA , C1QB , C1QC ) in breast mammary tissue (and, by inference, in brain vasculature). The variant is located 680kb downstream of C1Q genes. The complement system plays a central role in synaptic remodelling in the brain and in cellular damage response more generally in the body 33 , 34 . We hypothesise that greater expression of C1Q may lead to higher activity of the complement system which in turn may potentiate synapse loss in early AD 35 . Similarly, C1Q has roles in the genesis of atherosclerotic plaques 36 and in the regulation of early stages of inflammatory responses to the cardiomyocyte injury associated with a range of cardiac traits 37 . Our study, which applied the MTAG approach in a novel way across diseases, had several strengths. First, we secured high statistical power for our study by including GWAS with substantial sample sizes ranging from 185,000 to 1,000,000 participants and we boosted the power even higher by performing suitable multivariate methods. Second, we combined advanced methods of genetic epidemiology and basic sciences and sought to provide supporting evidence from a variety of data. However, a number of limitations also must be acknowledged. We restricted our analyses to a population of European ancestry. The lack of genetic diversity may have hampered the possibility of detecting other relevant variants. Additionally, we did not investigate a considerable portion of the genetic predisposition coming from rare variants (MAF < 1%) as we excluded them from our analyses. However, including these variants might lead to false-positive findings and biased results. Moreover, we used statistical methods to detect pleiotropy, and therefore considered a genetic locus pleiotropic if it was statistically significantly associated with two or more phenotypes. However, this approach for identification of pleiotropic genes may not always highlight shared biological pathways, as the identified genes could affect the traits independently via different pathways (horizontal pleiotropy), or they could even be expressed in different tissues in response to different signals 38 , 39 . Furthermore, due to a limited number of cells for specific cell types, we had to combine single-cell data from multiple samples. We focused on tissue samples that were already enriched for cardiomyocytes, endothelium, and macrophages. Finally, the expression for some candidate genes in our data was limited and thus additional sequencing data and reads are needed to investigate them further. Additional RNA sequencing data of different AD and CV conditions would probably be even more informative. In conclusion, we performed a multi-trait analysis on AD and CV traits and a subsequent colocalisation analysis detecting 19 shared genetic loci and further prioritizing two shared causal variants between the aforementioned traits. Our findings define shared mechanisms for AD and different cardiovascular diseases. The complement system has been explored as a target for novel preventive or disease-modifying therapies in cardiovascular disease 40 and AD 41 . Our work suggests that plectin or members of its interactome could offer new and potentially promising targets for preventive and therapeutic medicines with benefits across these common comorbid disorders. Online Methods Study population We restricted our study to a population of European ancestry. We used the summary statistics from seven GWAS on the following diseases: AD 10 , AF 42 , CAD 43 , cIMT, stroke 44 , SBP 45 , and DBP 45 . Supplementary Table 17 presents the basic characteristics of all the included GWAS. Genotypic quality control The seven initial datasets contained genotyped and imputed SNPs ranging from 7 to 34 million SNPs. We included in the analysis only SNPs that were present in both datasets (AD and the examined CV trait). Furthermore, we excluded all insertions, deletions, and rare variants (minor allele frequency; MAF < 0.01), variants with sample sizes less than 2/3 of the 90th percentile and palindromic SNPs. Finally, more than 5.75 million SNPs were included in the analysis. Multi-trait association analysis We performed five bivariate analyses on AD and a different each time CV trait (1. AF, 2. CAD, 3. cIMT, 4. Stroke, 5. SBP-DBP) using MTAG 14 . We calculated the genetic correlation between the traits and further corrected our data for sample overlap using bivariate LD score regression as implemented in MTAG. Each MTAG analysis generated distinct trait-specific datasets (11 in total: 5 with AD plus 6 with CV traits) containing the trait-specific effect estimates for the included SNPs after leveraging for genetic correlation of the examined traits. As a result, the summary statistics from MTAG can be interpreted as similar to those from a univariate single-trait GWAS 14 . Functional Mapping & Annotation We used Functional Mapping and Annotation of GWAS (FUMA) 46 to functionally analyse all the generated summary results from MTAG. All the genome-wide significant (GWS) SNPs (P < 5×10 − 8 ) were initially clumped (r 2 < 0.6) to determine the coordinates of the genomic risk loci and then clumped again (r 2 < 0.1) to define independent signals. SNPs in pairwise-LD at 0.1 ≤ r 2 < 0.6 or SNPs located closer than 500kb were assigned to the same LD block. SNPs that survived the second clumping were the independent signals. Independent SNPs with the smallest P-value in each LD block were defined as the top signals while the remaining were secondary signals. We further performed annotation and gene prioritization analysis including all SNPs that survived the first clumping. We used the European sample of 1000 Genome Project Phase 3 47 to calculate pairwise LD between SNPs. SNPs were positionally mapped to their nearest protein-coding genes (Ensembl build v92). To identify the unique AD top and secondary independent signals, we gathered the AD independent signals from all pairwise AD-CV analyses and excluded duplicate signals or proxies (either in distance ± 500 kb or in LD r 2 > 0.1), keeping the strongest signal with the smallest P-value. Trait-trait and trait-eQTL colocalisation analysis We used HyPrColoc R package 15 to perform colocalisation analysis. HyPrColoc is a Bayesian divisive clustering algorithm for identifying shared genetic associations between traits in a genomic region using GWAS summary statistics. We performed this method to identify colocalised loci between AD and CV traits and prioritise causal variants explaining the shared association. We performed a trait-trait colocalisation analysis for each top signal indicated from MTAG in a region ± 200 kb from the top SNP. We considered variant-specific priors for our analyses, which assumes that the probability of a variant being colocalised with a set of traits decreases as the number of the set of traits increases. The variant-specific priors model requires the specification of two priors. We specified the prior probability that a variant is associated with a single trait only at P = 1×10 − 4 and a conditional prior probability that a variant is associated with an additional trait given that it is already associated with another trait at P c = 0.02. A PP higher than 0.5 was considered adequate evidence that the examined traits colocalise in the locus. We divided the evidence of colocalisation into two categories: 1) considerable evidence (0.5 < PP < 0.75) and 2) strong evidence (PP ≥ 0.75). To deal with spurious pleiotropy, we restricted the analyses to regions with at least one SNP with P < 5×10 − 4 in the respective univariate GWAS. Additionally, we visually inspected the colocalised loci by constructing suitable regional plots. The variants explaining at least 80% of the shared association (%PP ≥ 80%) were considered candidate causal. To limit the probability of false positive findings, we considered as causal the variants that were associated (P < 0.01) with both AD and the respective CV trait in the respective included univariate GWAS. For the loci found to colocalise in the trait-trait colocalisation analysis, we further performed trait-expression quantitative trait loci (eQTL) colocalisation using AD, CV trait and eQTL from 48 tissues, retrieved from Genotype-Tissue Expression version 7 (GTEx v7), implementing the same parameters and approach as described in trait-trait colocalisation. The trait-eQTL colocalisation analysis was conducted to detect shared genes between the traits and investigate the tissues they are expressed. Single-cell & single-nuclei data acquisition We used Gene Expression Omnibus 48 (GEO) to retrieve data for single cells of the left ventricle from 6 heart failure (HF) cases and 7 healthy controls 49 , left ventricular single nuclei from 13 dilated cardiomyopathy (dCM) cases and 25 healthy controls 50 , and post-mortem brain single nuclei from 9 AD cases and 8 healthy controls. To deal with the small sample size in cardiac single-cell data, individuals with either coronary heart failure or dCM were considered HF cases. Quality control of single-cell & single-nuclei data To quality control (QC) the data we implemented the scFlow pipeline 51 . Samples with less than 100 cells in cardiac data and less than 200 cells in brain data were removed. For brain data, Ambient RNA profiles were performed using EmptyDrops 52 . We restricted the minimum number of expressive features to 300 for cardiac data and 100 for brain data. For cardiomyocyte-enriched samples, we set the minimum library size per cell to 1000 while keeping the default value for the rest cell types. Only genes with a minimum of 2 counts in at least 3 cells were included. Doublet cells and non-annotated genes were removed. After QC, there was adequate sample to analyse cardiomyocyte-enriched (2,989 cells from 2 HF cases and 5 controls) and endothelium-enriched single cells (2,269 cells from 4 HF cases and 4 controls), macrophage single nuclei (207,345 nuclei from 13 dHF and 25 controls), astrocyte (34060 nuclei from 25 AD cases and 24 controls), and microglia single nuclei (15292 nuclei from 25 AD cases and 24 controls). Single-cell & single-nuclei data integration, clustering and cell-type annotation Cells that successfully passed the QC were integrated across samples using the linked inference of genomic experimental relationships (LIGER) method 53 defining a parameter lambda = 5 and selecting a sample-specific optimum value for parameter K: cardiomyocyte-enriched single cells (K = 25), endothelium-enriched single cells (K = 30), macrophage single nuclei (K = 40), and brain single nuclei (K = 20). A dimensionality reduction was performed by implementing the uniform manifold approximation and projection (UMAP) 54 algorithm to generate two-dimensional embeddings of the LIGER integrated factors using the first 10 principal components (PCs) on heart single cells, the first 60 PCs on heart single nuclei and the first 30 PCs on brain single nuclei. We subsequently detected cell clusters of the UMAP embeddings implementing the Leiden community detection algorithm 55 using a sample-specific parameter k: cardiomyocyte-enriched (k = 9) and endothelium-enriched single cells (k = 10), heart single nuclei (k = 45), and brain single nuclei (k = 50). Following clustering, we used the Expression Weighted Celltype Enrichment (EWCE) 56 algorithm to perform a cell-type prediction on cell clusters using previously reported reference datasets for cardiac single cells 57 , cardiac single nuclei 50 and brain single nuclei 58 . Differential gene expression analysis Differential gene expression (DGE) analysis was performed separately for each cell-specific sample on all candidate genes detected from trait-eQTL colocalisation analysis. We investigated the expression of the genes in the RNAseq data and included only genes expressed in at least one cell type of the examined tissues. We followed a generalised linear mixed model approach as implemented in MAST 59 after excluding genes expressed in less than 10% of cells. Units for differential expression were defined as log 2 fold change per unit change of the respective contrast. We considered as meaningfully differentially expressed genes those with a log 2 fold change ≥ 0.25 and a nominal P -value < 0.05. Weighted gene co-expression network We further constructed cell type-specific co-expression networks on selected candidate genes using a high dimensional weighted gene co-expression network analysis (hdWGCNA) 60 R package. We applied the K-Nearest Neighbours algorithm to identify groups of similar cells by means of transcriptomics (metacells) and constructed a metacell gene expression matrix. We constructed the co-expression network using the lowest soft power threshold that has a Scale Free Topology Model Fit ≥ 0.8. Genes that were not grouped into any co-expression module were excluded (“grey” module). We also excluded modules with less than 20 genes. We obtained the module eigengene values, which describe the expression patterns of entire co-expression modules, and performed a differential module eigengenes analysis applying a Mann-Whitney U test. To reduce false-positive findings due to multiple testing inflation, we implemented the Benjamini-Hochberg false discovery rate (FDR) method 61 . We also conducted a pathway enrichment analysis using Enrichr v.3.0 R package 62 and analysed only gene-sets with at least 20 genes. Protein-protein interaction analysis We used the STRINGdb 63 R package to analyse the full protein-protein interaction network data from STRING v11 database 64 . We expanded the candidate set of genes from the trait-eQTL colocalisation analysis by incorporating genes with protein-protein interactions (experimental evidence ≥ 700) with the candidates. Using the previously constructed cell type-specific modules from hdWGCNA, we performed an enrichment analysis per module on candidate genes between the module and reference set using Fisher’s exact tests. The Benjamini-Hochberg FDR approach was used to correct for type I error inflation due to the multiple testing error. Declarations Competing Interest All authors declare no comparing interests Author Contributions A.D., D.W, P.M., and I.T. designed the research. F.K., N.F. and P.F.T. conducted the analyses and visualised the results. F.K., N.F., P.F.T., E.E., A.D., D.W., P.M., and I.T. interpreted the results. F.K. wrote the manuscript. N.F., P.F.T., D.M., P.E., A.D., D.W., P.M., and I.T. critically revised the manuscript. Acknowledgements The Genotype-Tissue Expression version 7 (GTEx v7) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from dbGaP accession number phs000424.v7.p2 on 30/03/2022. DW is supported by the Academy of Medical Sciences Professorship (APR7_1002). PMM and PE acknowledge personal support from the UK Dementia Research Institute, which is funding by the UKRI Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK, Edmond J. Safra Foundation and Lily Safra, an NIHR Senior Investigator Award, and the Imperial College Healthcare Trust (ICHT) NIHR Biomedical Research Centre. IT, AD, PE and PMM acknowledge additional generous support from the Trustees of the Sir Michael Uren Foundation for this work. Data availability The summary statistics from the GWAS included in this study are publicly available and can be retrieved from GWAS Catalog under the accession codes GCST007320 (AD), GCST006414 (AF), GCST003116 (CAD), #### (cIMT) (will be provided upon acceptance), GCST006906 (stroke), GCST006624 (SBP) and GCST006630 (DBP). Heart single-cell data from the left ventricle tissue was downloaded from Gene Expression Omnibus (GEO) under accession codes GSE109816 (cardiomyocytes-enriched samples) and GSE121893 (normal digested samples). Single-nuclei data for the left ventricular tissue was retrieved from GEO under accession code GSE109816 . Single-nuclei data for human post-mortem brain samples from AD and Control samples were downloaded from GEO XXXX (will be provided upon acceptance). The MTAG summary statistics generated in this study have been deposited in NHGRI-EBI GWAS Catalog under accession codes #### (will be provided upon acceptance). 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UK","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ioanna","middleName":"","lastName":"Tzoulaki","suffix":""}],"badges":[],"createdAt":"2024-01-11 01:20:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3851905/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3851905/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-024-53452-6","type":"published","date":"2024-11-13T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50403399,"identity":"aa350276-c109-4db8-a68b-cceb90827d79","added_by":"auto","created_at":"2024-01-31 04:10:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":708506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design schematic overview\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD Alzheimer’s disease, AF atrial fibrillation, CAD coronary artery disease, cIMT carotid intima media thickness, SBP \u0026amp; DBP systolic \u0026amp; diastolic blood pressure, CV cardiovascular, HF heart failure\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/2671210f04e805bc601066c0.png"},{"id":50403398,"identity":"245d73f5-868b-4938-8768-7cd07d2f2c88","added_by":"auto","created_at":"2024-01-31 04:10:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":518605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular plot visualising multiple regional plots for all the colocalised loci between Alzheimer’s disease (AD) and at least one cardiovascular trait (CV).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inner part presents the distribution of P-values (–log\u003csub\u003e10\u003c/sub\u003eP) from MTAG for the colocalised traits on the respective loci. The outer part presents the distribution of P-values (log\u003csub\u003e10\u003c/sub\u003eP) of expression quantitative trait loci (eQTL) from GTEX7 that colocalised with the respective traits. The inner part is presented with inner orientation while the outer part with outer orientation. The presented genes are the mapped genes of the AD or CV independent SNPs of the colocalized locus. Genes marked with red are shared genes between AD and CV with evidence of pleiotropic function supported by trait-eQTL colocalization analysis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/2f2edf8698b6e6e7cfb63f5c.png"},{"id":50403401,"identity":"640bc80e-779d-4435-9667-8de036e6de96","added_by":"auto","created_at":"2024-01-31 04:10:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358108,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional plot on the colocalised locus of candidate causal variant rs11786896 (mapped in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePLEC\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e) for Alzheimer’s disease (bottom), atrial fibrillation (middle) and the expression quantitative trait loci (top) for the respective tissues\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/dcb1d9f6d1737fb3738865a0.png"},{"id":50403702,"identity":"3f3fc5a9-bed6-4001-b77e-cc30860a6ee7","added_by":"auto","created_at":"2024-01-31 04:18:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":545488,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptomes of cardiac tissue for genes increasing the risk of cardiomyopathy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptomes form discrete cell-specific clusters using Uniform Manifold Approximation and Projection (UMAP). B) Expression of \u003cem\u003ePLEC \u003c/em\u003eand \u003cem\u003eC1Q \u003c/em\u003egenes\u003cem\u003e \u003c/em\u003eacross cell-specific clusters from UMAP. Red indicates higher expression. C) Enriched pathways of the \u003cem\u003ePLEC\u003c/em\u003e-containing module in cardiovascular endothelial cells between dilated cardiomyopathy cases and healthy controls. D) Enrichment of pathway genes in the protein interactomes of candidate genes in cardiovascular endothelial cells. The PLEC-interacting module is the brown and the C1q-interacting module is the pink.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/07b8a22ae46b4c66cf1d003d.png"},{"id":50403400,"identity":"76c1031f-0625-4e0b-8cb2-f54dde8ce2a3","added_by":"auto","created_at":"2024-01-31 04:10:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":862650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nuclei transcriptomes of human post-mortem brains for genes increasing the risk of Alzheimer’s disease.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA) The transcriptomes form discrete cell-specific clusters using Uniform Manifold Approximation and Projection (UMAP). B) Expression of candidate genes across cell types in human brain. C) Enriched pathways of the \u003cem\u003ePLEC\u003c/em\u003e-containing module in astrocytes between Alzheimer’s disease cases and healthy controls. D) Enrichment of pathway genes in the protein interactomes of candidate genes in astrocytes. The PLEC-interacting module is the cyan and the NDUFS3-interacting module is the darkred.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/06d4a7d5b094a7ae66d8d22b.png"},{"id":68983563,"identity":"0dc33ee4-8ee5-4062-a9d3-ebb6316576b2","added_by":"auto","created_at":"2024-11-14 08:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3906294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/7c329576-2ce3-4bc4-bdd8-b092508b30b6.pdf"},{"id":50403403,"identity":"5fb80c3c-11cd-40b3-8faa-b42e8d700292","added_by":"auto","created_at":"2024-01-31 04:10:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2315393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/fce73df5b359e1f247681592.pdf"},{"id":50403402,"identity":"86e9dfd4-9fe9-48a8-a3df-6ef9f9213199","added_by":"auto","created_at":"2024-01-31 04:10:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":631560,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3851905/v1/0234821b441db4441506a102.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease, the most common cause of dementia, is a leading health challenge for our times. More than 55\u0026nbsp;million people worldwide were estimated to be living with dementia in 2020 with 60%-70% of them being AD cases\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Alzheimer\u0026rsquo;s disease (AD) has been considered a brain-specific disease whose primary pathology is confined to the brain. However, epidemiological association and more recent causal genetic analyses have suggested mechanistic links between cardiovascular abnormalities and AD\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Several hypotheses have been proposed to explain this. AD and cardiovascular (CV) disease-related traits share common risk factors such as obesity, diabetes and inflammation, which may increase the risk of both diseases through independent (horizontal pleiotropy) or common biological pathways\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Other hypotheses include the indirect influence of impaired vascular functions that initiate or accelerate the progression of AD\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. They also may share common genetic determinants\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) have identified genetic risk loci for both AD and pathological CV traits and identified common genetic factors that may refer to the shared underlying pathways. One example of such genes is apolipoprotein E (\u003cem\u003eAPOE\u003c/em\u003e), which encodes a lipid-transport protein involved in cholesterol metabolism\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, that is the strongest genetic risk factor for AD\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and a risk factor for adverse CV traits, including coronary artery disease\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and myocardial infarction\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. A deeper understanding of the shared genetic architecture between AD and CV traits will provide insights into potentially shared and distinct aetiologies of these phenotypes. Identification of shared targets and mechanisms by which they confer functional effects can be used to discover those whose modulation could address both neurodegenerative and cardiovascular diseases.\u003c/p\u003e \u003cp\u003eHere, we further investigated the commonalities in the genetic architecture of AD and CV traits and identified pleiotropic loci affecting multiple traits aiming to define common targets for therapeutic modulation. We performed a large-scale multi-trait GWAS analysis on AD and several CV traits, followed by genetic colocalization analysis to highlight candidate pleiotropic genes and their tissue sites of action. To further characterise biological pathways involved in both diseases, we leveraged data from single-cell RNA-seq for differential gene expression with disease to explore relevant gene co-expression networks and protein-protein interactions in the brain and cardiovascular tissues. A schematic overview of the study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMulti-trait genetic association analysis identifies 44 shared loci between AD and CV traits\u003c/h2\u003e \u003cp\u003eWe performed five pairwise multi-trait analyses of GWAS (MTAG)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e on AD and coronary artery disease (CAD), atrial fibrillation (AF), stroke, carotid intima-media thickness (cIMT), and systolic and diastolic blood pressure (SBP, DBP). The analyses identified 62 unique genetic loci associated with AD at genome-wide significance (GWS) level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) across these pairwise MTAG analyses, of which 22 were novel (not within \u0026plusmn;\u0026thinsp;500 kilobases (kb) of the previously known AD loci (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Among the 62 AD genetic loci, there were 169 unique single-nucleotide polymorphisms (SNPs) (126 independent signals, linkage disequilibrium, LD\u0026thinsp;\u0026lt;\u0026thinsp;0.1) associated with AD at GWS level. Furthermore, we found 1,223 top signals associated with different CV traits at GWS level in 740 genetic loci (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Overall, 66 of the unique AD SNPs (44 loci) were additionally associated at GWS level with at least one of the examined CV traits (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eA colocalisation analysis defines genetic loci shared by AD and different CV traits\u003c/h2\u003e \u003cp\u003eUsing the Hypothesis Prioritisation in multi-trait Colocalization (HyPrColoc)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e method on 847 MTAG-reported loci (62 AD\u0026thinsp;+\u0026thinsp;785 CV), we identified 26 loci which colocalised between AD and CV traits with a posterior probability (PP)\u0026thinsp;\u0026gt;\u0026thinsp;0.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). Most colocalised loci were found either between AD and AF (10 loci) or between AD and DBP (7 loci). The most substantial evidence for colocalisation was observed for a locus at chr8:124,608,614\u0026thinsp;\u0026plusmn;\u0026thinsp;200kb (\u003cem\u003eRN7SKP155\u003c/em\u003e) associated with AD and cIMT (PP\u0026thinsp;=\u0026thinsp;1) and a locus at chr11:47,391,948\u0026thinsp;\u0026plusmn;\u0026thinsp;200kb (\u003cem\u003eSPI1\u003c/em\u003e) associated with AD and DBP (PP\u0026thinsp;=\u0026thinsp;0.95). Among the 26 loci with evidence for colocalization, there were three loci for which a single candidate causal variant explained a large proportion of the association: rs11786896 (mapped in \u003cem\u003ePLEC\u003c/em\u003e; colocalised with AD-AF; PP\u0026thinsp;=\u0026thinsp;0.97; 86% of PP explained by SNP), rs7529220 (mapped in \u003cem\u003eHSPG2\u003c/em\u003e; colocalised with AD-AF; PP\u0026thinsp;=\u0026thinsp;1; 90% of PP explained by SNP) and rs429358 (\u003cem\u003eAPOE\u003c/em\u003e; colocalised with AD-CAD; PP\u0026thinsp;=\u0026thinsp;0.57; 93% of PP explained by SNP).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGene expression colocalisation analysis prioritises causal genes shared by AD and CV traits\u003c/h2\u003e \u003cp\u003eTo identify potential pleiotropic causal genes for the colocalised loci, we tested the colocalisation of AD and CV traits with the expression of nearby genes in 48 tissues using expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression (GTEx) in the 26 colocalised loci. We found that AD and at least one CV trait colocalised in 19 loci with expression of one or more genes in the same tissue, for a total of 106 associations with 61 genes (\u003cb\u003eSupplementary Fig.\u0026nbsp;1, Supplementary Table\u0026nbsp;5\u003c/b\u003e). Of these, 60 associations were found for AF (in 8 loci with 30 genes), 22 for DBP (6 loci with 16 genes), 16 for stroke (2 loci with 11 genes) and 8 for cIMT (3 loci with 6 genes).\u003c/p\u003e \u003cp\u003eIn two loci, a single candidate causal variant explained the colocalisation of AD, CV trait and tissue-specific gene expression: rs11786896 (\u003cem\u003ePLEC\u003c/em\u003e) and rs7529220 (\u003cem\u003eHSPG2\u003c/em\u003e). The intronic variant rs11786896 (\u003cem\u003ePLEC\u003c/em\u003e) explained the colocalisation of AD and AF with expression levels of \u003cem\u003ePLEC\u003c/em\u003e in the cardiac left ventricle (PP\u0026thinsp;=\u0026thinsp;0.99, %PP explained by SNP\u0026thinsp;=\u0026thinsp;99%) and skeletal muscle (PP\u0026thinsp;=\u0026thinsp;0.92, %PP explained by SNP\u0026thinsp;=\u0026thinsp;98%) and with expression levels of \u003cem\u003eDGAT1\u003c/em\u003e in oesophageal mucosa (PP\u0026thinsp;=\u0026thinsp;0.93, %PP explained by SNP\u0026thinsp;=\u0026thinsp;85%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). rs11786896 was associated with increased risk of AD (Odds Ratio, OR\u0026thinsp;=\u0026thinsp;1.02, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e), increased risk of AF (OR\u0026thinsp;=\u0026thinsp;1.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and lower expression of \u003cem\u003ePLEC\u003c/em\u003e in cardiac left ventricle (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;13\u003c/sup\u003e), as well as in skeletal muscle (Beta\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e). The same variant was associated with higher expression of \u003cem\u003eDGAT1\u003c/em\u003e in oesophageal mucosa (Beta\u0026thinsp;=\u0026thinsp;0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eThe intergenic variant rs7529220 (\u003cem\u003eHSPG2\u003c/em\u003e) explained the colocalisation of AD and AF with expression levels of \u003cem\u003eC1QA\u003c/em\u003e (PP\u0026thinsp;=\u0026thinsp;0.85, %PP\u0026thinsp;=\u0026thinsp;82%), \u003cem\u003eC1QB\u003c/em\u003e (PP\u0026thinsp;=\u0026thinsp;0.83, %PP\u0026thinsp;=\u0026thinsp;97%) and \u003cem\u003eC1QC\u003c/em\u003e (PP\u0026thinsp;=\u0026thinsp;0.61, %PP\u0026thinsp;=\u0026thinsp;99%) in breast mammary tissue. The variant was associated with higher risk of AD (OR\u0026thinsp;=\u0026thinsp;1.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), higher risk of AF (OR\u0026thinsp;=\u0026thinsp;1.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.3\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e) and increased expression of \u003cem\u003eC1QA\u003c/em\u003e (Beta\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), \u003cem\u003eC1QB\u003c/em\u003e (Beta\u0026thinsp;=\u0026thinsp;0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.6\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e), and \u003cem\u003eC1QC\u003c/em\u003e (Beta\u0026thinsp;=\u0026thinsp;0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) in mammary tissue.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePLEC\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eC1Q\u003c/b\u003e \u003cb\u003eare differentially expressed in the left ventricle with heart failure\u003c/b\u003e\u003c/p\u003e \u003cp\u003eDefining the cells in which target genes are expressed and directions of expression associated with disease risk and with disease is important for predicting the directions of effect for potential therapeutic modulation. Therefore, we tested the expression patterns of \u003cem\u003ePLEC\u003c/em\u003e and \u003cem\u003eC1Q\u003c/em\u003e genes across cell types in single-cell RNA from the heart to discover whether differences in differential expression with disease were consistent with those predicted for disease risk. \u003cem\u003ePLEC\u003c/em\u003e was expressed in all cell types found in the cardiac left ventricle, while \u003cem\u003eC1Q\u003c/em\u003e was expressed only in macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). \u003cem\u003ePLEC\u003c/em\u003e was differentially expressed with heart failure (HF) relative to healthy controls with upregulated expression in the endothelium (log2 fold change, log2FC\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) but downregulated in macrophages (log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.7\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e). There was only a trend for differential expression with HF in cardiomyocytes (log2FC\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06). All \u003cem\u003eC1Q\u003c/em\u003e associated genes were downregulated in cardiac macrophages (\u003cem\u003eC1QA\u003c/em\u003e, log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e; \u003cem\u003eC1QB\u003c/em\u003e, log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; \u003cem\u003eC1QC\u003c/em\u003e, log2FC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e).\u003c/p\u003e\u003cp\u003eWe explored differential expression with HF further by constructing high dimensional weighted gene co-expression networks (WGCN) for \u003cem\u003ePLEC\u003c/em\u003e and \u003cem\u003eC1Q\u003c/em\u003e to identify modules of highly correlated genes across cell types. We generated 12 gene co-expression modules in cardiac endothelial cells, 14 in cardiomyocytes and 6 in macrophages (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). A differential module eigengene analysis indicated that the module including \u003cem\u003ePLEC\u003c/em\u003e was upregulated in cardiac vascular endothelial cells and cardiomyocytes in HF cases relative to the healthy controls and downregulated in macrophages with dilated cardiomyopathy (dCM) cases relative to healthy controls (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). The \u003cem\u003eC1Q\u003c/em\u003e-containing module also was downregulated in cardiomyocytes in HF cases relative to healthy controls and downregulated in macrophages in dCM cases relative to healthy controls. Gene-set enrichments for biological processes defined the pathways most highly enriched in \u003cem\u003ePLEC\u003c/em\u003e-containing modules (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e), which included the \u0026ldquo;vascular endothelial growth factor receptor-2 signalling\u0026rdquo; and \u0026ldquo;endothelium development\u0026rdquo; pathways in endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and many pathways related to mitochondrial oxidative metabolism in cardiomyocytes (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). In macrophages, the module including \u003cem\u003eC1Q\u003c/em\u003e genes was enriched for \u0026ldquo;complement activation\u0026rdquo; and \u0026ldquo;synapse pruning\u0026rdquo; pathways, among others (\u003cb\u003eSupplementary Fig.\u0026nbsp;3, Supplementary Table\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePLEC and C1Q interactomes are enriched in cardiomyocytes, cardiac vascular endothelial cells and macrophages\u003c/h2\u003e \u003cp\u003eTo gain insights into the potential functional roles of proteins, we performed cell-specific protein-protein interaction (PPI) analyses on the set of colocalised candidate genes by constructing their protein interactomes across cell types of human cardiovascular tissue (\u003cb\u003eSupplementary Tables\u0026nbsp;10 and 11\u003c/b\u003e). The PLEC interactome was upregulated in a pathway related to \u0026ldquo;ribosomal small subunit assembly\u0026rdquo; in endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) and upregulated in a \u0026ldquo;SRP-dependent co-translational protein targeting to membrane\u0026rdquo; pathway in cardiomyocytes (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). Additionally, an interactome containing both PLEC and NDUFS3 was enriched with HF and was upregulated in the pathways related to \u0026ldquo;aerobic electron transport chain\u0026rdquo; in endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) and \u0026ldquo;acetyl-CoA biosynthetic process from pyruvate\u0026rdquo; and \u0026ldquo;energy coupled proton transport\u0026rdquo; in cardiomyocytes (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). In macrophages, the PLEC-NDUFS3 interactome in HF was enriched for \u0026ldquo;mitochondria electron transport of cytochrome c to oxygen\u0026rdquo; while C1Q interactome was enriched for \u0026ldquo;cell junction disassembly\u0026rdquo; (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePLEC\u003c/b\u003e \u003cb\u003eis differentially expressed in brain astrocytes and is upregulated in AD\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe also tested the expression of \u003cem\u003ePLEC\u003c/em\u003e and \u003cem\u003eC1Q\u003c/em\u003e across different cell types in post-mortem human brain samples. \u003cem\u003ePLEC\u003c/em\u003e was highly expressed in astrocytes and (to a lesser degree) in neurons. \u003cem\u003eC1Q\u003c/em\u003e genes were expressed primarily in microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). \u003cem\u003ePLEC\u003c/em\u003e was significantly upregulated in astrocytes from AD donors relative to non-diseased control donors (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;1.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). \u003cem\u003eC1Q\u003c/em\u003e genes were not significantly differentially expressed in microglia but consistently showed lower mean expression with AD (\u003cem\u003eC1QA\u003c/em\u003e: log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.23; \u003cem\u003eC1QB\u003c/em\u003e: log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.3; \u003cem\u003eC1QC\u003c/em\u003e: log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9).\u003c/p\u003e\u003cp\u003eWe explored the cell-specific differential expression of these and co-expressed genes further with WGCNA, which identified 14 gene co-expression modules in astrocytes and 8 in microglia (\u003cb\u003eSupplementary Table\u0026nbsp;12\u003c/b\u003e). Differential module eigengene analyses showed that the \u003cem\u003ePLEC\u003c/em\u003e-containing module was upregulated in astrocytes and the \u003cem\u003eC1Q\u003c/em\u003e-containing module was downregulated in microglia (\u003cb\u003eSupplementary Table\u0026nbsp;13\u003c/b\u003e). Gene-set enrichment for biological processes identified 25 associated pathways for the \u003cem\u003ePLEC\u003c/em\u003e-containing module in astrocytes including \u0026ldquo;neuron projection morphogenesis\u0026rdquo; and \u0026ldquo;extracellular structure organisation\u0026rdquo; and 25 associated pathways for the \u003cem\u003eC1Q\u003c/em\u003e gene module in microglia including the \u0026ldquo;positive regulation of intrinsic apoptotic signalling\u0026rdquo; (\u003cb\u003eSupplementary Fig.\u0026nbsp;6, Supplementary Table\u0026nbsp;14\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe further explored the discordant directions in \u003cem\u003eC1Q\u003c/em\u003e expression between eQTLs and post-mortem brain single nuclei data in relation to the beta-amyloid pathology load in microglia. For subjects with lower beta-amyloid load in the brain, we observed an increasing expression of \u003cem\u003eC1Q\u003c/em\u003e in concordance with eQTL but subjects with higher beta-amyloid load showed decreased expression (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePLEC, NDUFS3 and C1Q interactomes are enriched in astrocytes and microglia\u003c/h2\u003e \u003cp\u003eA PPI analysis on candidate gene networks highlighted that the PLEC-NDUFS3 interactome also was enriched in both astrocytes and microglia in AD cases compared to controls (\u003cb\u003eSupplementary Tables\u0026nbsp;10, 15 and 16\u003c/b\u003e). Associated functional pathways were enriched for the \u0026ldquo;aerobic electron transport chain\u0026rdquo; and \u0026ldquo;SRP\u0026thinsp;\u0026minus;\u0026thinsp;dependent co-translational protein targeting to membrane\u0026rdquo; pathways in the astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In microglia, the C1Q interactome was enriched in \u0026ldquo;SRP\u0026thinsp;\u0026minus;\u0026thinsp;dependent co-translational protein targeting to membrane\u0026rdquo; pathway (\u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe adopted a novel approach to understanding the co-occurrence of AD and CV disease and traits based on several multi-trait GWAS to characterise the shared genetic architecture of AD with CV traits. Convergent evidence from colocalisation between AD, CV traits and eQTLs prioritised two genetic regions that each included a single candidate causal variant (rs11786896 expressed via \u003cem\u003ePLEC\u003c/em\u003e and rs7529220 expressed via \u003cem\u003eC1QA\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, and \u003cem\u003eC1QC\u003c/em\u003e) shared between AD and AF. Single-cell RNA-sequence data, co-expression network and protein-protein interaction analyses together were consistent in showing that \u003cem\u003ePLEC\u003c/em\u003e is upregulated in left ventricular endothelium and cardiomyocytes with HF and in brain astrocytes with AD. By contrast, while \u003cem\u003eC1Q\u003c/em\u003e genes are predicted to be upregulated with greater disease risk in cardiac macrophages for HF and in brain microglia for AD, we found opposite directions of difference with disease for both. We explored differences in the direction of changes from early disease (low beta-amyloid pathology load) to late (high beta-amyloid pathology load) for microglia and found the congruence with directions predicted for disease risk in early disease that was lost with in later disease progression. Our findings provide new insights into genetic pleiotropic effects and potential shared mechanisms causally related to both AD and CVD.\u003c/p\u003e \u003cp\u003eOut of the several CV traits and diseases examined with AD, AF showed the largest number of pleiotropic signals with AD. Numerous observational studies, provide growing evidence that AF is associated with cognitive impairment, risk of AD and other dementias\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. However, it has been unclear whether the diseases have a shared pathophysiology or whether the relationship arises as downstream consequences of AD (e.g., stroke). Here we provide evidence defining common genetic determinants for the two diseases. The colocalised intronic variant rs11786896 within the plectin gene (\u003cem\u003ePLEC\u003c/em\u003e) was associated with lower expression of \u003cem\u003ePLEC\u003c/em\u003e in the cardiac left ventricle (and skeletal muscle) and increased the risk of both AD and AF. \u003cem\u003ePLEC\u003c/em\u003e is a member of a protein family, named plakins, with a crucial structural role in the cytoskeleton including cell architecture and tissue integrity and a partially functional role in the assembly, positioning, and regulation of signalling complexes\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Plectin is expressed as various tissue-dependent protein isoforms in several tissues with each tissue to be characterised by different proportion and composition of plectin isoforms and each distinct isoform to be characterised by specific functions\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, the precise role of plectin in macrophages is unknown and thus, further study is needed to determine its specific functions and interactions in macrophages.\u003c/p\u003e \u003cp\u003ePrevious studies of human tissues or preclinical models provide independent evidence for an association of plectin with diseases including AD and AF\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our new data and analyses provide evidence that risk in AD is affected via functions of plectin in astrocytes\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Astrocytes play multiple roles, central to the pathology of AD, including metabolic support for neurons, modulation of brain microvascular function and, through activities associated with those of microglia, inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. We hypothesise that these functional roles are mediated in part by interactions of plectin with intermediate filaments (IFs), microtubules and actin filaments\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. IFs are important structural components of the cytoskeleton with crucial roles in synaptic activity, neurogenesis and repair after brain injury\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Differences in expression of plectins modulate neuronal function and vesicular trafficking generally and interactions with tau suggest potential roles specific to AD\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePLEC\u003c/em\u003e may play related roles in cardiomyocytes for assembling and mobilizing the intermediate filaments and their networks, effects that both modulate contractile function in cardiomyocytes and inflammatory responses in macrophages\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother colocalised variant between AD and AF, the intergenic rs7529220, which is located 19k upstream from Heparan Sulfate Proteoglycan 2 (\u003cem\u003eHSPG2\u003c/em\u003e) and 21k downstream from Chymotrypsin Like Elastase 3B (\u003cem\u003eCELA3B\u003c/em\u003e), was associated with increased risk of AD and AF and higher expression of three genes of the Complement Component 1, Q Subcomponent (\u003cem\u003eC1Q\u003c/em\u003e) family (\u003cem\u003eC1QA\u003c/em\u003e, \u003cem\u003eC1QB\u003c/em\u003e, \u003cem\u003eC1QC\u003c/em\u003e) in breast mammary tissue (and, by inference, in brain vasculature). The variant is located 680kb downstream of \u003cem\u003eC1Q\u003c/em\u003e genes. The complement system plays a central role in synaptic remodelling in the brain and in cellular damage response more generally in the body\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. We hypothesise that greater expression of \u003cem\u003eC1Q\u003c/em\u003e may lead to higher activity of the complement system which in turn may potentiate synapse loss in early AD\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Similarly, C1Q has roles in the genesis of atherosclerotic plaques\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and in the regulation of early stages of inflammatory responses to the cardiomyocyte injury associated with a range of cardiac traits\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study, which applied the MTAG approach in a novel way across diseases, had several strengths. First, we secured high statistical power for our study by including GWAS with substantial sample sizes ranging from 185,000 to 1,000,000 participants and we boosted the power even higher by performing suitable multivariate methods. Second, we combined advanced methods of genetic epidemiology and basic sciences and sought to provide supporting evidence from a variety of data. However, a number of limitations also must be acknowledged. We restricted our analyses to a population of European ancestry. The lack of genetic diversity may have hampered the possibility of detecting other relevant variants. Additionally, we did not investigate a considerable portion of the genetic predisposition coming from rare variants (MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%) as we excluded them from our analyses. However, including these variants might lead to false-positive findings and biased results. Moreover, we used statistical methods to detect pleiotropy, and therefore considered a genetic locus pleiotropic if it was statistically significantly associated with two or more phenotypes. However, this approach for identification of pleiotropic genes may not always highlight shared biological pathways, as the identified genes could affect the traits independently via different pathways (horizontal pleiotropy), or they could even be expressed in different tissues in response to different signals\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Furthermore, due to a limited number of cells for specific cell types, we had to combine single-cell data from multiple samples. We focused on tissue samples that were already enriched for cardiomyocytes, endothelium, and macrophages. Finally, the expression for some candidate genes in our data was limited and thus additional sequencing data and reads are needed to investigate them further. Additional RNA sequencing data of different AD and CV conditions would probably be even more informative.\u003c/p\u003e \u003cp\u003eIn conclusion, we performed a multi-trait analysis on AD and CV traits and a subsequent colocalisation analysis detecting 19 shared genetic loci and further prioritizing two shared causal variants between the aforementioned traits. Our findings define shared mechanisms for AD and different cardiovascular diseases. The complement system has been explored as a target for novel preventive or disease-modifying therapies in cardiovascular disease\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and AD\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our work suggests that plectin or members of its interactome could offer new and potentially promising targets for preventive and therapeutic medicines with benefits across these common comorbid disorders.\u003c/p\u003e "},{"header":"Online Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe restricted our study to a population of European ancestry. We used the summary statistics from seven GWAS on the following diseases: AD\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, AF\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, CAD\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, cIMT, stroke\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, SBP\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and DBP\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003eSupplementary Table\u0026nbsp;17\u003c/b\u003e presents the basic characteristics of all the included GWAS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenotypic quality control\u003c/h2\u003e \u003cp\u003eThe seven initial datasets contained genotyped and imputed SNPs ranging from 7 to 34\u0026nbsp;million SNPs. We included in the analysis only SNPs that were present in both datasets (AD and the examined CV trait). Furthermore, we excluded all insertions, deletions, and rare variants (minor allele frequency; MAF\u0026thinsp;\u0026lt;\u0026thinsp;0.01), variants with sample sizes less than 2/3 of the 90th percentile and palindromic SNPs. Finally, more than 5.75\u0026nbsp;million SNPs were included in the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMulti-trait association analysis\u003c/h2\u003e \u003cp\u003eWe performed five bivariate analyses on AD and a different each time CV trait (1. AF, 2. CAD, 3. cIMT, 4. Stroke, 5. SBP-DBP) using MTAG\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. We calculated the genetic correlation between the traits and further corrected our data for sample overlap using bivariate LD score regression as implemented in MTAG. Each MTAG analysis generated distinct trait-specific datasets (11 in total: 5 with AD plus 6 with CV traits) containing the trait-specific effect estimates for the included SNPs after leveraging for genetic correlation of the examined traits. As a result, the summary statistics from MTAG can be interpreted as similar to those from a univariate single-trait GWAS\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Mapping \u0026amp; Annotation\u003c/h2\u003e \u003cp\u003eWe used Functional Mapping and Annotation of GWAS (FUMA)\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e to functionally analyse all the generated summary results from MTAG. All the genome-wide significant (GWS) SNPs (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) were initially clumped (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6) to determine the coordinates of the genomic risk loci and then clumped again (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1) to define independent signals. SNPs in pairwise-LD at 0.1\u0026thinsp;\u0026le;\u0026thinsp;r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.6 or SNPs located closer than 500kb were assigned to the same LD block. SNPs that survived the second clumping were the independent signals. Independent SNPs with the smallest P-value in each LD block were defined as the top signals while the remaining were secondary signals. We further performed annotation and gene prioritization analysis including all SNPs that survived the first clumping. We used the European sample of 1000 Genome Project Phase 3\u003csup\u003e47\u003c/sup\u003e to calculate pairwise LD between SNPs. SNPs were positionally mapped to their nearest protein-coding genes (Ensembl build v92).\u003c/p\u003e \u003cp\u003eTo identify the unique AD top and secondary independent signals, we gathered the AD independent signals from all pairwise AD-CV analyses and excluded duplicate signals or proxies (either in distance\u0026thinsp;\u0026plusmn;\u0026thinsp;500 kb or in LD r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.1), keeping the strongest signal with the smallest P-value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eTrait-trait and trait-eQTL colocalisation analysis\u003c/h2\u003e \u003cp\u003eWe used HyPrColoc R package\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e to perform colocalisation analysis. HyPrColoc is a Bayesian divisive clustering algorithm for identifying shared genetic associations between traits in a genomic region using GWAS summary statistics. We performed this method to identify colocalised loci between AD and CV traits and prioritise causal variants explaining the shared association.\u003c/p\u003e \u003cp\u003eWe performed a trait-trait colocalisation analysis for each top signal indicated from MTAG in a region\u0026thinsp;\u0026plusmn;\u0026thinsp;200 kb from the top SNP. We considered variant-specific priors for our analyses, which assumes that the probability of a variant being colocalised with a set of traits decreases as the number of the set of traits increases. The variant-specific priors model requires the specification of two priors. We specified the prior probability that a variant is associated with a single trait only at P\u0026thinsp;=\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and a conditional prior probability that a variant is associated with an additional trait given that it is already associated with another trait at P\u003csub\u003ec\u003c/sub\u003e = 0.02.\u003c/p\u003e \u003cp\u003eA PP higher than 0.5 was considered adequate evidence that the examined traits colocalise in the locus. We divided the evidence of colocalisation into two categories: 1) considerable evidence (0.5\u0026thinsp;\u0026lt;\u0026thinsp;PP\u0026thinsp;\u0026lt;\u0026thinsp;0.75) and 2) strong evidence (PP\u0026thinsp;\u0026ge;\u0026thinsp;0.75). To deal with spurious pleiotropy, we restricted the analyses to regions with at least one SNP with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e in the respective univariate GWAS. Additionally, we visually inspected the colocalised loci by constructing suitable regional plots. The variants explaining at least 80% of the shared association (%PP\u0026thinsp;\u0026ge;\u0026thinsp;80%) were considered candidate causal. To limit the probability of false positive findings, we considered as causal the variants that were associated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with both AD and the respective CV trait in the respective included univariate GWAS.\u003c/p\u003e \u003cp\u003eFor the loci found to colocalise in the trait-trait colocalisation analysis, we further performed trait-expression quantitative trait loci (eQTL) colocalisation using AD, CV trait and eQTL from 48 tissues, retrieved from Genotype-Tissue Expression version 7 (GTEx v7), implementing the same parameters and approach as described in trait-trait colocalisation. The trait-eQTL colocalisation analysis was conducted to detect shared genes between the traits and investigate the tissues they are expressed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell \u0026amp; single-nuclei data acquisition\u003c/h2\u003e \u003cp\u003eWe used Gene Expression Omnibus\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e (GEO) to retrieve data for single cells of the left ventricle from 6 heart failure (HF) cases and 7 healthy controls\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, left ventricular single nuclei from 13 dilated cardiomyopathy (dCM) cases and 25 healthy controls\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and post-mortem brain single nuclei from 9 AD cases and 8 healthy controls. To deal with the small sample size in cardiac single-cell data, individuals with either coronary heart failure or dCM were considered HF cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eQuality control of single-cell \u0026amp; single-nuclei data\u003c/h2\u003e \u003cp\u003eTo quality control (QC) the data we implemented the scFlow pipeline\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Samples with less than 100 cells in cardiac data and less than 200 cells in brain data were removed. For brain data, Ambient RNA profiles were performed using EmptyDrops\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. We restricted the minimum number of expressive features to 300 for cardiac data and 100 for brain data. For cardiomyocyte-enriched samples, we set the minimum library size per cell to 1000 while keeping the default value for the rest cell types. Only genes with a minimum of 2 counts in at least 3 cells were included. Doublet cells and non-annotated genes were removed. After QC, there was adequate sample to analyse cardiomyocyte-enriched (2,989 cells from 2 HF cases and 5 controls) and endothelium-enriched single cells (2,269 cells from 4 HF cases and 4 controls), macrophage single nuclei (207,345 nuclei from 13 dHF and 25 controls), astrocyte (34060 nuclei from 25 AD cases and 24 controls), and microglia single nuclei (15292 nuclei from 25 AD cases and 24 controls).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSingle-cell \u0026amp; single-nuclei data integration, clustering and cell-type annotation\u003c/h2\u003e \u003cp\u003eCells that successfully passed the QC were integrated across samples using the linked inference of genomic experimental relationships (LIGER) method\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e defining a parameter lambda\u0026thinsp;=\u0026thinsp;5 and selecting a sample-specific optimum value for parameter K: cardiomyocyte-enriched single cells (K\u0026thinsp;=\u0026thinsp;25), endothelium-enriched single cells (K\u0026thinsp;=\u0026thinsp;30), macrophage single nuclei (K\u0026thinsp;=\u0026thinsp;40), and brain single nuclei (K\u0026thinsp;=\u0026thinsp;20). A dimensionality reduction was performed by implementing the uniform manifold approximation and projection (UMAP)\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e algorithm to generate two-dimensional embeddings of the LIGER integrated factors using the first 10 principal components (PCs) on heart single cells, the first 60 PCs on heart single nuclei and the first 30 PCs on brain single nuclei. We subsequently detected cell clusters of the UMAP embeddings implementing the Leiden community detection algorithm\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e using a sample-specific parameter k: cardiomyocyte-enriched (k\u0026thinsp;=\u0026thinsp;9) and endothelium-enriched single cells (k\u0026thinsp;=\u0026thinsp;10), heart single nuclei (k\u0026thinsp;=\u0026thinsp;45), and brain single nuclei (k\u0026thinsp;=\u0026thinsp;50). Following clustering, we used the Expression Weighted Celltype Enrichment (EWCE)\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e algorithm to perform a cell-type prediction on cell clusters using previously reported reference datasets for cardiac single cells\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, cardiac single nuclei\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e and brain single nuclei\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression analysis\u003c/h2\u003e \u003cp\u003eDifferential gene expression (DGE) analysis was performed separately for each cell-specific sample on all candidate genes detected from trait-eQTL colocalisation analysis. We investigated the expression of the genes in the RNAseq data and included only genes expressed in at least one cell type of the examined tissues. We followed a generalised linear mixed model approach as implemented in MAST\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e after excluding genes expressed in less than 10% of cells. Units for differential expression were defined as log\u003csub\u003e2\u003c/sub\u003e fold change per unit change of the respective contrast. We considered as meaningfully differentially expressed genes those with a log\u003csub\u003e2\u003c/sub\u003e fold change\u0026thinsp;\u0026ge;\u0026thinsp;0.25 and a nominal \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWeighted gene co-expression network\u003c/h2\u003e \u003cp\u003eWe further constructed cell type-specific co-expression networks on selected candidate genes using a high dimensional weighted gene co-expression network analysis (hdWGCNA)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e R package. We applied the K-Nearest Neighbours algorithm to identify groups of similar cells by means of transcriptomics (metacells) and constructed a metacell gene expression matrix. We constructed the co-expression network using the lowest soft power threshold that has a Scale Free Topology Model Fit\u0026thinsp;\u0026ge;\u0026thinsp;0.8. Genes that were not grouped into any co-expression module were excluded (\u0026ldquo;grey\u0026rdquo; module). We also excluded modules with less than 20 genes. We obtained the module eigengene values, which describe the expression patterns of entire co-expression modules, and performed a differential module eigengenes analysis applying a Mann-Whitney U test. To reduce false-positive findings due to multiple testing inflation, we implemented the Benjamini-Hochberg false discovery rate (FDR) method\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. We also conducted a pathway enrichment analysis using Enrichr v.3.0 R package\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e and analysed only gene-sets with at least 20 genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction analysis\u003c/h2\u003e \u003cp\u003eWe used the STRINGdb\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e R package to analyse the full protein-protein interaction network data from STRING v11 database\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We expanded the candidate set of genes from the trait-eQTL colocalisation analysis by incorporating genes with protein-protein interactions (experimental evidence\u0026thinsp;\u0026ge;\u0026thinsp;700) with the candidates. Using the previously constructed cell type-specific modules from hdWGCNA, we performed an enrichment analysis per module on candidate genes between the module and reference set using Fisher\u0026rsquo;s exact tests. The Benjamini-Hochberg FDR approach was used to correct for type I error inflation due to the multiple testing error.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eAll authors declare no comparing interests\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eA.D., D.W, P.M., and I.T. designed the research. F.K., N.F. and P.F.T. conducted the analyses and visualised the results. F.K., N.F., P.F.T., E.E., A.D., D.W., P.M., and I.T. interpreted the results. F.K. wrote the manuscript. N.F., P.F.T., D.M., P.E., A.D., D.W., P.M., and I.T. critically revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe Genotype-Tissue Expression version 7 (GTEx v7) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript were obtained from dbGaP accession number phs000424.v7.p2 on 30/03/2022. DW is supported by the Academy of Medical Sciences Professorship (APR7_1002). PMM and PE acknowledge personal support from the UK Dementia Research Institute, which is funding by the UKRI Medical Research Council, Alzheimer\u0026rsquo;s Society and Alzheimer\u0026rsquo;s Research UK, Edmond J. Safra Foundation and Lily Safra, an NIHR Senior Investigator Award, and the Imperial College Healthcare Trust (ICHT) NIHR Biomedical Research Centre. IT, AD, PE and PMM acknowledge additional generous support from the Trustees of the Sir Michael Uren Foundation for this work.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe summary statistics from the GWAS included in this study are publicly available and can be retrieved from GWAS Catalog under the accession codes \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST007320\u003c/span\u003e (AD), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST006414\u003c/span\u003e (AF), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST003116\u003c/span\u003e (CAD), #### (cIMT) (will be provided upon acceptance), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST006906\u003c/span\u003e (stroke), \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST006624\u003c/span\u003e (SBP) and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGCST006630\u003c/span\u003e (DBP). Heart single-cell data from the left ventricle tissue was downloaded from Gene Expression Omnibus (GEO) under accession codes \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGSE109816\u003c/span\u003e (cardiomyocytes-enriched samples) and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGSE121893\u003c/span\u003e (normal digested samples). Single-nuclei data for the left ventricular tissue was retrieved from GEO under accession code \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGSE109816\u003c/span\u003e. Single-nuclei data for human post-mortem brain samples from AD and Control samples were downloaded from GEO XXXX (will be provided upon acceptance). The MTAG summary statistics generated in this study have been deposited in NHGRI-EBI GWAS Catalog under accession codes #### (will be provided upon acceptance). All other data generated in this study are provided with this published article (and its supplementary information files).\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eNo previously unreported custom computer code or mathematical algorithm was used to generate results central to the conclusions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRizzi L, Rosset I, Roriz-Cruz M (2014) Global epidemiology of dementia: Alzheimer's and vascular types. \u003cem\u003eBiomed Res Int\u003c/em\u003e 908915, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2014/908915\u003c/span\u003e\u003cspan address=\"10.1155/2014/908915\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGauthier S, Servaes WC, Morais S, Rosa-Neto JA (2022) P. 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Nucleic Acids Res 47:D607\u0026ndash;D613. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/nar/gky1131\u003c/span\u003e\u003cspan address=\"10.1093/nar/gky1131\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3851905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3851905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSeveral cardiovascular (CV) traits and diseases co-occur with Alzheimer\u0026rsquo;s disease (AD). We mapped their shared genetic architecture using multi-trait genome-wide association studies. Subsequent fine-mapping and colocalisation highlighted 19 genetic loci associated with both AD and CV diseases. We prioritised rs11786896, which colocalised with AD, atrial fibrillation (AF) and expression of \u003cem\u003ePLEC\u003c/em\u003e in the heart left ventricle, and rs7529220, which colocalised with AD, AF and expression of \u003cem\u003eC1Q\u003c/em\u003e family genes. Single-cell RNA-sequencing data, co-expression network and protein-protein interaction analyses provided evidence for different mechanisms of \u003cem\u003ePLEC\u003c/em\u003e, which is upregulated in left ventricular endothelium and cardiomyocytes with heart failure (HF) and in brain astrocytes with AD. Similar common mechanisms are implicated for \u003cem\u003eC1Q\u003c/em\u003e in heart macrophages with HF and in brain microglia with AD. These findings highlight inflammatory and pleomorphic risk determinants for the co-occurrence of AD and CV diseases and suggest PLEC, C1Q and their interacting proteins as novel therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-31 04:10:48","doi":"10.21203/rs.3.rs-3851905/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4bd2475b-e694-4035-8703-0344074f2e60","owner":[],"postedDate":"January 31st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":28465337,"name":"Health sciences/Diseases/Neurological disorders/Dementia/Alzheimer's disease"},{"id":28465338,"name":"Health sciences/Medical research/Epidemiology"},{"id":28465339,"name":"Health sciences/Medical research/Genetics research"},{"id":28465340,"name":"Health sciences/Diseases/Cardiovascular diseases"}],"tags":[],"updatedAt":"2024-11-14T08:07:16+00:00","versionOfRecord":{"articleIdentity":"rs-3851905","link":"https://doi.org/10.1038/s41467-024-53452-6","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-11-13 05:00:00","publishedOnDateReadable":"November 13th, 2024"},"versionCreatedAt":"2024-01-31 04:10:48","video":"","vorDoi":"10.1038/s41467-024-53452-6","vorDoiUrl":"https://doi.org/10.1038/s41467-024-53452-6","workflowStages":[]},"version":"v1","identity":"rs-3851905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3851905","identity":"rs-3851905","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}